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Energies, Volume 15, Issue 3 (February-1 2022) – 579 articles

Cover Story (view full-size image): Achieving high capacities and milder conditions for hydrogen storage requires the design of improved and innovative materials and systems. The novel hybrid storage method provides a smart trade-off solution between high-pressure storage technology and compact solid-state hydrogen storage in hydride compounds for clean mobility. View this paper
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20 pages, 37219 KiB  
Article
Experimental Analysis of the R744/R404A Cascade Refrigeration System with Internal Heat Exchanger. Part 2: Exergy Characteristics
by Min-Ju Jeon
Energies 2022, 15(3), 1251; https://doi.org/10.3390/en15031251 - 8 Feb 2022
Cited by 4 | Viewed by 2474
Abstract
This paper examines the exergy efficiency and exergy destruction rate of the R744/R404A cascade refrigeration system (CRS) using an internal heat exchanger in supermarkets according to various conditions affecting the system. A refrigerant of a low-temperature cycle uses R744 and a refrigerant of [...] Read more.
This paper examines the exergy efficiency and exergy destruction rate of the R744/R404A cascade refrigeration system (CRS) using an internal heat exchanger in supermarkets according to various conditions affecting the system. A refrigerant of a low-temperature cycle uses R744 and a refrigerant of a high-temperature cycle in the CRS uses R404A. Experiments were conducted by changing various conditions on the high- and low-temperature side, and exergy analysis was performed accordingly. The main results are summarized as follows: (1) the lower the total exergy destruction rate of the CRS, the higher the exergy efficiency of the system, and accordingly the coefficient of performance (COP) of the system is also improved. (2) In the CRS, since the optimum cascade evaporation temperature exists (about −16 °C), it can be said that the limit point, that is, the cascade evaporation temperature with the maximum COP of the system, is the optimum point at about −16 °C. Therefore, at this optimum point, the exergy destruction rate of the cascade heat exchanger becomes the minimum. In other words, it should be noted that when the cascade evaporation temperature is the optimum point, the exergy destruction rate of the R744 compressor and the cascade heat exchanger is minimal. The purpose of this study is to provide basic design data by analyzing the exergy characteristics according to various conditions on the high- and low-temperature side for optimal design of a CRS to which R744 is applied. Full article
(This article belongs to the Topic Exergy Analysis and Its Applications)
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<p>Photograph of the experimental apparatus for the CRS using R744.</p>
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<p>Schematic diagram of experimental apparatus for the CRS using R744. [<a href="#B13-energies-15-01251" class="html-bibr">13</a>].</p>
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<p>The conceptual diagram of the R744/R404A CRS.</p>
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<p>Exergy destruction rate, exergy efficiency and COP of the CRS with respect to subcooling degree of R404A cycle.</p>
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<p>Exergy destruction rate, exergy efficiency and COP of the CRS with respect to superheating degree of R404A cycle.</p>
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<p>Exergy destruction rate, exergy efficiency and COP of the CRS with respect to superheating degree of R744 cycle.</p>
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<p>Exergy destruction rate, exergy efficiency and COP of the CRS with respect to condensation temperature of the CRS.</p>
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<p>Exergy destruction rate, exergy efficiency and COP of the CRS with respect to the evaporation temperature of the CRS.</p>
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<p>Exergy destruction rate, exergy efficiency and COP of the CRS with respect to the evaporation temperature of the cascade heat exchanger.</p>
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<p>Exergy destruction rate, exergy efficiency and COP of the CRS with respect to the IHE efficiency of the R404A cycle.</p>
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<p>Exergy destruction rate, exergy efficiency and COP of the CRS with respect to the IHE efficiency of the R744 cycle.</p>
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<p>Performance analysis results with respect to superheating degree in the R404A cycle of the CRS.</p>
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13 pages, 4206 KiB  
Article
How to Reduce the Design of Disc-Shaped Heat Exchangers to a Zero-Degrees-of-Freedom Task
by Enrico Sciubba
Energies 2022, 15(3), 1250; https://doi.org/10.3390/en15031250 - 8 Feb 2022
Viewed by 1827
Abstract
The continuous quest for improving the performance of heat exchangers, together with ever more stringent volume and weight constraints, especially in enclosed applications like internal combustion engines and electronic devices, has stimulated the search for compact, high-performance units. One of the shapes that [...] Read more.
The continuous quest for improving the performance of heat exchangers, together with ever more stringent volume and weight constraints, especially in enclosed applications like internal combustion engines and electronic devices, has stimulated the search for compact, high-performance units. One of the shapes that has emerged from a vast body of research is the disc-shaped heat exchanger, in which the fluid to be heated/cooled flows through radial—often bifurcated—channels carved inside a metallic disc. The disc in turn exchanges thermal energy with the hot/cold source (the environment or another body). Several studies have been devoted to the identification of an “optimal shape” of the channels: most of them are based on the extremization of some global property of the device, like its monetary or resource cost, its efficiency, the outlet temperature of one of the fluids, the total irreversibility of the process, etc. The present paper demonstrates that-for all engineering purposes there is only one correct design procedure for such a heat exchanger, and that if a few basic rules of engineering common sense are adopted, this procedure depends solely on the technical specifications (type of operation, thermal load, materials, surface quality): the design in fact reduces to a zero-degree of freedom problem. The procedure is described in detail, and it is shown that a proper application of the constraints completely identifies the shape, size and similarity indices of both the disc and the internal channels. The goal of this study is to demonstrate that-in this, as in many similar cases-a straightforward application of prime principles and of diligent engineering rules, may generate “optimal” designs: these principles guarantee a sort of “embedded optimality”. Full article
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<p>Sketch of a disc-shaped heat exchanger (<b>a</b>): highly-branched; (<b>b</b>): assembly; (<b>c</b>) single-branched configuration.</p>
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<p>“Typical” CFD of a DSHE. Reprinted with permission from Ref. [<a href="#B12-energies-15-01250" class="html-bibr">12</a>]. Copyright 2017 Elsevier.</p>
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<p>Fundamental modes of operation of a DSHE: (<b>a</b>) Convection both sides; (<b>b</b>) Conduction both sides; (<b>c</b>) Conduction and convection.</p>
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<p>DSHE nomenclature (here, <span class="html-italic">z</span><sub>0</sub> = 3, <span class="html-italic">z<sub>b</sub></span> = <span class="html-italic">n</span> = 1, <span class="html-italic">γ</span><sub>0</sub> = 60°).</p>
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<p>Nusselt number vs. Graetz number.</p>
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<p>Identification of the radii of successive branchings (here, <span class="html-italic">z</span><sub>0</sub> = 3, <span class="html-italic">z<sub>b</sub></span> = 3).</p>
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<p>The DSHE-C1 described in Refs. [<a href="#B10-energies-15-01250" class="html-bibr">10</a>,<a href="#B12-energies-15-01250" class="html-bibr">12</a>].</p>
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<p>The improved DSHE-C1 (above) and two 3–D renderings.</p>
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<p>Ultra-micro DSHE: Results of a RANS CFD simulation (Adapted from Ref. [<a href="#B10-energies-15-01250" class="html-bibr">10</a>]).</p>
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<p>The improved ultra-micro DSHE-C2.</p>
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<p>Two 3–D renderings of the improved DSHE-C2.</p>
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18 pages, 7971 KiB  
Article
Neutronic Analysis of Start-Up Tests at China Experimental Fast Reactor
by Jiwon Choe, Chirayu Batra, Vladimir Kriventsev and Deokjung Lee
Energies 2022, 15(3), 1249; https://doi.org/10.3390/en15031249 - 8 Feb 2022
Cited by 3 | Viewed by 2453
Abstract
The China Experimental Fast Reactor (CEFR) is a small, sodium-cooled fast reactor with 20 MW(e) of power. Start-up tests of the CEFR were performed from 2010 to 2011. The China Institute of Atomic Energy made some of the neutronics start-up-test data available to [...] Read more.
The China Experimental Fast Reactor (CEFR) is a small, sodium-cooled fast reactor with 20 MW(e) of power. Start-up tests of the CEFR were performed from 2010 to 2011. The China Institute of Atomic Energy made some of the neutronics start-up-test data available to the International Atomic Energy Agency (IAEA) as part of an international neutronics benchmarking exercise by distributing the experimental data to interested organizations from the member states of the IAEA. This benchmarking aims to validate and verify the physical models and neutronics simulation codes with the help of the recorded experimental data. The six start-up tests include evaluating criticality, control-rod worth, reactivity effects, and neutron spectral characteristics. As part of this coordinated research, the IAEA performed neutronics calculations using the Monte Carlo codes Serpent 2 and OpenMC, which can minimize modeling assumptions and produce reference solutions for code verification. Both codes model a three-dimensional heterogeneous core with an ENDF/B-VII.1 cross-section library. This study presents the calculation results with a well-estimated criticality and a reasonably good estimation of reactivities. The description and analysis of the core modeling assumptions, challenges in modeling a dense SFR core, results of the first phase of this project, and comparative analysis with measurements are presented. Full article
(This article belongs to the Topic Nuclear Energy Systems)
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<p>Cross-sectional views of 3D CEFR core of Serpent 2 (<b>left</b>) and OpenMC (<b>right</b>). XY-plane at the center view shows 79 fuel subassemblies surrounded by SS reflectors and B<sub>4</sub>C shielding. The boundary condition is given as void; thus, Serpent represents void boundary condition in black color. YZ and ZX planes at the center show axial configuration. All subassemblies have SS reflector at the bottom region.</p>
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<p>Core-loading pattern with fuel subassemblies and mock-up fuel subassemblies. (<b>Left</b>: 71 fuel subassemblies and 8 mock-up fuel subassemblies; <b>Right</b>: 72 fuel subassemblies and 7 mock-up fuel subassemblies). Bright green colored positions are for fuel subassemblies. Colored positions with number I to IV are for fuel SAs. AZ, KC and PC are positions for control rods. IN is position for neutron source SA. C2 is for SS SA.</p>
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<p>Results of criticality.</p>
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<p>Comparisons of Control-Rod Worths. i.e., RE2 means regulating rod No. 2, and 2 × RE means two regulating rods; RE1 and RE2.</p>
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<p>Normalized assembly power distribution of Serpent 2 at operation loading with control-rod positions of “2 × RE + 3 × SH + 3 × SA Before” (<span class="html-italic">k<sub>eff</sub></span> = 1.00192 ± 0.00006).</p>
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<p>Normalized assembly power distribution of OpenMC at operation loading with control-rod positions of “2 × RE + 3 × SH + 3 × SA Before” (<span class="html-italic">k<sub>eff</sub></span> = 1.00181 ± 0.00004).</p>
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<p>Difference in assembly power at operation loading with control-rod positions of “2 × RE + 3 × SH + 3 × SA Before”.</p>
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<p>Flux distribution comparison at operation loading with control-rod positions of “2 × RE + 3 × SH + 3 × SA Before”.</p>
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<p>S-curves of regulating rods (<b>RE1</b>, <b>RE2</b>) and shim rods (<b>SH1</b>, <b>SH2</b>, <b>SH3</b>). The standard of the bottom is the bottom of the fuel region.</p>
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<p>Positions and Void FA Loading for Void Reactivity Measurement.</p>
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<p>Sodium-void worth.</p>
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<p>Sodium density as function of temperature.</p>
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<p>Reactivity change as function of temperature: increasing process and decreasing process.</p>
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<p>Results of temperature coefficients.</p>
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<p>Positions and subassembly loading for swap-reactivity measurement. (Red color: fuel subassemblies; Purple color: type-I SS subassemblies).</p>
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<p>Comparison of swap reactivity results: test case of multiple rods.</p>
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<p>Comparison of swap reactivity results: test case of single rod.</p>
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16 pages, 4532 KiB  
Article
Evaluation of the Melting Gasification Process for Recovery of Energy and Resources from Automobile Shredder Residues
by Heung-Min Yoo, Sang-Yeop Lee, Sung-Jin Cho, Yong-Chil Seo and Ha-Na Jang
Energies 2022, 15(3), 1248; https://doi.org/10.3390/en15031248 - 8 Feb 2022
Cited by 3 | Viewed by 2219
Abstract
In this study, we investigated the applicability of an automobile shredder residue (ASR) as an energy and recycling resource. First, ASR gasification was conducted in a fixed-bed reactor (throughput = 1 kg/h) at different temperatures (800, 1000, and 1200 °C) and an equivalence [...] Read more.
In this study, we investigated the applicability of an automobile shredder residue (ASR) as an energy and recycling resource. First, ASR gasification was conducted in a fixed-bed reactor (throughput = 1 kg/h) at different temperatures (800, 1000, and 1200 °C) and an equivalence ratio of 0.1–0.5. Clay bricks were prepared with the solid residue obtained from the gasification process to address the issue of solid-residue production in pyrolysis. The syngas (H2 + CO) from ASR gasification had maximum and minimum yields of approximately 86 and 40 vol.%, respectively. Furthermore, the yield of syngas increased with the temperature and equivalence ratio (ER); therefore, the optimum conditions for the ASR gasification were determined to be a temperature of 1200 °C and an ER of 0.5. In addition, solid residues, such as char and ash, began to melt due to thermal heating in the range of 1300–1400 °C and were converted into melting slag, which was subsequently used to manufacture clay bricks. The absorption ratios and compressive strengths of the clay bricks were compared to those set by Korean Industrial Standards to evaluate the quality of the clay bricks. As a result, the manufactured clay bricks were estimated to have a compressive strength of over 22.54 N/mm2 and an absorption ratio of less than 10%. Additionally, greenhouse gas (GHG) emissions from the melting–gasification process were estimated and compared with those from ASR incineration, calculated using the greenhouse gas equations provided by the Korean Ministry of Environment. As a result, it was revealed that the GHG emissions from the combined melting–gasification process (gasification, melting system, and clay-brick manufacturing) were approximately ten times higher than those from the ASR-incineration process. Thus, in terms of operation cost on the carbon capture process for GHG reduction, the combined melting–gasification process would be a more efficient process than that of incineration. Full article
(This article belongs to the Topic Solid Waste Management)
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Graphical abstract

Graphical abstract
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<p>Process Diagram of Fixed-bed Gasification System. 1. Oxygen supply; 2. Oxygen controller; 3. Mass flow controller; 4. Feeder; 5. Feeding pipe; 6. Reactor with heater; 7. Cyclone; 8. Residue collector; 9. Scrubber‘ 10. Fabric filter; 11. Water pump; 12. Filtering system; 13. Silica gel; 14. Gas vacuum pump; 15. Syngas controller; and 16. Micro-GC, TC Thermocouples.</p>
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<p>Process of Present Study for Efficient ASR Disposal.</p>
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<p>Concept for GHG Emission Estimate.</p>
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<p>TGA Results of Fuels.</p>
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<p>Gas Composition with Different Temperatures and ER from ASR Gasification.</p>
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<p>Assessment of ASR Gasification Efficiency. (<b>a</b>) Yields of Solid Residue and Dry Gas with Different Temperatures and ER, (<b>b</b>) Results of Carbon Conversion and Cold gas Efficiency with Different Temperatures and ER.</p>
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<p>Compressive Strength Results of Clay Bricks for Different Conditions.</p>
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<p>Pictures of Melting Slag by Scanning Electron Microscopy (the top: 1000<math display="inline"><semantics> <mo>×</mo> </semantics></math>/the bottom: 300<math display="inline"><semantics> <mo>×</mo> </semantics></math>).</p>
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14 pages, 3679 KiB  
Article
Effect of Ground Electrodes on the Susceptibility to Damage of Customer Premises Equipment (CPE) under Impulse Conditions
by Usman Muhammad, Normiza Mohamad Nor, Annuar Mohd Ramli and Nurul Nadia Ahmad
Energies 2022, 15(3), 1247; https://doi.org/10.3390/en15031247 - 8 Feb 2022
Cited by 2 | Viewed by 1747
Abstract
One of the requirements for the safe operation of customer premises equipment (CPE) is an adequate grounding system as a means to divert high fault currents to the ground. In this work we report on the results of an experimental study of the [...] Read more.
One of the requirements for the safe operation of customer premises equipment (CPE) is an adequate grounding system as a means to divert high fault currents to the ground. In this work we report on the results of an experimental study of the impulse characteristics at a charging voltage of 30 kV on the surge protective device connected to 16 earth electrodes and installed at two sites, giving various ground resistance at low voltages, RDC values. All of these grounding electrodes were installed and tested under the same charging voltage to determine the effectiveness of ground electrodes toward the damage of a modem at the premises. We observed that modems did not experience damage when the ground electrode of the distribution pole (DP) had an RDC below 30 Ω in general and below 46 Ω when ground electrodes installed in low resistivity soil were used. The impulse polarity did not affect the damage susceptibility of the CPE. Full article
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<p>Test arrangement used in the study.</p>
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<p>Voltage and current traces, measured with voltage divider 1 and current transformer 1, respectively, for configuration (f) installed at site 1.</p>
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<p>Measured traces on the modem for the case of no damage to the modem for configuration (h) installed at site 2b (<b>a</b>) Voltage trace measured with voltage probe 2 (x scale is 10 μs/div, and y scale is 5 kV/div); (<b>b</b>) Current flows through the modem, measured with current transformer (CT) 2 (x scale is 10 μs/div, and y scale is 5 kV/div, and the voltage magnitude is multiplied by 10, since the CT used had a sensitivity of 0.1 V/A).</p>
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<p>Measured traces on the modem for the case of damage to the modem for configuration (d), installed at site 2b (<b>a</b>) Voltage trace measured with voltage divider 2 (x scale is 10 μs/div, and y scale is 5 kV/div); (<b>b</b>) Current flows through the modem, measured with current transformer (CT) 2 (x scale is 10 μs/div, and y scale is 5 kV/div, and the voltage magnitude is multiplied by 10, since the CT used had a sensitivity of 0.1 V/A).</p>
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<p>Current flows through the power adapter, measured with current transformer (CT) 3 (<b>a</b>) Configuration (b) installed at site 1, for the case of no damage to the modem; (<b>b</b>) Configuration (a) installed at site 2, for the case of damage to the modem.</p>
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20 pages, 4339 KiB  
Article
Energy Recovery from Waste—Closing the Municipal Loop
by Emilia den Boer, Kamil Banaszkiewicz, Jan den Boer and Iwona Pasiecznik
Energies 2022, 15(3), 1246; https://doi.org/10.3390/en15031246 - 8 Feb 2022
Cited by 5 | Viewed by 3246
Abstract
Municipal waste management in the EU has been challenged to a thorough transformation towards a Circular Economy. It is addressed by a number of quantitative policy targets, including a restriction on municipal waste landfilling to 10% in 2035. This paper presents the data [...] Read more.
Municipal waste management in the EU has been challenged to a thorough transformation towards a Circular Economy. It is addressed by a number of quantitative policy targets, including a restriction on municipal waste landfilling to 10% in 2035. This paper presents the data on municipal waste composition in a large Polish city, based on thorough waste sorting analyses. On average, 374 kg of municipal waste is collected per capita in Wroclaw, of which 41% are separately collected fractions. The approach to implement the EU recycling targets until 2035 is presented, including an increase of sorting and recycling efficiency and a significant share of recyclables being retrieved from the residual waste fraction. Notwithstanding the recycling targets, an important stream of residual waste remains, amounting to 200 k ton in 2020 and approx. 130 k ton in 2035, which is available for energy recovery. The respective LHV values range from 8.5 to 7.6 MJ/kg. The results indicate that the residual waste stream, after satisfying the recycling targets, is still suitable for energy recovery through the whole period until 2035. Moreover, it is a necessary step towards closing the materials cycling in the municipal sector and the only option so far to reduce landfilling sufficiently. Full article
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<p>Sampling schedule according to modified SWA Tool methodology.</p>
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<p>Results of municipal waste collection in Wrocław in the period 2013–2020.</p>
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<p>Composition of separately collected light recyclables—main categories.</p>
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<p>Detailed composition of &gt;40 mm fractions of separately collected light recyclables.</p>
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<p>Composition of separately collected paper and cardboard—main categories.</p>
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<p>Composition of separately collected glass.</p>
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<p>Composition of separately collected biowaste.</p>
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<p>Residual waste composition by major fractions in Wrocław.</p>
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<p>Composition of the &gt;80 mm fraction of residual waste in Wrocław by detailed categories.</p>
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<p>Assumed shares of materials diverted for recycling from different waste fractions.</p>
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<p>Contribution of materials derived from different waste fractions to obtain 50% recycling of all recyclables.</p>
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<p>Prognosis of the individual recycling rates to comply with CE targets.</p>
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<p>Water content and loss of ignition (LOI) of the residual waste fractions.</p>
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<p>Higher and Lower Heating Values of material fractions contained in residual waste.</p>
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<p>Quantity and fuel properties of residual waste stream in the period 2020–2035.</p>
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<p>CO<sub>2</sub> emissions from residual waste combustion in the period 2020–2035.</p>
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34 pages, 6637 KiB  
Review
Heat Transfer Characteristics of Conventional Fluids and Nanofluids in Micro-Channels with Vortex Generators: A Review
by Mushtaq T. Al-Asadi, Hussein A. Mohammed and Mark C. T. Wilson
Energies 2022, 15(3), 1245; https://doi.org/10.3390/en15031245 - 8 Feb 2022
Cited by 7 | Viewed by 3199
Abstract
An effective way to enhance the heat transfer in mini and micro electronic devices is to use different shapes of micro-channels containing vortex generators (VGs). This attracts researchers due to the reduced volume of the electronic micro-chips and increase in the heat generated [...] Read more.
An effective way to enhance the heat transfer in mini and micro electronic devices is to use different shapes of micro-channels containing vortex generators (VGs). This attracts researchers due to the reduced volume of the electronic micro-chips and increase in the heat generated from the devices. Another way to enhance the heat transfer is using nanofluids, which are considered to have great potential for heat transfer enhancement and are highly suited to application in practical heat transfer processes. Recently, several important studies have been carried out to understand and explain the causes of the enhancement or control of heat transfer using nanofluids. The main aim upon which the present work is based is to give a comprehensive review on the research progress on the heat transfer and fluid flow characteristics of nanofluids for both single- and two- phase models in different types of micro-channels. Both experimental and numerical studies have been reviewed for traditional and nanofluids in different types and shapes of micro-channels with vortex generators. It was found that the optimization of heat transfer enhancement should consider the pumping power reduction when evaluating the improvement of heat transfer. Full article
(This article belongs to the Special Issue Computational Heat Transfer and Fluid Mechanics)
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<p>Heat exchanger classification (<b>a</b>) plain rectangular fins: (<b>b</b>) offset strip fins, (<b>c</b>) louvered fins (<b>d</b>) wavy fins.</p>
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<p>Micro-channel omega shape in mm [<a href="#B61-energies-15-01245" class="html-bibr">61</a>] © Elsevier, 2015.</p>
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<p>Different inlet and outlet positions and header shapes of micro-channel heat sinks [<a href="#B63-energies-15-01245" class="html-bibr">63</a>] © Elsevier, 2015. (<b>a</b>) C-type; (<b>b</b>) Z-type; (<b>c</b>) I-type; (<b>d</b>) rectangular; (<b>e</b>) trapezoidal; (<b>f</b>) triangular.</p>
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<p>Magnetic field distribution to enhance the heat transfer [<a href="#B64-energies-15-01245" class="html-bibr">64</a>] © Elsevier, 2015.</p>
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<p>Growth in number of publications of micro-channels [<a href="#B77-energies-15-01245" class="html-bibr">77</a>] © Elsevier, 2017.</p>
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<p>Curved duct with square cross-sectional area [<a href="#B78-energies-15-01245" class="html-bibr">78</a>] © Elsevier, 2011.</p>
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<p>Rectangular micro-channel [<a href="#B83-energies-15-01245" class="html-bibr">83</a>] © Elsevier, 2015; (<b>a</b>) geometry description; (<b>b</b>) various cylindrical grooves (cases A0–A3) and square ribs (case B).</p>
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<p>Rectangular micro-channel [<a href="#B83-energies-15-01245" class="html-bibr">83</a>] © Elsevier, 2015; (<b>a</b>) geometry description; (<b>b</b>) various cylindrical grooves (cases A0–A3) and square ribs (case B).</p>
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<p>Square channel with different ribs and cavities, (<b>a</b>) (Tri.C-C.R), (<b>b</b>) (Tri.C–Tri.R), (<b>c</b>) (Tri.C–Tra.R), (<b>e</b>) (Tra.C-C.R), (<b>f</b>) (Tra.C-Tri.R), (<b>g</b>) (Tra.C- Tra.R) [<a href="#B86-energies-15-01245" class="html-bibr">86</a>] © Elsevier, 2015.</p>
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<p>Irregular channel [<a href="#B86-energies-15-01245" class="html-bibr">86</a>] © Elsevier, 2015.</p>
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<p>Various types of fins (<b>a</b>) LRFA, (<b>b</b>) LTFA, (<b>c</b>) ARFA, and (<b>d</b>) ATFA [<a href="#B90-energies-15-01245" class="html-bibr">90</a>] © Elsevier, 2009.</p>
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<p>Growth of publications in nanofluids [<a href="#B77-energies-15-01245" class="html-bibr">77</a>] © Elsevier, 2017.</p>
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<p>The influence of nanoparticle concentrations on thermal conductivity.</p>
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<p>The influence of nanoparticle concentrations on thermal conductivity [<a href="#B189-energies-15-01245" class="html-bibr">189</a>] © Elsevier, 2016.</p>
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<p>Validations between numerical findings of Qu and Mudawar [<a href="#B235-energies-15-01245" class="html-bibr">235</a>] © Elsevier, 2002 and experiments of Kawano et al. [<a href="#B236-energies-15-01245" class="html-bibr">236</a>] © Elsevier, 1998; (<b>a</b>) friction coefficient, (<b>b</b>) inlet thermal resistance, and (<b>c</b>) outlet thermal resistance.</p>
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<p>Validation of the present model against experimental data of Kawano et al. [<a href="#B236-energies-15-01245" class="html-bibr">236</a>] © Elsevier, 1998 and alternative numerical results of Qu and Mudawar [<a href="#B235-energies-15-01245" class="html-bibr">235</a>] © Elsevier, 2002 and Al-Asadi et al. [<a href="#B1-energies-15-01245" class="html-bibr">1</a>,<a href="#B8-energies-15-01245" class="html-bibr">8</a>].</p>
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14 pages, 1813 KiB  
Article
Fast Real-Time RDFT- and GDFT-Based Direct Fault Diagnosis of Induction Motor Drive
by Piotr Kołodziejek and Daniel Wachowiak
Energies 2022, 15(3), 1244; https://doi.org/10.3390/en15031244 - 8 Feb 2022
Cited by 4 | Viewed by 1866
Abstract
This paper presents the theoretical analysis and experimental verification of a direct fault harmonic identification approach in a converter-fed electric drive for automated diagnosis purposes. On the basis of the analytical model of the proposed real-time direct fault diagnosis, the fault-related harmonic component [...] Read more.
This paper presents the theoretical analysis and experimental verification of a direct fault harmonic identification approach in a converter-fed electric drive for automated diagnosis purposes. On the basis of the analytical model of the proposed real-time direct fault diagnosis, the fault-related harmonic component is calculated using recursive DFT (RDFT) and Goertzel DFT (GDFT), applied instead of the full spectrum calculations required in the most popular FFT algorithm. The simulation model of an inverter sensorlessly controlled induction motor drive is linked with the induction machine rotor fault model for testing the sensitivity of the GDFT- and RDFT-based fault diagnosis to state variable estimation errors. According to the presented simulation results, the accuracy of the direct identification of a fault-related harmonic is sensitive to the quality of fault harmonic frequency estimation. The sensitivity analysis with respect to RDFT and GDFT algorithms is included. Based on the experimental setup with a sensorlessly controlled induction motor drive with the investigated rotor fault, fault diagnosis algorithms were implemented in the microprocessor by integration with the control system in one microcontroller and experimentally verified. The RDFT and GDFT approach has shown accurate and fast direct automated fault identification at a significantly decreased number of arithmetical operations in the microcontroller, which is convenient for the frequency-domain fault diagnosis in electric drives and supports fault-tolerant control system implementation. Full article
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<p>Integrated control system and fault diagnostic algorithms between ADC interrupts of the microcontroller.</p>
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<p>The amplitude of the rotor fault related harmonic obtained using (<b>a</b>) RDFT and (<b>b</b>) Goertzel’s algorithm for variable number of broken rotor bars and variable electromagnetic torque and rotor speed, with no slip estimation error assumed.</p>
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<p>The amplitude of the rotor fault related harmonic obtained using (<b>a</b>) RDFT and (<b>b</b>) Goertzel’s algorithm for a variable number of broken rotor bars.</p>
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<p>The amplitude of broken rotor harmonic at estimated frequency obtained using (<b>a</b>) RDFT and (<b>b</b>) GDFT algorithms for variable slip estimation error.</p>
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<p>(<b>a</b>) Experimental induction motor drive setup with controlled load, (<b>b</b>) back-to-back IGBT-based inverter, (<b>c</b>) rotor with 2 broken rotor bars, (<b>d</b>) rotor with 3 broken rotor bars.</p>
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<p>Real-time amplitude estimation of the rotor fault related harmonic for Goertzel and RDFT algorithms at ω<sub>r</sub> = 0.65, T<sub>L</sub> = 0.72 and (<b>a</b>) Scalar control V/f = const and rotor fault, (<b>b</b>) Scalar control V/f = const and healthy rotor, (<b>c</b>) Multiscalar vector control and broken rotor, (<b>d</b>) Multiscalar vector control and healthy rotor.</p>
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<p>Real-time amplitude estimation of the rotor fault related harmonic for Goertzel and RDFT algorithms at ω<sub>r</sub> = 0.65, T<sub>L</sub> = 0.72 and (<b>a</b>) Scalar control V/f = const and rotor fault, (<b>b</b>) Scalar control V/f = const and healthy rotor, (<b>c</b>) Multiscalar vector control and broken rotor, (<b>d</b>) Multiscalar vector control and healthy rotor.</p>
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13 pages, 10648 KiB  
Article
Multimodal Carbonates: Distribution of Oil Saturation in the Microporous Regions of Arab Formations
by Tadeusz W. Patzek, Ahmed M. Saad and Ahmed Hassan
Energies 2022, 15(3), 1243; https://doi.org/10.3390/en15031243 - 8 Feb 2022
Cited by 11 | Viewed by 2327
Abstract
Perhaps as much as 50% of the oil-in-place in carbonate formations around the world is locked away in the easy to bypass microporosity. If some of this oil is unlocked by the improved recovery processes focused on tight carbonate formations, the world may [...] Read more.
Perhaps as much as 50% of the oil-in-place in carbonate formations around the world is locked away in the easy to bypass microporosity. If some of this oil is unlocked by the improved recovery processes focused on tight carbonate formations, the world may gain a major source of lower-rate power over several decades. Here, we overview the Arab D formation in the largest oil field on earth, the Ghawar. We investigate the occurrence of microporosity of different origins and sizes using scanning electron microscopy (SEM) and pore casting techniques. Then, we present a robust calculation of the probability of invasion and oil saturation distribution in the nested micropores using mercury injection capillary pressure data available in the literature. We show that large portions of the micropores in Arab D formation would have been bypassed during primary drainage unless the invading crude oil ganglia were sufficiently long. We also show that, under prevailing conditions of primary drainage of the strongly water-wet Arab formations in the Ghawar, the microporosity there was invaded and the porosity-weighted initial oil saturations of 60–85% are expected. Considering the asphaltenic nature of crude oil in the Ghawar, we expect the invaded portions of the pores to turn mixed-wet, thus becoming inaccessible to waterflooding until further measures are taken to modify the system’s surface chemistry and/or create substantial local pore pressure gradients. Full article
(This article belongs to the Topic Enhanced Oil Recovery Technologies)
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<p>The 480 carbonate core samples in the database in Appendix A3 of [<a href="#B24-energies-15-01243" class="html-bibr">24</a>] can be described by one, two or three nested macro- and micro-pore systems, each characterized by a unique Thomeer [<a href="#B25-energies-15-01243" class="html-bibr">25</a>,<a href="#B26-energies-15-01243" class="html-bibr">26</a>] hyperbola. The capillary entry pressures into the macroporosity that surrounds the microporous regions are significantly lower than the capillary pressure plateaus that span 30 to 60 plus percent of the total porosity.</p>
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<p>SEM images of the etched Arab-D pore casts showing the three carbonate microporosity types identified by Cantrell and Hagerty [<a href="#B23-energies-15-01243" class="html-bibr">23</a>]; the solid represents the pore space and the etched away grains are black voids. (<b>a</b>) Interparticle (IP) macropores appear as solid epoxy, while type I microporosity in microporous grains (MG) appears as a fine network of sponge-like micropores. (<b>b</b>) The heavily micritized matrix type II microporosity is abundant throughout the sample. MM and MG seem to have very similar pore morphologies. (<b>c</b>) Intraparticle moldic pore (MO) appears as solid epoxy within a micritized grain. Type III microporosity in intercrystalline micropores (MEC) seems to interconnect the MO to the rest of the matrix pore network. Images by Ahmed Hassan [<a href="#B29-energies-15-01243" class="html-bibr">29</a>].</p>
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<p>SEM images of the Arab-D pore casts showing the four nested porositons identified by Cantrell and Hagerty [<a href="#B23-energies-15-01243" class="html-bibr">23</a>], namely, <math display="inline"><semantics> <mi mathvariant="script">M</mi> </semantics></math>, <math display="inline"><semantics> <msub> <mi mathvariant="script">P</mi> <mn>1</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi mathvariant="script">P</mi> <mn>2</mn> </msub> </semantics></math>, and <math display="inline"><semantics> <msub> <mi mathvariant="script">P</mi> <mn>3</mn> </msub> </semantics></math>. The grey solid represents the pore space. Images by Ahmed Hassan [<a href="#B29-energies-15-01243" class="html-bibr">29</a>].</p>
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<p>The semilogarithmic plot of the multimodal probability distribution for all pore throat diameters in the carbonate database in Clerke et al. [<a href="#B24-energies-15-01243" class="html-bibr">24</a>]. Each red curve is a fit of a part of the distribution of the logarithm of pore sizes with a Generalized Extreme Value or GEV pdf for the macroporosity and three normal distributions for the microporosity. The black curve is the sum of the four distributions.</p>
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<p>(<b>a</b>) The probabilities of invading different nested porositons in the carbonate database from Clerke et al. [<a href="#B24-energies-15-01243" class="html-bibr">24</a>]; (<b>b</b>) the corresponding porosity-weighted oil saturations in the nested porositons. P1 denotes primary drainage of cores with only P1inM. P12 is drainage of only porositon 1inM in the cores with two porositons 1 and 2. P22 is drainage of porositon 2 in the cores with 2in1inM. P13 is drainage of porositon 1inM in the cores with three porositons. P23 is drainage of porositon 2in1inM and P33 is drainage of porositon 3in2in1inM in the cores with three porositons.</p>
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<p>(<b>a</b>) The MICP curves for the rock samples the are described by a single Thomeer hyperbola (Porositon 1 in Porositon M ). (<b>b</b>) The MICP curves for the rock samples the are described by two Thomeer hyperbolas (Porositon 2 in Porositon 1 in Porositon M). (<b>c</b>) The MICP curves for the rock samples are described by three Thomeer hyperbolas (Porositon 3 in Porositon 2 in Porositon 1 in Porositon M). <math display="inline"><semantics> <msub> <mi>B</mi> <mi>v</mi> </msub> </semantics></math> is the infinite pressure extrapolation of the sample porosity occupied by mercury that results from a single Thomeer hyperbola. <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>V</mi> <msub> <mo>=</mo> <mrow> <mi>v</mi> <mi>t</mi> <mi>o</mi> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math> is the sum of all <math display="inline"><semantics> <msub> <mi>B</mi> <mi>v</mi> </msub> </semantics></math>s of multiple Thomeer hyperbolas.</p>
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<p>The heights of vertical oil columns necessary to drain the carbonate rock samples with different sets of porositons. From <b>a</b> to <b>f</b> clockwise: oil saturations in samples that contain only Porositon 1 in M, Porositons 1 and 2 in M, Porositon 2 in M, Porositon 3 in 1 in M, Porositons 2 and 3 in M, and Porositon 3 in M.</p>
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19 pages, 15214 KiB  
Article
Impact of Signalized Intersections on CO2 and NOx Emissions of Heavy Duty Vehicles
by Nicolás Deschle, Ernst Jan van Ark, René van Gijlswijk and Robbert Janssen
Energies 2022, 15(3), 1242; https://doi.org/10.3390/en15031242 - 8 Feb 2022
Cited by 4 | Viewed by 2196
Abstract
Pollutant emissions have been a topic of interest in the last decades. Not only environmentalists but also governments are taking rapid action to reduce emissions. As one of the main contributors, the transport sector is being subjected to strict scrutiny to ensure it [...] Read more.
Pollutant emissions have been a topic of interest in the last decades. Not only environmentalists but also governments are taking rapid action to reduce emissions. As one of the main contributors, the transport sector is being subjected to strict scrutiny to ensure it complies with the short and long-term regulations. The measures imposed by governments clearly involve all the stakeholders in the logistics sector, from road authorities and logistic operators to truck manufacturers. The improvement of traffic conditions is one of the perspectives in which the reduction of emissions is being addressed. Optimization of traffic flow, avoidance of unnecessary stops, control of the cruise speed, and coordination of trips in an energy-efficient way are necessary steps to remain compliant with the upcoming regulations. In this study, we have estimated the CO2 and NOx emissions in heavy-duty vehicles while traversing signalized intersections, and we examined the differences between various behavioral scenarios. We found a consistent trend indicating that avoiding a stop can potentially reduce CO2 and NOx emissions by up to 0.32kg and 1.8g, respectively. Furthermore, an upper bound for the yearly CO2 savings is provided for the case of The Netherlands. A reduction of 3.2% of the total CO2 emitted by heavy-duty vehicles is estimated. These results put traffic control in the main scene as a yet unexplored dimension to control pollutant emissions, enabling authorities to more accurately estimate cost–benefit plans for traffic control system investments. Full article
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<p>Diagram of a tractor with the location of the different measuring systems installed on the vehicle depicted in green. (1) CAN-bus sensor, (2) GPS antenna, (3) Air pressure sensor, (4) Temperature sensor, and (5) Exhaust sensors of NO<sub>x</sub> and O<sub>2</sub>. (0) Indicates the location of the onboard unit system responsible for logging the data.</p>
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<p>Example of the extraction of the data around the intersections. (<b>a</b>) Signalized intersections were identified on the map. (<b>b</b>) The fragment of length 2 km symmetric around the intersection was selected based on the position data. (<b>c</b>) All the time series relevant for this study were gathered; from top to bottom, the velocity, acceleration, instantaneous CO<sub>2</sub>, and the instantaneous NO<sub>x</sub>. At the bottom, the position of the vehicle relative to the stop line is plotted for reference. There is a period between 60 to 100 <math display="inline"><semantics> <mi mathvariant="normal">s</mi> </semantics></math> in which the vehicle is waiting at the traffic light. Around 100 <math display="inline"><semantics> <mi mathvariant="normal">s</mi> </semantics></math> is when the vehicle starts moving. Data spans only until the vehicle is 1000 <math display="inline"><semantics> <mi mathvariant="normal">m</mi> </semantics></math> past the intersection.</p>
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<p>Details of the set of rules used in the study to determine the three clusters. The rules are mainly based on the approach to the intersection, and they are significantly laxer on the conditions of the velocity after the intersection. In all cases, the vehicle mean velocity over the immediate 400 <math display="inline"><semantics> <mi mathvariant="normal">m</mi> </semantics></math> segment 600 <math display="inline"><semantics> <mi mathvariant="normal">m</mi> </semantics></math> away from the intersection is greater than 60 <math display="inline"><semantics> <mrow> <mi>km</mi> <mo>/</mo> <mi mathvariant="normal">h</mi> </mrow> </semantics></math>. The 600 <math display="inline"><semantics> <mi mathvariant="normal">m</mi> </semantics></math> prior to the intersection is where the clusters are distinguished; for the no-stop case, the mean velocity is greater than 60<math display="inline"><semantics> <mrow> <mi>km</mi> <mo>/</mo> <mi mathvariant="normal">h</mi> </mrow> </semantics></math> over the whole 600 <math display="inline"><semantics> <mi mathvariant="normal">m</mi> </semantics></math> segment. For the stop cluster, the vehicle stops completely (the velocity vanishes) at least once in the segment. Finally, for the slow-down cluster, the mean velocity is lower than the mean velocity on the previous segment but greater than 30 <math display="inline"><semantics> <mrow> <mi>km</mi> <mo>/</mo> <mi mathvariant="normal">h</mi> </mrow> </semantics></math>, and furthermore, the instantaneous velocity is greater than 30 <math display="inline"><semantics> <mrow> <mi>km</mi> <mo>/</mo> <mi mathvariant="normal">h</mi> </mrow> </semantics></math>. The mean velocity over the 1000 <math display="inline"><semantics> <mi mathvariant="normal">m</mi> </semantics></math> after the intersection for the vehicles in all the clusters needs to be greater than 30 <math display="inline"><semantics> <mrow> <mi>km</mi> <mo>/</mo> <mi mathvariant="normal">h</mi> </mrow> </semantics></math>.</p>
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<p>Speed profiles for the three different groups. The median instantaneous speed (<math display="inline"><semantics> <mrow> <msubsup> <mi>v</mi> <mi>j</mi> <mo>′</mo> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>, see Equation (<a href="#FD7-energies-15-01242" class="html-disp-formula">7</a>)) of each of the three groups; no-stop, slow-down, and stop in green, amber, and red, respectively. The shaded areas indicate the instantaneous standard deviation for each of the groups.</p>
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<p>(<b>a</b>) Median instantaneous CO<sub>2</sub> flow for the three groups. Green, amber, and red represent the no-stop, slow-down, and stop groups, respectively. Position is relative to the location of the intersection as described on OSM. Vertical dashed lines denote the different regions used to group the speed profiles (see <a href="#sec2-energies-15-01242" class="html-sec">Section 2</a>). (<b>b</b>) Results for the CO<sub>2</sub> flow for the three groups. Green, amber, and red represent the no-stop, slow-down, and stop groups, respectively. Differences between all groups are significant.</p>
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<p>Distribution of the CO<sub>2</sub> emitted on the 2 km passage. In green, amber, and red the median values for the no-stop, slow-down, and stop clusters, respectively. The distribution is skewed, and many values of emitted CO<sub>2</sub> occur when interaction with other factors such as traffic is present. This indicates that the gap between the no-stopping vehicle and a vehicle that stops can increase.</p>
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<p>(<b>a</b>) Median instantaneous NO<sub>x</sub> flow for the three groups. Green, amber, and red represent the no-stop, slow-down, and stop groups, respectively. The position is relative to the location of the intersection, as described on OSM. The vertical dashed lines denote the different regions used to group the speed profiles (see <a href="#sec2-energies-15-01242" class="html-sec">Section 2</a> for further details). (<b>b</b>) Results for the NO<sub>x</sub> flow for the three groups. Green, amber, and red represent the no-stop, slow-down, and stop groups, respectively. Differences between all groups are significant.</p>
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<p>Cumulative flows for the three groups. Green, amber, and red represent the no-stop, slow-down, and stop groups, respectively. The position is relative to the location of the intersection as described in OSM. The vertical dashed line denotes the location of the intersection. (<b>a</b>) Results for the CO<sub>2</sub> flow for the three groups; (<b>b</b>) Results for NO<sub>x</sub> for the three groups. Green, amber, and red represent the no-stop, slow-down, and stop groups, respectively.</p>
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<p>Values used to scale up the results to all the trips in The Netherlands. (<b>a</b>) The number of signalized intersections per km of road for each trip. The mean value, <math display="inline"><semantics> <mrow> <mn>0.89</mn> </mrow> </semantics></math>, represents the expectation value for the number of intersections found per km of road. (<b>b</b>) Shows the probabilities <math display="inline"><semantics> <msub> <mi>p</mi> <mi>i</mi> </msub> </semantics></math> with <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>∈</mo> <mo>{</mo> <mi>no</mi> <mo>-</mo> <mi>stop</mi> <mo>,</mo> <mi>slow</mi> <mo>-</mo> <mi>down</mi> <mo>,</mo> <mi>stop</mi> <mo>}</mo> </mrow> </semantics></math> to find each of the three different behaviors when an HDV encounters a traffic light.</p>
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<p>Distribution of the total mass of the vehicles (tractor and load). The mean value is indicated in red.</p>
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32 pages, 796 KiB  
Review
A Review of DC-AC Converters for Electric Vehicle Applications
by Khairy Sayed, Abdulaziz Almutairi, Naif Albagami, Omar Alrumayh, Ahmed G. Abo-Khalil and Hedra Saleeb
Energies 2022, 15(3), 1241; https://doi.org/10.3390/en15031241 - 8 Feb 2022
Cited by 27 | Viewed by 17722
Abstract
This paper comprehensively reviews the current status of multidisciplinary technologies in electric vehicles. Because the electric vehicle market will expand dramatically in the coming few years, research accomplishments in power electronics technology for electric vehicles will be highly attractive. Challenges in power electronics [...] Read more.
This paper comprehensively reviews the current status of multidisciplinary technologies in electric vehicles. Because the electric vehicle market will expand dramatically in the coming few years, research accomplishments in power electronics technology for electric vehicles will be highly attractive. Challenges in power electronics technology for driving electric vehicles, charging batteries, and circuit topologies are being explored. This paper aims primarily to address the practical issues of the future electric vehicles and help researchers obtain an overview of the latest techniques in electric vehicles, focusing on power electronics-based solutions for both current and future electric vehicle technologies. In this work, different medium-and high-voltage DC-AC inverter topologies are investigated and compared in terms of power losses and component requirements. Recent research on electric vehicle power converters is also discussed, with highlighting on soft-switching and multilevel inverters for electric vehicle motor drives. In this paper, a methodical overview and general classification of DC-AC power converters are presented. In specific topologies, drawbacks such as voltage stresses on active switches and control complications may occur, which can make them difficult for immediate commercialization. However, various modified circuit topologies have been recommended to overcome these drawbacks and enhance the system performance. Full article
(This article belongs to the Special Issue Planning and Operation of Microgrids)
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<p>Various types of power electronics converters used in a typical series hybrid vehicle design system.</p>
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<p>Three-Phase Two-Level Inverter (TLI) Topology for EV Motor Drive.</p>
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<p>Three-phase three-Level topology of neutral point clamped multilevel inverter (NPC-MLI) for EV motor drive.</p>
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<p>Classifications of the DC-AC iverters.</p>
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<p>Three-phase voltage source inverter (VSI) topology.</p>
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<p>Three-phase current source inverter (CSI) topology.</p>
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<p>Three-phase impedance source inverter (ISI) topology.</p>
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<p>Single-phase two boost inverter (TBI) topology.</p>
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<p>Resonant AC-link inverter for EV motor drive. (<b>a</b>) Series resonant AC-link, (<b>b</b>) Parallel resonant AC-link.</p>
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<p>Series Resonant DC-link Inverter (SRDCLI) Topology for EV Motor Drive.</p>
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<p>Parallel resonant DC-link inverter (PRDCLI) topology for EV motor drive.</p>
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<p>Active Clamp Resonant DC-Link Inverter (ACRDCLI) Topology for EV Motor Drive.</p>
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<p>Reduced voltage resonant DC-link inverter (RVRDCLI) for EV motor drive.</p>
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<p>Load resonant DC-AC inverter (LRI) topology for EV motor drive. (<b>a</b>) Series-load resonant DC-AC inverter, and (<b>b</b>) Parallel-load resonant DC-AC inverter.</p>
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<p>Three-phase zero voltage transition (ZVT) inverter topology for EV motor drive.</p>
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<p>Three-phase zero-current transition (ZCT) inverter topology for EV motor drive.</p>
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<p>Three-phase resonant snubber (ZVT) DC-AC inverter topology for EV motor drive.</p>
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<p>Three-phase quasi-resonant DC-AC inverter topology for EV motor drive.</p>
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<p>Three-phase three-level diode-clamped multilevel inverter (DC-MLI) for EV motor drive.</p>
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<p>Three-phase three-levels flying-capacitor multilevel inverter (FC-MLI) topology for EV motor drive.</p>
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<p>Three-phase three-level topology of cascaded H-bridge multilevel inverter (CHB–MLI) for EV motor drive.</p>
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<p>Three-phase five-level cascaded switched-capacitor multilevel boost inverter (CBSC-MLI) for EV motor drive.</p>
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<p>Three-phase seven-level switched-inductor multilevel inverter (SI-MLI) for EV motor drive.</p>
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<p>Three-phase thirteen-level switched-capacitor multilevel boost inverter (SCB-MLI) with partial charging.</p>
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<p>Three-phase seven-level switched-capacitor PWM inverter using series-parallel (SCI-S/P) conversion.</p>
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<p>Three-phase hybrid multilevel inverter (HMLI-SC) using switched-capacitor for EV motor drive.</p>
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19 pages, 6372 KiB  
Article
Multi-Objective Optimization of Parameters of Channels with Staggered Frustum of a Cone Based on Response Surface Methodology
by Zhen Zhao, Liang Xu, Jianmin Gao, Lei Xi, Qicheng Ruan and Yunlong Li
Energies 2022, 15(3), 1240; https://doi.org/10.3390/en15031240 - 8 Feb 2022
Cited by 8 | Viewed by 1929
Abstract
In this study, Response Surface Methodology (RSM) and multi-objective genetic algorithm were used to obtain optimum parameters of the channels with frustum of a cone with better flow and heat transfer performance. Central composite face-centered design (CCF) was applied [...] Read more.
In this study, Response Surface Methodology (RSM) and multi-objective genetic algorithm were used to obtain optimum parameters of the channels with frustum of a cone with better flow and heat transfer performance. Central composite face-centered design (CCF) was applied to the experimental design of the channel parameters, and on this basis, the response surface models were constructed. The sensitivity of the channel parameters was analyzed by Sobol’s method. The multi-objective optimization of the channel parameters was carried out with the goal of achieving maximum Nusselt number ratio (Nu/Nu0) and minimum friction coefficient ratio (f/f0). The results show that the root mean square errors (RSME) of the fitted response surface models are less than 0.25 and the determination coefficients (R2) are greater than 0.93; the models have high accuracy. Sobol’s method can quantitatively analyze the influence of the channel parameters on flow and heat transfer performance of the channels. When the response is Nu/Nu0, from high to low, the total sensitivity indexes of the channel parameters are frustum of a cone angle (α), Reynolds number (Re), spanwise spacing ratio (Z2/D), and streamwise spacing ratio (Z1/D). When the response is f/f0, the total sensitivity indexes of the channel parameters from high to low are Re, Z1/D, α and Z2/D. Four optimization channels are selected from the Pareto solution set obtained by multi-objective optimization. Compared with the reference channel, the Nu/Nu0 of the optimized channels is increased by 21.36% on average, and the f/f0 is reduced by 9.16% on average. Full article
(This article belongs to the Special Issue Heat Transfer and Heat Recovery Systems)
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<p>Physical model of the periodic channel with staggered frustums of a cone.</p>
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<p>The calculation model of the channel.</p>
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<p>The grid diagram of the calculation model.</p>
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<p>Verification of the numerical method.</p>
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<p>Flow chart of the optimization of the channel parameters.</p>
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<p>Comparison between numerical calculation and <span class="html-italic">RSM</span> prediction: (<b>a</b>) <span class="html-italic">Nu</span>/<span class="html-italic">Nu</span><sub>0</sub>; (<b>b</b>) <span class="html-italic">f</span>/<span class="html-italic">f</span><sub>0</sub>.</p>
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<p>3D surface and contour map of <span class="html-italic">Nu/Nu</span><sub>0</sub>: (<b>a</b>) <span class="html-italic">Re–</span><span class="html-italic">α</span>; (<b>b</b>) <span class="html-italic">Re–Z</span><sub>1</sub>/<span class="html-italic">D</span>; (<b>c</b>) <span class="html-italic">Re–Z</span><sub>2</sub>/<span class="html-italic">D</span>; (<b>d</b>) <span class="html-italic">α–Z</span><sub>1</sub>/<span class="html-italic">D</span>; (<b>e</b>) <span class="html-italic">α–Z</span><sub>2</sub>/<span class="html-italic">D</span>; (<b>f</b>) <span class="html-italic">Z</span><sub>1</sub>/<span class="html-italic">D–Z</span><sub>2</sub>/<span class="html-italic">D</span>.</p>
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<p>3D surface and contour map of <span class="html-italic">f/f</span><sub>0</sub>: (<b>a</b>) <span class="html-italic">Re–</span><span class="html-italic">α</span>; (<b>b</b>) <span class="html-italic">Re–Z</span><sub>1</sub>/<span class="html-italic">D</span>; (<b>c</b>) <span class="html-italic">Re–Z</span><sub>2</sub>/<span class="html-italic">D</span>; (<b>d</b>) <span class="html-italic">α–Z</span><sub>1</sub>/<span class="html-italic">D</span>; (<b>e</b>) <span class="html-italic">α–Z</span><sub>2</sub>/<span class="html-italic">D</span>; (<b>f</b>) <span class="html-italic">Z</span><sub>1</sub>/<span class="html-italic">D–Z</span><sub>2</sub>/<span class="html-italic">D</span>.</p>
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<p>Sensitivity of the parameters when the response is <span class="html-italic">Nu</span>/<span class="html-italic">Nu</span><sub>0</sub>: (<b>a</b>) first-order sensitivity index; (<b>b</b>) total sensitivity index.</p>
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<p>Sensitivity of the parameters when the response is <span class="html-italic">f/f</span><sub>0</sub>: (<b>a</b>) first-order sensitivity index; (<b>b</b>) total sensitivity index.</p>
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<p>Solution set of multi-objective optimization.</p>
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<p>Result of K-means clustering of the Pareto solution set.</p>
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<p>The Comparison of Reference Channel and Optimization Point C: (<b>a</b>) Surface Streamline and Temperature Distribution of the Reference Channel; (<b>b</b>) Surface Streamline and Temperature Distribution of the Optimized Channel; (<b>c</b>) <span class="html-italic">Nu</span> Distribution of the Reference Channel; (<b>d</b>) <span class="html-italic">Nu</span> Distribution of the Optimized Channel.</p>
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42 pages, 8882 KiB  
Article
Accelerating Energy-Economic Simulation Models via Machine Learning-Based Emulation and Time Series Aggregation
by Alexander J. Bogensperger, Yann Fabel and Joachim Ferstl
Energies 2022, 15(3), 1239; https://doi.org/10.3390/en15031239 - 8 Feb 2022
Cited by 1 | Viewed by 2404
Abstract
Energy-economic simulation models with high levels of detail, high time resolutions, or large populations (e.g., distribution networks, households, electric vehicles, energy communities) are often limited due to their computational complexity. This paper introduces a novel methodology, combining cluster-based time series aggregation and sampling [...] Read more.
Energy-economic simulation models with high levels of detail, high time resolutions, or large populations (e.g., distribution networks, households, electric vehicles, energy communities) are often limited due to their computational complexity. This paper introduces a novel methodology, combining cluster-based time series aggregation and sampling methods, to efficiently emulate simulation models using machine learning and significantly reduce both simulation and training time. Machine learning-based emulation models require sufficient and high-quality data to generalize the dataset. Since simulations are computationally complex, their maximum number is limited. Sampling methods come into play when selecting the best parameters for a limited number of simulations ex ante. This paper introduces and compares multiple sampling methods on three energy-economic datasets and shows their advantage over a simple random sampling for small sample-sizes. The results show that a k-means cluster sampling approach (based on unsupervised learning) and adaptive sampling (based on supervised learning) achieve the best results especially for small sample sizes. While a k-means cluster sampling is simple to implement, it is challenging to increase the sample sizes if the emulation model does not achieve sufficient accuracy. The iterative adaptive sampling is more complex during implementation, but can be re-applied until a certain accuracy threshold is met. Emulation is then applied on a case study, emulating an energy-economic simulation framework for peer-to-peer pricing models in Germany. The evaluated pricing models are the “supply and demand ratio” (SDR) and “mid-market rate pricing” (MMR). A time series aggregation can reduce time series data of municipalities by 99.4% with less than 5% error for 98.2% (load) and 95.5% (generation) of all municipalities and hence decrease the simulation time needed to create sufficient training data. This paper combines time series aggregation and emulation in a novel approach and shows significant acceleration by up to 88.9% of the model’s initial runtime for the simulation of the entire population of around 12,000 municipalities. The time for re-calculating the population (e.g., for different scenarios or sensitivity analysis) can be increased by a factor of 1100 while still retaining high accuracy. The analysis of the simulation time shows that time series aggregation and emulation, considered individually, only bring minor improvements in the runtime but can, however, be combined effectively. This can significantly speed up both the simulation itself and the training of the emulation model and allows for flexible use, depending on the capabilities of the models and the practitioners. The results of the peer-to-peer pricing for approximately 12,000 German municipalities show great potential for energy communities. The mechanisms offer good incentives for the addition of necessary flexibility. Full article
(This article belongs to the Special Issue Artificial Intelligence for Smart Energy Systems)
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<p>Schematic illustration of (hybrid) simulation, emulation, and surrogate/meta-models.</p>
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<p>Basic emulation or surrogate/meta-model workflow based on the literature review.</p>
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<p>Methodology applied in this paper.</p>
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<p>Comparison of sampling methods with multiple datasets and machine learning models.</p>
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<p>Impact of sampling methods on dataset 1.</p>
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<p>Impact of sampling methods on dataset 2.</p>
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<p>Impact of sampling methods on dataset 3.</p>
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<p>Comparison of the accuracy in time series aggregation using k-Means and random sampling.</p>
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<p>Distribution of prediction errors per described feature of all German municipalities (<span class="html-italic">n</span> = 11,977) with 50 typical hours neglecting values outside the range between the 1st and 99th percentiles.</p>
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<p>High-level structure of the simulation framework.</p>
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<p>Simulation time of municipalities by number of buildings for all generated municipalities excluding 1% of outliers (<span class="html-italic">n</span> = 11,838).</p>
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<p>Detailed structure of the simulation model and the parts substituted by our emulation-model (gray).</p>
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<p>Supply duration curves for nine municipalities of different sizes, including the ground truth, the model prediction, own consumption, and the total consumption within the community.</p>
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<p>Supply duration curves for nine municipalities of different sizes, including the ground truth, the model prediction, and the total generation within the community.</p>
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<p>Simulation sampling, training, testing, and benchmarking time of the supply and demand model with and without time series aggregation.</p>
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<p>Model accuracy as a function of sampling size using TSA for training. The testing was conducted on the benchmark dataset.</p>
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<p>Distribution of annual average P2P prices of all German municipalities (<span class="html-italic">n</span> = 11,977). Actual prices are shown in (<b>a</b>) while weighted prices are displayed in (<b>b</b>). The prices in (<b>b</b>) have been weighted per hour according to the current demand (buy prices) or supply (sell prices).</p>
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<p>Price duration curve of average P2P prices (per hour) of all municipalities in the population (<span class="html-italic">n</span> = 11,977).</p>
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<p>Regional disparities in savings from leveraging P2P buy prices versus normal retail pricing.</p>
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<p>Regional disparities in added value from leveraging P2P sell prices versus selling at the market.</p>
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<p>Flow chart of our adaptive sampling scheme as implemented in this work.</p>
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<p>Simplified visualization of the hierarchy and attributes of classes used for generating a digital representation of the assets inside a municipality using the preprocessing module of the simulation framework. Each class inherits all attributes of its parent class.</p>
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<p>MAE of the trained ML model (supply) depending on mean generation and consumption within each municipality in the test set.</p>
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<p>MAE of the trained ML model (demand) depending on mean generation and consumption within each municipality in the test set.</p>
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25 pages, 8601 KiB  
Article
Simulation Research on a Cogeneration System of Low-Concentration Photovoltaic/Thermal Coupled with Air-Source Heat Pump
by Dengxin Ai, Ke Xu, Heng Zhang, Tianheng Chen and Guilin Wang
Energies 2022, 15(3), 1238; https://doi.org/10.3390/en15031238 - 8 Feb 2022
Cited by 4 | Viewed by 1823
Abstract
In this paper, a low-concentration photovoltaic/thermal (LCPV/T) coupled with air-source heat pump (AHP) system is proposed which fully utilizes the heat generated by LCPV/T and improves the performance of the AHP. The system is built and investigated in the Transient System Simulation Program [...] Read more.
In this paper, a low-concentration photovoltaic/thermal (LCPV/T) coupled with air-source heat pump (AHP) system is proposed which fully utilizes the heat generated by LCPV/T and improves the performance of the AHP. The system is built and investigated in the Transient System Simulation Program (TRNSYS) and an experimental room model is established to verify the feasibility of the system. The performance of the system is researched from the perspective of energy and exergy, and the system performance with LCPV/T and without LCPV/T is compared. Finally, the influence of the variation of key parameters of the system is studied. The results indicated that on the coldest day, the electrical efficiency of LCPV/T reached 10% which was equal to the electrical exergy efficiency. The maximum thermal efficiency was 31.88% while thermal exergy efficiency was 2.7%. The maximum coefficient of performance (COP) of AHP was 3.3, and the thermal exergy efficiency was 47%. The indoor temperature was maintained at about 20 °C in the heating season. When LCPV/T was adopted, the COP and thermal exergy efficiency of the AHP was generally higher than those without LCPV/T. In conclusion, the utilization of LCPV/T has a positive impact on the performance of the AHP. Full article
(This article belongs to the Special Issue Smart Photovoltaic Energy Systems for a Sustainable Future Ⅱ)
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<p>Schematic cross-sectional view of PV/T.</p>
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<p>Geometric schematic of CPC. Reprint with permission [<a href="#B25-energies-15-01238" class="html-bibr">25</a>]. Copyright 2017, Elsevier.</p>
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<p>Schematic diagram of the LCPV/T coupled with AHP system.</p>
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<p>Inlet and outlet temperature of LCPV/T on 15 January.</p>
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<p>Power and thermal performance of LCPV/T on 15 January.</p>
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<p>Operation performance of AHP on 15 January.</p>
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<p>Exergy performance of LCPV/T on 15 January.</p>
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<p>Exergy performance of AHP on 15 January.</p>
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<p>Weekly power generation and consumption of the system.</p>
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<p>Pressure variation of fan in heating season.</p>
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<p>Pressure variation of LCPV/T in heating season.</p>
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<p>Entropy change of fan in heating season.</p>
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<p>Entropy change of LCPV/T in heating season.</p>
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<p>Indoor temperature comparison between experimental room and reference room.</p>
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<p>Annual power generation and consumption of the LCPV/T-AHP system.</p>
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<p>AHP performance comparison between the LCPV/T-AHP system and AHP only system on 15 January.</p>
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<p>Power consumption comparison between the LCPV/T-AHP system and the AHP only system.</p>
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<p>COP comparison between the LCPV/T-AHP system and the AHP only system.</p>
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<p>AHP exergy efficiency comparison between the LCPV/T-AHP system and the AHP only system.</p>
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<p>Electrical efficiency and thermal efficiency of LCPV/T under variable air flow rate.</p>
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<p>Electrical exergy efficiency and thermal exergy efficiency of the LCPV/T under variable air flow rate.</p>
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<p>COP and exergy efficiency of the LCPV/T under variable air flow rate.</p>
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<p>Power consumption of the AHP and the power production of the LCPV/T under variable air flow rate.</p>
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<p>Electrical efficiency and thermal efficiency of the LCPV/T under variable PV cell area.</p>
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<p>Electrical exergy efficiency and thermal exergy efficiency of the LCPV/T under variable PV cell area.</p>
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<p>Variation of COP and exergy efficiency of the AHP under different PV cell area.</p>
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<p>Power generation of the LCPV/T and consumption of the AHP under different PV cell area.</p>
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17 pages, 6567 KiB  
Article
Design of Distributed Bragg Reflectors for Green Light-Emitting Devices Based on Quantum Dots as Emission Layer
by Iman E. Shaaban, Ahmed S. Samra, Shabbir Muhammad and Swelm Wageh
Energies 2022, 15(3), 1237; https://doi.org/10.3390/en15031237 - 8 Feb 2022
Cited by 1 | Viewed by 3362
Abstract
Light-emitting diodes based on quantum dots as an active emission can be considered as a promising next generation for application in displays and lighting. We report a theoretical investigation of green emission at 550 nm of microcavity inorganic–organic light-emitting devices based on Zn [...] Read more.
Light-emitting diodes based on quantum dots as an active emission can be considered as a promising next generation for application in displays and lighting. We report a theoretical investigation of green emission at 550 nm of microcavity inorganic–organic light-emitting devices based on Zn (Te, Se) alloy quantum dots as an active layer. Distributed Bragg Reflector (DBR) has been applied as a bottom mirror. The realization of high-quality DBR consisting of both high and low refractive index structures is investigated. The structures applied for high refractive index layers are (ZrO2, SiNx, ZnS), while those applied for low index layers are (Zr, SiO2, CaF2). DBR of ZnS/CaF2 consisting of three pairs with a high refractive index step of (Δn = 0.95) revealed a broad stop bandwidth (178 nm) and achieved a high reflectivity of 0.914. Full article
(This article belongs to the Topic Applications of Nanomaterials in Energy Systems)
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<p>(<b>a</b>) Schematic structure of device, (<b>b</b>) energy level diagram.</p>
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<p>Reflectance of DBR structure.</p>
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<p>Microcavity structure used for an optical analysis.</p>
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<p>Emission peak positions against QD size.</p>
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<p>Schematic layer structure of three devices at 550 nm based on (<b>a</b>) DBR1 (<b>b</b>) DBR2 (<b>c</b>) DBR3.</p>
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<p>Reflectance spectra by the number of pairs for devices based on (<b>a</b>) DBR1 (<b>b</b>) DBR2 (<b>c</b>) DBR3.</p>
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<p>Calculated finesse versus number of DBR periods for devices based on (DBR1, DBR2, and DBR3).</p>
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<p>Plots of stopband width and bottom mirror peak reflectance versus refractive index contrast.</p>
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<p>Electroluminance spectra for devices based on (<b>a</b>) DBR1 (<b>b</b>) DBR2 (<b>c</b>) DBR3.</p>
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<p>Electroluminance spectra for devices based on (<b>a</b>) DBR1 (<b>b</b>) DBR2 (<b>c</b>) DBR3.</p>
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<p>Electroluminescence spectra of device based on DBR3 at three-resonance mode.</p>
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<p>Calculated bottom reflection spectra of the device fabricated by Kitabayashi et al. [<a href="#B20-energies-15-01237" class="html-bibr">20</a>].</p>
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23 pages, 6427 KiB  
Article
Power Load Forecasting Considering Climate Factors Based on IPSO-Elman Method in China
by Jicheng Liu and Yu Yin
Energies 2022, 15(3), 1236; https://doi.org/10.3390/en15031236 - 8 Feb 2022
Cited by 16 | Viewed by 1900
Abstract
In order to implement the national need for the optimal allocation of power resources, power load forecasting, as an important research topic, has important theoretical and practical significance. The purpose of this study is to construct a prediction model considering climate factors based [...] Read more.
In order to implement the national need for the optimal allocation of power resources, power load forecasting, as an important research topic, has important theoretical and practical significance. The purpose of this study is to construct a prediction model considering climate factors based on a large amount of historical data, and to prove that the prediction accuracy is related to both climate factors and load regularity. The results of load forecasting are affected by many climate factors, so firstly the climate variables affecting load forecasting are screened. Secondly, a load prediction model based on the IPSO-Elman network learning algorithm is constructed by taking the difference between the predicted value of the neural network and the actual value as the fitness function of particle swarm optimization. In view of the great influence of weights and thresholds on the prediction accuracy of the Elman neural network, the particle swarm optimization algorithm (PSO) is used to optimize parameters in order to improve the prediction accuracy of ELMAN neural network. Thirdly, prediction with and without climate factors is compared and analyzed, and the prediction accuracy of the model compared by using cosine distance and various error indicators. Finally, the stability discriminant index of historical load regularity is introduced to prove that the accuracy of the prediction model is related to the regularity of historical load in the forecast area. The prediction method proposed in this paper can provide reference for power system scheduling. Full article
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<p>IPSO-Elman neural network prediction flow chart.</p>
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<p>Indicator histogram of Region 1.</p>
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<p>Indicator P-P diagram of Region 1.</p>
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<p>Indicator histogram of Region 2.</p>
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<p>Indicator P-P diagram of Region 2.</p>
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<p>State of partial scattered plot map. (<b>a</b>) Average Daily Load and Maximum Temperature (Region 1) (<b>b</b>) Average Daily Load and Minimum Temperature (Region 2).</p>
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<p>Indicator histograms of Region 1.</p>
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<p>Indicator scattered plot map of Region 1.</p>
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<p>Comparison of forecasting errors with four neuron numbers.</p>
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<p>Structure of IPSO-Elman neural network.</p>
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<p>Forecast results on 11 January 2021 (<b>a</b>) Forecast results for Region 1 (<b>b</b>) Forecast results for Region 2.</p>
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<p>Forecasting results from 11 January 2021 to 17 January 2021. (<b>a</b>) Forecasting results for Region 1. (<b>b</b>) Forecasting results for Region 2.</p>
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<p>Forecast results considering climate factors. (<b>a</b>) Forecast results for Region 1. (<b>b</b>) Forecast results for Region 2.</p>
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<p>Comparison of forecast and actual load with climate factors (10 January).</p>
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<p>Comparison of actual load and forecasting results with and without climate Factors (10 January).</p>
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<p>High-frequency component of Region 1.</p>
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<p>High-frequency component of Region 2.</p>
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13 pages, 9566 KiB  
Review
Critical Review on Robust Speed Control Techniques for Permanent Magnet Synchronous Motor (PMSM) Speed Regulation
by Kifayat Ullah, Jaroslaw Guzinski and Adeel Feroz Mirza
Energies 2022, 15(3), 1235; https://doi.org/10.3390/en15031235 - 8 Feb 2022
Cited by 48 | Viewed by 6669
Abstract
The permanent magnet synchronous motor (PMSM) is a highly efficient energy saving machine. Due to its simple structural characteristics, good heat radiation capability, and high efficiency, PMSMs are gradually replacing AC induction motors in many industrial applications. The PMSM has a nonlinear system [...] Read more.
The permanent magnet synchronous motor (PMSM) is a highly efficient energy saving machine. Due to its simple structural characteristics, good heat radiation capability, and high efficiency, PMSMs are gradually replacing AC induction motors in many industrial applications. The PMSM has a nonlinear system and lies on parameters that differ over time with complex high-class dynamics. To achieve the excessive performance operation of a PMSM, it essentially needs a speed controller for providing accurate speed tracking, slight overshoot, and robust disturbance repulsion. Therefore, this article provides an overview of different robust control techniques for PMSMs and reviews the implementation of a speed controller. In view of the uncertainty factors, such as parameter perturbation and load disturbance, the H∞ robust control strategy is mainly reviewed based on the traditional control techniques, i.e., robust H∞ sliding mode controller (SMC), and H∞ robust current controller based on Hamilton–Jacobi Inequality (HJI) theory. Based on comparative analysis, this review simplifies the development trend of different control technologies used for a PMSM speed regulation system. Full article
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<p>Principal diagram of PMSM speed regulation based on H∞ robust controller.</p>
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<p>Basic structure of PMSM PI controller.</p>
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<p>Basic structure of the PMSM PCC control.</p>
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<p>Basic structure of the PMSM SMC control.</p>
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<p>Basic structure of FLC.</p>
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<p>Basic structure of the PMSM FLC control.</p>
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<p>Structure of ANN layers.</p>
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<p>Basic structure of the PMSM NNC control.</p>
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<p>Basic structure of the PMSM ESO control.</p>
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20 pages, 9705 KiB  
Article
State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles under Dynamic Load Conditions
by Ethelbert Ezemobi, Mario Silvagni, Ahmad Mozaffari, Andrea Tonoli and Amir Khajepour
Energies 2022, 15(3), 1234; https://doi.org/10.3390/en15031234 - 8 Feb 2022
Cited by 28 | Viewed by 3408
Abstract
Among numerous functions performed by the battery management system (BMS), online estimation of the state of health (SOH) is an essential and challenging task to be accomplished periodically. In electric vehicle (EV) applications, accurate SOH estimation minimizes failure risk and improves reliability by [...] Read more.
Among numerous functions performed by the battery management system (BMS), online estimation of the state of health (SOH) is an essential and challenging task to be accomplished periodically. In electric vehicle (EV) applications, accurate SOH estimation minimizes failure risk and improves reliability by predicting battery health conditions. The challenge of accurate estimation of SOH is based on the uncertain dynamic operating condition of the EVs and the complex nonlinear electrochemical characteristics exhibited by the lithium-ion battery. This paper presents an artificial neural network (ANN) classifier experimentally validated for the SOH estimation of lithium-ion batteries. The ANN-based classifier model is trained experimentally at room temperature under dynamic variable load conditions. Based on SOH characterization, the training is done using features such as the relative values of voltage, state of charge (SOC), state of energy (SOE) across a buffer, and the instantaneous states of SOC and SOE. At implementation, due to the slow dynamics of SOH, the algorithm is triggered on a large-scale periodicity to extract these features into buffers. The features are then applied as input to the trained model for SOH estimation. The classifier is validated experimentally under dynamic varying load, constant load, and step load conditions. The model accuracies for validation data are 96.2%, 96.6%, and 93.8% for the respective load conditions. It is further demonstrated that the model can be applied on multiple cell types of similar specifications with an accuracy of about 96.7%. The performance of the model analyzed with the confusion matrices is consistent with the requirements of the automotive industry. The classifier was tested on a Texas F28379D microcontroller unit (MCU) board. The result shows that an average real-time execution speed of 8.34 µs is possible with a negligible memory occupation. Full article
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<p>Architecture for SOH estimation using ANN-based classifier. The yellow signals are measurements from the cell.</p>
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<p>Experimental setup for data acquisition from lithium-ion batteries connected in series.</p>
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<p>Characteristics of the major profiles used for cell aging. (<b>a</b>) constant current (1.75 A) constant voltage charge; (<b>b</b>) constant current (0.7 A) discharge; (<b>c</b>) step current (0 A to 4.23 A) charge; (<b>d</b>) pack constant power discharge (75 W); (<b>e</b>) step current (0 A to −10 A) discharge; (<b>f</b>) dynamic current profile.</p>
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<p>SOH of cylindrical LG MJI 18650 lithium-ion cell across the aging cycles.</p>
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<p>Characterization of the SOH based on cell voltage and capacity at ambient temperature.</p>
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<p>Characterization of the SOH based on the cell voltage and SOC, and capacity at ambient temperature.</p>
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<p>Characterization of the SOH based on the cell voltage and SOC, and SOE at ambient temperature.</p>
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<p>Feature extraction across buffer of time length of 40 s. (<b>a</b>) Relative values of voltage, SOC, and SOE in the first cycle. (<b>b</b>) Relative values of voltage, SOC, and SOE in the last cycle.</p>
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<p>Architecture for pattern recognition feed-forward artificial neural network (ANN) for SOH classification. x(n): input; w: weight of layer neurons; b: bias of the layers; HAF: hidden layer activation function; OAF: output layer activation function.</p>
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<p>Mean square error performance result for ANN-based classifier training using Levenberg–Marquardt function.</p>
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<p>First-two dynamic load profiles of the aging cycles applied for model training and validation.</p>
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<p>Confusion matrix for performance analysis of ANN-based classifier in the dataset trained with LG 18650 MJ1 lithium-ion. The diagonal cells are correctly classified buffers. The last row with gray background is the TPR. The last column with gray background is the precision per class. The bottom right cell with dark background is the total accuracy.</p>
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<p>Confusion matrix for performance analysis of ANN-based classifier under dynamic changing load profile validation. The diagonal cells are correctly classified buffers. The last row with gray background is the TPR. The last column with gray background is the precision per class. The bottom right cell with dark background is the total accuracy.</p>
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<p>Samples of constant load profile and the voltage used for model validation.</p>
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<p>Confusion matrix for performance analysis of ANN-based classifier under constant charge current profile validation. The diagonal cells are correctly classified buffers. The last row with gray background is the TPR. The last column with gray background is the precision per class. The bottom right cell with dark background is the total accuracy.</p>
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<p>Sample of step charge current profile and the voltage used for model validation.</p>
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<p>Confusion matrix for performance analysis of ANN-based classifier under the step charging current profile validation. The diagonal cells are correctly classified buffers. The last row with gray background is the TPR. The last column with gray background is the precision per class. The bottom right cell with dark background is the total accuracy.</p>
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<p>SOH of cylindrical Sanyo NCR 18650 GA lithium-ion cell across the aging cycles.</p>
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<p>Confusion matrix for model validation with NCR 18650 GA lithium-ion cell under dynamic load profile. The diagonal cells are correctly classified buffers. The last row with gray background is the TPR. The last column with gray background is the precision per class. The bottom right cell with dark background is the total accuracy.</p>
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<p>Result summary of the training and validation of the classifier indicating the TPR, precision per class, and the total accuracy of the classifier according to the confusion matrices.</p>
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18 pages, 3558 KiB  
Article
Porewater Geochemical Assessment of Seismic Indications for Gas Hydrate Presence and Absence: Mahia Slope, East of New Zealand’s North Island
by Richard B. Coffin, Gareth Crutchley, Ingo Pecher, Brandon Yoza, Thomas J. Boyd and Joshu Mountjoy
Energies 2022, 15(3), 1233; https://doi.org/10.3390/en15031233 - 8 Feb 2022
Cited by 2 | Viewed by 1966
Abstract
We compare sediment vertical methane flux off the Mahia Peninsula, on the Hikurangi Margin, east of New Zealand’s North Island, with a combination of geochemical, multichannel seismic and sub-bottom profiler data. Stable carbon isotope data provided an overview of methane contributions to shallow [...] Read more.
We compare sediment vertical methane flux off the Mahia Peninsula, on the Hikurangi Margin, east of New Zealand’s North Island, with a combination of geochemical, multichannel seismic and sub-bottom profiler data. Stable carbon isotope data provided an overview of methane contributions to shallow sediment carbon pools. Methane varied considerably in concentration and vertical flux across stations in close proximities. At two Mahia transects, methane profiles correlated well with integrated seismic and TOPAS data for predicting vertical methane migration rates from deep to shallow sediment. However, at our “control site”, where no seismic blanking or indications of vertical gas migration were observed, geochemical data were similar to the two Mahia transect lines. This apparent mismatch between seismic and geochemistry data suggests a potential to underestimate gas hydrate volumes based on standard seismic data interpretations. To accurately assess global gas hydrate deposits, multiple approaches for initial assessment, e.g., seismic data interpretation, heatflow profiling and controlled-source electromagnetics, should be compared to geochemical sediment and porewater profiles. A more thorough data matrix will provide better accuracy in gas hydrate volume for modeling climate change and potential available energy content. Full article
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<p>(<b>a</b>) Regional overview map showing the location of the study area (blue box) within the Hikurangi subduction wedge, east of New Zealand’s North Island. Map coordinates are in degrees (WGS84 datum). “mbsl” = meters below sea level. (<b>b</b>) Enlargement of the study area (Mahia Study Area) showing 2D seismic reflection lines shown in this study (blue lines), as well as piston core sites (yellow dots, labeled, e.g., PC01) at the three transects. The red contour marks the approximate upper limit of hydrate stability in sediments. Note: TOPAS data that we show in this manuscript are coincident with the seismic lines (blue lines).</p>
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<p>Geophysical data and interpretations at the Control Site (see <a href="#energies-15-01233-f001" class="html-fig">Figure 1</a>b for location). (<b>a</b>) TOPAS data showing the locations (black arrows) of Piston Cores 01, 65 and 66 (see <a href="#energies-15-01233-f001" class="html-fig">Figure 1</a>b for geographic locations). (<b>b</b>) Multichannel seismic data—amplitudes. Same horizontal scale, but different vertical scale than (<b>a</b>). GHSZ: Gas hydrate stability zone (see text for details). (<b>c</b>) root-mean-square (RMS) amplitudes from (<b>b</b>). Identical scale and extent as (<b>b</b>). (<b>d</b>) Variance attribute of the seismic data in (<b>b</b>). Identical scale and extent as (<b>b</b>). (<b>e</b>) Structural interpretation of the seismic data that may explain the lack of BSRs in the presence of high CH<sub>4</sub> flux near the surface (see discussion). Note: interpretation extent is from the vertical dotted lines in (<b>d</b>), which are also shown in (<b>b</b>,<b>c</b>). (<b>f</b>) Enlargement of shallow part of our interpretation, from the broken black box in (<b>e</b>).</p>
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<p>Geochemical data from the Control Site. Piston core numbers presented in seismic profile (<a href="#energies-15-01233-f002" class="html-fig">Figure 2</a>) are indicated in red boxes. See <a href="#energies-15-01233-f001" class="html-fig">Figure 1</a>b for geographical locations and <a href="#energies-15-01233-f002" class="html-fig">Figure 2</a> for locations on TOPAS and seismic data. Geochemical data include sediment CH<sub>4</sub> and porewater SO<sub>4</sub><sup>2−</sup> concentrations. “cmbsf” = cm below seafloor. Table shows the sulfate–methane transition zone (SMT), determined from the SO<sub>4</sub><sup>2−</sup> concentrations.</p>
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<p>(<b>a</b>) TOPAS data from Mahia Transect 1; see <a href="#energies-15-01233-f001" class="html-fig">Figure 1</a>b for geographical location. Numbered black arrows are piston core sites. Highly reflective regions interpreted as free gas (labeled). (<b>b</b>) Seismic data from the same location as the TOPAS data above. Horizontal and vertical scales are the same as those used for the TOPAS data. The possible base of gas hydrate stability (BGHS) is interpreted as the upper termination of strong reflectivity.</p>
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<p>Geochemical data from Mahia Transect 1. Piston core numbers presented in seismic profile (<a href="#energies-15-01233-f004" class="html-fig">Figure 4</a>) are indicated in red boxes. See <a href="#energies-15-01233-f001" class="html-fig">Figure 1</a>b for geographical locations and <a href="#energies-15-01233-f004" class="html-fig">Figure 4</a> for locations on TOPAS and seismic data. Geochemical data include sediment CH<sub>4</sub> and porewater SO<sub>4</sub><sup>2−</sup> concentrations. “cmbsf” = cm below seafloor. Table in lower right shows the sulfate–methane transition zone (SMT), determined from the SO<sub>4</sub><sup>2−</sup> concentrations.</p>
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<p>(<b>a</b>) TOPAS data from Mahia Transect 2; see <a href="#energies-15-01233-f001" class="html-fig">Figure 1</a>b for geographical location. Numbered black arrows are piston core sites. (<b>b</b>) Seismic data from the same location as the TOPAS data in (<b>a</b>). Horizontal scale is the same as for the above TOPAS data plot in (<b>a</b>), while the vertical scale differs. The possible base of gas hydrate stability (BGHS) is interpreted as the upper termination of strong reflectivity.</p>
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<p>Geochemical data from Mahia Transect 2. Piston core numbers presented in seismic profile (<a href="#energies-15-01233-f006" class="html-fig">Figure 6</a>) are indicated in red boxes. See <a href="#energies-15-01233-f001" class="html-fig">Figure 1</a>b for geographical locations and <a href="#energies-15-01233-f006" class="html-fig">Figure 6</a> for locations on TOPAS and seismic data. Geochemical data include sediment CH<sub>4</sub> and porewater SO<sub>4</sub><sup>2−</sup> concentrations. “cmbsf” = cm below seafloor. Table in lower right shows the sulfate–methane transition zone (SMT), determined from the SO<sub>4</sub><sup>2−</sup> concentrations.</p>
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<p>A comparison of sediment core total inorganic and organic carbon concentrations and d<sup>13</sup>C through piston core profiles. Sediment carbon profiles are presented from the core sites with highest vertical CH<sub>4</sub> flux. Piston core numbers presented in seismic and TOPAS profiles (<a href="#energies-15-01233-f002" class="html-fig">Figure 2</a>, <a href="#energies-15-01233-f004" class="html-fig">Figure 4</a> and <a href="#energies-15-01233-f006" class="html-fig">Figure 6</a>) are indicated in red boxes.</p>
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19 pages, 3293 KiB  
Article
Evaluation and Dynamic Evolution of the Total Factor Environmental Efficiency in China’s Mining Industry
by Xiangqian Wang, Shudong Wang and Yongqiu Xia
Energies 2022, 15(3), 1232; https://doi.org/10.3390/en15031232 - 8 Feb 2022
Cited by 8 | Viewed by 1772
Abstract
The mining industry plays an extremely important strategic role in China’s economic and social development. In the new era of pursuing circular/green/efficient development, the evaluation of the total factor environmental efficiency (TFEE) of China’s mining industry is essential for alleviating resource waste and [...] Read more.
The mining industry plays an extremely important strategic role in China’s economic and social development. In the new era of pursuing circular/green/efficient development, the evaluation of the total factor environmental efficiency (TFEE) of China’s mining industry is essential for alleviating resource waste and environmental pollution. The Epsilon-Based Measure (EBM) model effectively solves the shortcomings of radial and non-radial DEA models. In addition, the Malmquist–Luenberger (ML) index can measure the dynamic change of efficiency value. Combining the EBM model and the ML productivity index, this paper evaluates the TFEE from the static and dynamic perspective in China’s 31 provincial mining industries over the period 2007–2016. The Theil index is employed to reveal the root of the overall provincial TFEE gap (OGTFEE) in China’s mining industry. The results show that the average total factor static environmental efficiency (TFSEE) of China’s provincial mining industry exhibits a low score of 0.6589 and with significant spatio-temporal differences. The provincial TFEE gap within four major areas (WGTFEE), especially that in east and west areas, is the main cause of the OGTFEE in China’s mining industry. Technical change contributes more to the TFEE decline in China’s mining industry. There are differences in improving the TFEE among China’s 31 provincial mining industries, and corresponding countermeasures can be formulated accordingly. This study provides theoretical and practical basis for the clean and green development of China’s mining industry. Full article
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<p>Research framework for this study.</p>
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<p>Changes of redundancy rate of the input and output variables in China’s mining industry.</p>
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<p>Average redundancy rate of input and output variables in China’s 31 provincial mining industries from 2007 to 2016.</p>
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<p>Change trend of TFDEE and its decomposition index in China’s mining industry.</p>
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<p>Average score of TFDEE and its decomposition index in China’s 31 provincial mining industries during 2007–2016.</p>
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<p>The advantages and disadvantages for improving environmental efficiency in China’s 31 provincial mining industries.</p>
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23 pages, 7955 KiB  
Article
A Comparative Study of Different Quality Oil Shales Developed in the Middle Jurassic Shimengou Formation, Yuqia Area, Northern Qaidam Basin, China
by Yueyue Bai, Zhaojun Liu, Simon C. George and Jingyao Meng
Energies 2022, 15(3), 1231; https://doi.org/10.3390/en15031231 - 8 Feb 2022
Cited by 4 | Viewed by 1990
Abstract
Oil shales are developed in the Shale Member of the Middle Jurassic Shimengou Formation in the Qaidam Basin, China. The oil shales can be classified into three quality groups (low-, medium-, and high-quality oil shales) through a comprehensive analysis protocol that includes Rock-Eval [...] Read more.
Oil shales are developed in the Shale Member of the Middle Jurassic Shimengou Formation in the Qaidam Basin, China. The oil shales can be classified into three quality groups (low-, medium-, and high-quality oil shales) through a comprehensive analysis protocol that includes Rock-Eval pyrolysis, total organic carbon (TOC) content, proximate analysis, gas chromatography-mass spectrometry (GC-MS), X-ray diffraction (XRD), major and trace element analyses, and maceral analysis. The low-quality oil shales mainly contain type II1 kerogen, the medium-quality oil shales mainly contain type I-II1 kerogen, and the high-quality oil shales mainly contain type I kerogen. All are immature to early thermally mature. The oil yield of the oil shales is directly related to their quality and are positively correlated with TOC content and calorific value. All studied samples were deposited under anaerobic conditions but in different paleoenvironments. The low-quality oil shales were mainly deposited in fresh-water environments, whereas the high-quality oil shales were usually developed in highly saline and reducing environments. Salinity stratification and evidence of algal blooms that are conducive to organic matter enrichment were identified in both medium- and high-quality oil shales, the latter having the highest paleoproductivity and the best preservation conditions. In summary, shale quality is controlled by a combination of factors, including algal abundance, preservation conditions, the existence of algal blooms and salinity stratification, and paleoproductivity. This study reveals how these different factors affect the quality of oil shales, which might provide an in-depth explanation for the formation process of lacustrine oil shales. Full article
(This article belongs to the Special Issue Research and Development Progress in Oil Shale)
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<p>Geological map of the Yuqia Area in the Qaidam Basin, China, showing the location of the YYY-1 well (modified from [<a href="#B24-energies-15-01231" class="html-bibr">24</a>]).</p>
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<p>Vertical distribution of samples from the Shale Member of the Shimengou Formation, YYY-1 well.</p>
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<p>Characteristics of different-quality oil shales. (<b>a</b>) Oil shales in an outcrop, Yuqia area; (<b>b</b>) Low-quality oil shales, 358.30–358.40 m, YYY-1 well; (<b>c</b>) Medium-quality oil shales, 394.06–397.08 m, YYY-1 well; (<b>d</b>) High-quality oil shales, 342.18–343.21 m, YYY-1 well; (<b>e</b>) Medium-quality oil shales, rhythmic bedding, 353.56 m, YYY-1 well; (<b>f</b>) High-quality oil shales, horizontal bedding, 342.80 m, YYY-1 well; (<b>g</b>) Fish fossils in low-quality oil shales, 358.01 m, YYY-1 well; (<b>h</b>) Shell fossils in medium-quality oil shales, 399.67 m, YYY-1 well).</p>
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<p>Characteristics of organic macerals in different quality oil shales. (<b>a</b>) 342.48 m, high-quality oil shale, alginites; (<b>b</b>) 346.20 m, high-quality oil shale, alginites, and sporinite; (<b>c</b>) 368.30 m, low-quality oil shale, vitrinite; (<b>d</b>) 348.80 m, medium-quality oil shale, resinite, incident ligh; (<b>e</b>) 348.80 m, medium-quality oil shale, resinite, fluorescence; (<b>f</b>) 370.10 m, low-quality oil shale, sporinite).</p>
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<p>The organic matter type of different-quality oil shales in the Shale Member of the Shimengou Formation. Diagrams after Espitalié et al., Langford and Blanc-Valleron, and Mukhopadhyay et al. [<a href="#B64-energies-15-01231" class="html-bibr">64</a>,<a href="#B65-energies-15-01231" class="html-bibr">65</a>,<a href="#B66-energies-15-01231" class="html-bibr">66</a>].</p>
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<p>Vertical distribution of salinity and terrigenous detrital input in different-quality oil shales in the Shale Member of the Shimengou Formation (See <a href="#energies-15-01231-f002" class="html-fig">Figure 2</a> for a legend; Gamma/C30 hopane = Gammacerane/C<sub>30</sub> αβ hopane).</p>
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<p>Vertical distribution of redox conditions and lithology in different-quality oil shales in the Shale Member of the Shimengou Formation (See <a href="#energies-15-01231-f002" class="html-fig">Figure 2</a> for a legend; see <a href="#energies-15-01231-t004" class="html-table">Table 4</a> for ratio abbreviations).</p>
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<p>Vertical distribution of paleoproductivity in different-quality oil shales in the Shale Member of the Shimengou Formation (See <a href="#energies-15-01231-f002" class="html-fig">Figure 2</a> for a legend; see <a href="#energies-15-01231-t004" class="html-table">Table 4</a> for ratio abbreviations).</p>
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<p>Correlations of total organic carbon (TOC) content and alginite content for the different quality oil shales in the Shale Member of the Shimengou Formation.</p>
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<p>Relationships of (<b>a</b>) Tmax versus oxygen index (OI), and (<b>b</b>) total organic carbon (TOC) content versus hydrogen index (HI) for the different quality oil shales in the Shale Member of the Shimengou Formation.</p>
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<p>Relationships between total organic carbon (TOC) content and (<b>a</b>) terrigenous detrital minerals, and (<b>b</b>) clay minerals in the different-quality oil shales in the Shale Member of the Shimengou Formation.</p>
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<p>Organic matter enrichment models for the different quality oil shales in the Shale Member of Shimengou Formation. (<b>a</b>) organic matter enrichment model of low-quality oil shale; (<b>b</b>) organic matter enrichment model of medium-quality oil shale; (<b>c</b>) organic matter enrichment model of high-quality oil shale.</p>
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28 pages, 17612 KiB  
Article
Assessment of the Effectiveness of Photovoltaic Panels at Public Transport Stops: 3D Spatial Analysis as a Tool to Strengthen Decision Making
by Anna Fijałkowska, Kamila Waksmundzka and Jerzy Chmiel
Energies 2022, 15(3), 1230; https://doi.org/10.3390/en15031230 - 8 Feb 2022
Cited by 6 | Viewed by 2684
Abstract
The potential of solar energy encourages research into new applications of this technology. Access to renewable energy is an important element of modern urban policies aimed at sustainable development and the energy security of residents but also limits energy production from conventional sources [...] Read more.
The potential of solar energy encourages research into new applications of this technology. Access to renewable energy is an important element of modern urban policies aimed at sustainable development and the energy security of residents but also limits energy production from conventional sources due to the pollution associated with them. More and more often, projects of new urban infrastructure facilities include integrated photovoltaic panels. Assessing solar potential is an important step when planning the layout of solar panels, and the increasing number of high-rise buildings increases shaded areas, sometimes even for most of the day. Therefore, a detailed shading analysis can be important for city decision makers, investors and local communities. The results of the 3D spatial analysis presented in the article can be used to optimize the location and analyse the profitability of photovoltaic installations in a city. The aim of the project was to evaluate the effectiveness of photovoltaic panels on the shelters of public transport bus/tram stops. The proposed methodology for calculating the solar potential and shading may be a valuable extension of existing solutions in the field of planning installation power and the location of individual panels. The research methodology can be used in the future to support decision making and spatial planning related to the placement of photovoltaic panels. It was tested for bus shelters located in the centre of Warsaw (Poland). The results can also be used to assess the impact of alternatives to newly designed high-rise buildings and to plan the provision of photovoltaic panels to other city infrastructure facilities. Full article
(This article belongs to the Special Issue Energy Decision Making: Problems, Methods, and Tools)
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<p>Global summary of estimated solar photovoltaic (PV) power generation potential [<a href="#B49-energies-15-01230" class="html-bibr">49</a>].</p>
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<p>Global summary of estimated solar photovoltaic (PV) power generation potential—enlargement for the area of Poland (52° N, 19° E) [<a href="#B49-energies-15-01230" class="html-bibr">49</a>].</p>
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<p>The location of the Rondo ONZ 04 tram stop. (<b>a</b>) Visualization of the location of the Rondo ONZ 04 tram stop (source: Google Maps, 2021). (<b>b</b>) The surroundings of the Rondo ONZ 04 tram stop, view from the northwest (source: Google Earth 2021). (<b>c</b>) Visualization of the outlines of buildings (orange) in the vicinity of the Rondo ONZ 04 tram stop (data sources: BDOT10k and orthophotomap (WMS) provided by PZGiK).</p>
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<p>The location of the Rondo ONZ 04 tram stop. (<b>a</b>) Visualization of the location of the Rondo ONZ 04 tram stop (source: Google Maps, 2021). (<b>b</b>) The surroundings of the Rondo ONZ 04 tram stop, view from the northwest (source: Google Earth 2021). (<b>c</b>) Visualization of the outlines of buildings (orange) in the vicinity of the Rondo ONZ 04 tram stop (data sources: BDOT10k and orthophotomap (WMS) provided by PZGiK).</p>
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<p>The number of buildings and number of floors of buildings in the vicinity of the Rondo ONZ 04 tram stop, included in the analysis (source: study based on BDOT10k).</p>
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<p>The location of the Dworzec Centralny 08 tram stop. (<b>a</b>) Visualization of the location of the Dworzec Centralny 08 tram stop (source: Google Maps, 2021). (<b>b</b>) The surroundings of the Dworzec Centralny 08 tram stop, view from the northwest (source: Google Earth 2021). (<b>c</b>) Visualization of the outlines of buildings (blue) in the vicinity of the Dworzec Centralny 08 tram stop (data sources: BDOT10k and orthophotomap (WMS) provided by PZGiK).</p>
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<p>The number of buildings and number of floors of buildings in the vicinity of the Dworzec Centralny 08 tram stop, included in the analysis (source: study based on BDOT10k).</p>
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<p>Subsequent levels of detail for building representation in CityGML models [<a href="#B58-energies-15-01230" class="html-bibr">58</a>].</p>
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<p>Factors included in solar and shading analysis.</p>
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<p>The scheme of the methodology showing the subsequent stages of the analyses.</p>
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<p>The result of the stage of data preparation for spatial analysis—3D layers for the surroundings of the Rondo ONZ 04 tram stop.</p>
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<p>The result of the stage of data preparation for spatial analysis—3D layers for the surroundings of the Dworzec Centralny 08 tram stop.</p>
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<p>Average monthly daily solar radiation (kWh/m<sup>2</sup>) for the centre of Warsaw in June and December (there are no data for December 1995) (Data source: [<a href="#B64-energies-15-01230" class="html-bibr">64</a>]).</p>
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<p>Subsequent stages of calculating the viewshed model for one DTM cell: (<b>a</b>) illustration of directions from a given location, (<b>b</b>) obscuring model (the visible part of the sky is shown in white, invisible in grey), (<b>c</b>) the obtained model superimposed on a semicircular photograph of the sky [<a href="#B77-energies-15-01230" class="html-bibr">77</a>].</p>
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<p>Examples of viewshed modelling results: (<b>a</b>) sun map for 45° N, calculated for the period between the winter solstice (21 December) and the summer solstice (21 June); the illustration shows that the position of the sun in the sky changes throughout the year depending on the time of day and month; (<b>b</b>) sky map divided into sectors—eight zenith angles and 16 azimuths; each colour represents a unique sector or part of the sky from which the scattered radiation comes (random colours) [<a href="#B77-energies-15-01230" class="html-bibr">77</a>].</p>
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<p>The viewshed superimposed (<b>a</b>) on the sun map; (<b>b</b>) on the sky map (grey colour marks obstructed sky directions [<a href="#B77-energies-15-01230" class="html-bibr">77</a>].</p>
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<p>Solar potential values (kWh/m<sup>2</sup>) obtained for the Rondo ONZ 04 bus shelter, calculated for 10:30 A.M. on 22 June 2020.</p>
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<p>Shadow solids (in blue) generated in the morning for a selected part of buildings in the centre of Warsaw on a selected day of March [<a href="#B82-energies-15-01230" class="html-bibr">82</a>].</p>
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<p>Workflow diagram for 3D shading spatial analysis.</p>
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<p>Arrangement of buildings and their shadows for the neighbourhood of Rondo ONZ 04 tram stop generated for 22 June 2020 at 10:30 a.m.</p>
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<p>Arrangement of buildings and their shadows for the neighbourhood of Dworzec Centralny 08 tram stop generated for 22 June 2020 at 10:30 a.m.</p>
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<p>Diagram showing various scenarios of spatial analysis taken into account depending on the type of relationship between the solids of shadows cast by buildings and the roofs.</p>
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<p>Specific relationships between the shadow cast by the buildings (grey) and the tram shelter (orange): (<b>a</b>) partial shading; (<b>b</b>) partial shading, but the overlapping part does not include any part of the shelter roof.</p>
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<p>Results obtained for the shelter roof in the neighbourhood of Dworzec Centralny 08 tram stop for 22 June 2020: (<b>a</b>) solar potential in kWh per stop shelter roof area; (<b>b</b>) percentage loss due to shelter shading; (<b>c</b>) actual solar potential including shading (kWh) per stop shelter roof area. The maximum value is about 1.8 kWh (6.48 MJ).</p>
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<p>Results obtained for the shelter roof in the neighbourhood of Dworzec Centralny 08 tram stop for 22 December 2020: (<b>a</b>) solar potential in kWh per stop shelter roof area; (<b>b</b>) percentage loss due to shelter shading; (<b>c</b>) actual solar potential including shading (kWh) per stop shelter roof area. The maximum value is about 0.38 kWh (1.37 MJ).</p>
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<p>Results obtained for the shelter roof in the neighbourhood of Rondo ONZ 04 tram stop for 22 June 2020: (<b>a</b>) solar potential in kWh per stop shelter roof area; (<b>b</b>) percentage loss due to shelter shading; (<b>c</b>) actual solar potential including shading (kWh) per stop shelter roof area. The maximum value is about 1.9 kWh (6.84 MJ).</p>
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<p>Results obtained for the shelter roof in the neighbourhood of Rondo ONZ 04 tram stop for 22 December 2020: (<b>a</b>) solar potential in kWh per stop shelter roof area; (<b>b</b>) percentage loss due to shelter shading; (<b>c</b>) actual solar potential including shading (kWh) per stop shelter roof area. The maximum value is about 1.8 kWh (6.48 MJ).</p>
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25 pages, 1666 KiB  
Article
Drivers and Challenges of Peer-to-Peer Energy Trading Development in Thailand
by Siripha Junlakarn, Phimsupha Kokchang and Kulyos Audomvongseree
Energies 2022, 15(3), 1229; https://doi.org/10.3390/en15031229 - 8 Feb 2022
Cited by 28 | Viewed by 6455
Abstract
Recent developments in disruptive technologies along with the cost reduction of photovoltaics have been transforming business models in the electricity sector worldwide. The rise of prosumers has led to a more decentralized and open local green energy market through the emergence of peer-to-peer [...] Read more.
Recent developments in disruptive technologies along with the cost reduction of photovoltaics have been transforming business models in the electricity sector worldwide. The rise of prosumers has led to a more decentralized and open local green energy market through the emergence of peer-to-peer (P2P) energy trading, where consumers and prosumers can buy or sell electricity through an online trading platform. P2P energy trading has the potential to make green energy more accessible at the local level, provide a customer choice that aligns with community values, and promote the use of renewable energy (RE) for local consumption. Although P2P energy trading has already been adopted in some countries, its implementation remains challenging in other countries, including Thailand. In this work, we investigated the drivers and challenges of implementing P2P energy trading in Thailand based on the perspectives of P2P energy trading pilot project developers participating in the regulatory sandbox program. A strategic framework was used to identify the respondents’ standpoints on the political, economic, social, technological, legal, and environmental (PESTLE) factors that can influence the implementation of P2P energy trading. This can help businesses, policymakers, and regulators better understand drivers and barriers of P2P energy trading, which is a potential local energy market. This paper also provides policy recommendations for regulatory changes for the future development of P2P energy trading, including opening a third-party access (TPA) regime, enabling a liberalized market in the electricity market, and integrating the role and responsibilities of the prosumer for P2P energy trading into existing law. Full article
(This article belongs to the Special Issue Sustainable Energy & Society)
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<p>Overview of P2P projects in the ERC sandbox program in Thailand classified by project developer, project area, and the use of BT.</p>
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<p>Perceived drivers for P2P energy trading project development under the PESTEL framework.</p>
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<p>Perceived challenges of project developers to P2P energy trading project development under the PESTEL analysis.</p>
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33 pages, 5319 KiB  
Article
Electro-Hydraulic Variable-Speed Drive Networks—Idea, Perspectives, and Energy Saving Potentials
by Lasse Schmidt and Kenneth Vorbøl Hansen
Energies 2022, 15(3), 1228; https://doi.org/10.3390/en15031228 - 8 Feb 2022
Cited by 18 | Viewed by 3054
Abstract
Electro-hydraulic differential cylinder drives with variable-speed displacement units as their central transmission element are subject to an increasing focus in both industry and academia. A main reason is the potential for substantial efficiency increases due to avoidance of throttling of the main flows. [...] Read more.
Electro-hydraulic differential cylinder drives with variable-speed displacement units as their central transmission element are subject to an increasing focus in both industry and academia. A main reason is the potential for substantial efficiency increases due to avoidance of throttling of the main flows. Research contributions have mainly been focusing on appropriate compensation of volume asymmetry and the development of standalone self-contained and compact solutions, with all necessary functions onboard. However, as many hydraulic actuator systems encompass multiple cylinders, such approaches may not be the most feasible ones with respect to efficiency or commercial feasibility. This article presents the idea of multi-cylinder drives, characterized by electrically and hydraulically interconnected variable-speed displacement units essentially allowing for completely avoiding throttle elements, while allowing for hydraulic and electric power sharing as well as the sharing of auxiliary functions and fluid reservoir. With drive topologies taking offset in communication theory, the concept of electro-hydraulic variable-speed drive networks is introduced. Three different drive networks are designed for an example application, including component sizing and controls in order to demonstrate their potentials. It is found that such drive networks may provide simple physical designs with few building blocks and increased energy efficiencies compared to standalone drives, while exhibiting excellent dynamic properties and control performance. Full article
(This article belongs to the Special Issue Intelligent Fluid Power Drive Technology)
Show Figures

Figure 1

Figure 1
<p>Electro-hydraulic variable-speed drive network in dual cylinder system with VsD interconnections between all four chambers. Here, × marks possible points at which VsD(s) can be connected to link the system to a reservoir.</p>
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<p>Examples of basic data communication networks.</p>
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<p>Basic electro-hydraulic variable-speed drive network topologies for a dual cylinder system.</p>
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<p>Examples of electro-hydraulic variable-speed drive network topologies in case of shared chambers.</p>
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<p>Hydraulically actuated crane used for case study.</p>
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<p>Losses estimated from [<a href="#B59-energies-15-01228" class="html-bibr">59</a>] with <math display="inline"><semantics> <mrow> <mi>D</mi> <mo>=</mo> <msub> <mi>D</mi> <mi>ref</mi> </msub> <mo>=</mo> <mn>27.75</mn> </mrow> </semantics></math> ccm. (<b>A</b>) A4FM torque loss; (<b>B</b>) A4FM drain flow; (<b>C</b>) A4FM cross port leakage flow.</p>
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<p>(<b>A</b>) Water cooled MS2N copper loss coefficient; (<b>B</b>) water cooled MS2N core loss coefficient with <math display="inline"><semantics> <mrow> <msub> <mi>ε</mi> <mi mathvariant="normal">c</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>.</p>
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<p>(<b>A</b>) Total efficiency of the reference VsD, from inverter inlet to hydraulic outlet; (<b>B</b>) displacement unit efficiency; (<b>C</b>) electric motor efficiency; (<b>D</b>) inverter efficiency.</p>
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<p>(<b>A</b>) Point-to-point VDN topology; (<b>B</b>) specific interconnection scheme with 0 ↔ 1 and with 0 ↔ 3.</p>
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<p>Schematic for VDN-PP with interconnection scheme 0 ↔ 1, 0 ↔ 3.</p>
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<p>(<b>A</b>) Example of interconnection scheme for linear VDN topology; (<b>B</b>) specific interconnection scheme 1 ↔ 2 ↔ 3 ↔ 4, 0 ↔ 1 for linear VDN topology.</p>
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<p>Schematic for VDN-L with interconnection scheme 2 ↔ 1 ↔ 3 ↔ 4, 0 ↔ 3.</p>
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<p>(<b>A</b>) Example of interconnection scheme for linear VDN topology; (<b>B</b>) specific interconnection scheme 0 ↔ 1 ↔ 23 ↔ 4 for linear VDN topology with shared chambers.</p>
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<p>Schematic for VDN-L with interconnection scheme 2 ↔ 1 ↔ 3 ↔ 4, 0 ↔ 3.</p>
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<p>System control structure for VDN-PP actuated crane. The control structures for the VDN-L and VDN-LS appear in a similar way. Note that <math display="inline"><semantics> <msup> <mo>•</mo> <mo>∗</mo> </msup> </semantics></math> reference for a variable •.</p>
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<p>(<b>A</b>,<b>D</b>) Closed loop frequency responses for VDN-PP in crane application. (<b>B</b>,<b>E</b>) closed loop frequency responses for VDN-L in crane application; (<b>C</b>,<b>F</b>) closed loop frequency responses for VDN-LS in crane application. All responses are evaluated at mid-strokes.</p>
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<p>Valve actuated crane application with separate metering supplied by variable-speed displacement unit, and associated efficiency under payload retraction, <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>x</mi> <mo>˙</mo> </mover> <mrow> <mn>1</mn> <mo>,</mo> <mi>max</mi> </mrow> </msub> <mo>=</mo> <mo>−</mo> <msub> <mover accent="true"> <mi>x</mi> <mo>˙</mo> </mover> <mrow> <mn>2</mn> <mo>,</mo> <mi>max</mi> </mrow> </msub> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math> mm/s. Under payload extension, the efficiency is zero as this is a load aiding situation.</p>
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<p>Efficiency for VDN-PP in crane application with interconnection scheme 0 ↔ 1 and 0 ↔ 3. (<b>A</b>) Total efficiency under payload extension, <math display="inline"><semantics> <mrow> <mo>−</mo> <msub> <mover accent="true"> <mi>x</mi> <mo>˙</mo> </mover> <mrow> <mn>1</mn> <mo>,</mo> <mi>max</mi> </mrow> </msub> <mo>=</mo> <msub> <mover accent="true"> <mi>x</mi> <mo>˙</mo> </mover> <mrow> <mn>2</mn> <mo>,</mo> <mi>max</mi> </mrow> </msub> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math> mm/s; (<b>B</b>) total efficiency under payload retraction, <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>x</mi> <mo>˙</mo> </mover> <mrow> <mn>1</mn> <mo>,</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>=</mo> <mo>−</mo> <msub> <mover accent="true"> <mi>x</mi> <mo>˙</mo> </mover> <mrow> <mn>2</mn> <mo>,</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math> mm/s. <math display="inline"><semantics> <msub> <mover accent="true"> <mi>η</mi> <mo>¯</mo> </mover> <mi mathvariant="normal">D</mi> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>η</mi> <mo>¯</mo> </mover> <mi mathvariant="normal">M</mi> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>η</mi> <mo>¯</mo> </mover> <mi mathvariant="normal">I</mi> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>η</mi> <mo>¯</mo> </mover> <mi mathvariant="normal">T</mi> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>η</mi> <mrow> <mi mathvariant="normal">T</mi> <mo>,</mo> <mi>max</mi> </mrow> </msub> </semantics></math> corresponds to the mean displacement unit, motor, inverter, total efficiencies, and maximum total efficiency.</p>
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<p>Efficiency for VDN-L in crane application with interconnection scheme 2 ↔ 1 ↔ 3 ↔ 4, 0 ↔ 3. (<b>A</b>) total efficiency under payload extension, <math display="inline"><semantics> <mrow> <mo>−</mo> <msub> <mover accent="true"> <mi>x</mi> <mo>˙</mo> </mover> <mrow> <mn>1</mn> <mo>,</mo> <mi>max</mi> </mrow> </msub> <mo>=</mo> <msub> <mover accent="true"> <mi>x</mi> <mo>˙</mo> </mover> <mrow> <mn>2</mn> <mo>,</mo> <mi>max</mi> </mrow> </msub> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math> mm/s; (<b>B</b>) total efficiency under payload retraction, <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>x</mi> <mo>˙</mo> </mover> <mrow> <mn>1</mn> <mo>,</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>=</mo> <mo>−</mo> <msub> <mover accent="true"> <mi>x</mi> <mo>˙</mo> </mover> <mrow> <mn>2</mn> <mo>,</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math> mm/s. <math display="inline"><semantics> <msub> <mover accent="true"> <mi>η</mi> <mo>¯</mo> </mover> <mi mathvariant="normal">D</mi> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>η</mi> <mo>¯</mo> </mover> <mi mathvariant="normal">M</mi> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>η</mi> <mo>¯</mo> </mover> <mi mathvariant="normal">I</mi> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>η</mi> <mo>¯</mo> </mover> <mi mathvariant="normal">T</mi> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>η</mi> <mrow> <mi mathvariant="normal">T</mi> <mo>,</mo> <mi>max</mi> </mrow> </msub> </semantics></math> corresponds to the mean displacement unit, motor, inverter, total efficiencies and maximum total efficiency.</p>
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<p>Efficiency for VDN-LS in crane application with interconnection scheme 1 ↔ 23 ↔ 4 and 0 ↔ 23. (<b>A</b>) total efficiency under payload extension, <math display="inline"><semantics> <mrow> <mo>−</mo> <msub> <mover accent="true"> <mi>x</mi> <mo>˙</mo> </mover> <mrow> <mn>1</mn> <mo>,</mo> <mi>max</mi> </mrow> </msub> <mo>=</mo> <msub> <mover accent="true"> <mi>x</mi> <mo>˙</mo> </mover> <mrow> <mn>2</mn> <mo>,</mo> <mi>max</mi> </mrow> </msub> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math> mm/s; (<b>B</b>) total efficiency under payload retraction, <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>x</mi> <mo>˙</mo> </mover> <mrow> <mn>1</mn> <mo>,</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>=</mo> <mo>−</mo> <msub> <mover accent="true"> <mi>x</mi> <mo>˙</mo> </mover> <mrow> <mn>2</mn> <mo>,</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math> mm/s. <math display="inline"><semantics> <msub> <mover accent="true"> <mi>η</mi> <mo>¯</mo> </mover> <mi mathvariant="normal">D</mi> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>η</mi> <mo>¯</mo> </mover> <mi mathvariant="normal">M</mi> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>η</mi> <mo>¯</mo> </mover> <mi mathvariant="normal">I</mi> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>η</mi> <mo>¯</mo> </mover> <mi mathvariant="normal">T</mi> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>η</mi> <mrow> <mi mathvariant="normal">T</mi> <mo>,</mo> <mi>max</mi> </mrow> </msub> </semantics></math> corresponds to the mean displacement unit, motor, inverter, total efficiencies, and maximum total efficiency.</p>
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<p>(<b>A</b>) Reference and actual cylinder positions for VDN-PP case; (<b>B</b>) reference and actual cylinder positions for VDN-L case; (<b>C</b>) reference and actual cylinder positions for the VDN-LS case; (<b>D</b>) chamber pressures for the VDN-PP case; (<b>E</b>) chamber pressures for the VDN-L case; (<b>F</b>) Chamber pressures for the VDN-LS case; (<b>G</b>) VsD shaft speeds for the VDN-PP case; (<b>H</b>) VsD shaft speeds for the VDN-L case; (<b>I</b>) the VsD shaft speeds for the VDN-LS case.</p>
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<p>(<b>A</b>) Input and output power of VDN with point-to-point topology; (<b>B</b>) input and output power of VDN with linear topology; (<b>C</b>) input and output power of VDN with linear topology with shared chambers; (<b>D</b>) VDN core losses; (<b>E</b>) VDN inverter losses; (<b>F</b>) VDN copper losses; (<b>G</b>) VDN friction losses; (<b>H</b>) VDN leakage losses.</p>
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<p>Design pressures over the course of piston strokes for individual chamber topologies. (<b>A</b>) <math display="inline"><semantics> <msub> <mi>p</mi> <mn>1</mn> </msub> </semantics></math>; (<b>B</b>) <math display="inline"><semantics> <msub> <mi>p</mi> <mn>3</mn> </msub> </semantics></math>; (<b>C</b>) <math display="inline"><semantics> <msub> <mi>p</mi> <mn>4</mn> </msub> </semantics></math>. Note; <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mn>2</mn> </msub> <mo>=</mo> <msub> <mi>p</mi> <mi>min</mi> </msub> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math> bar.</p>
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<p>Design pressures over the course of piston strokes for shared chamber topologies. (<b>A</b>–<b>C</b>) <math display="inline"><semantics> <msub> <mi>p</mi> <mn>13</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>p</mi> <mn>2</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>p</mi> <mn>4</mn> </msub> </semantics></math> for the case with chambers 1 and 3 shared; (<b>D</b>–<b>F</b>) <math display="inline"><semantics> <msub> <mi>p</mi> <mn>14</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>p</mi> <mn>2</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>p</mi> <mn>3</mn> </msub> </semantics></math> for the case with chambers 1 and 4 shared; (<b>G</b>–<b>I</b>) <math display="inline"><semantics> <msub> <mi>p</mi> <mn>23</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>p</mi> <mn>1</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>p</mi> <mn>4</mn> </msub> </semantics></math> for the case with chambers 2 and 3 shared; (<b>J</b>–<b>L</b>) <math display="inline"><semantics> <msub> <mi>p</mi> <mn>24</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>p</mi> <mn>1</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>p</mi> <mn>3</mn> </msub> </semantics></math> for the case with chambers 2 and 4 shared.</p>
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27 pages, 5879 KiB  
Article
Effect of Manufacturing Inaccuracies on the Wake Past Asymmetric Airfoil by PIV
by Daniel Duda, Vitalii Yanovych, Volodymyr Tsymbalyuk and Václav Uruba
Energies 2022, 15(3), 1227; https://doi.org/10.3390/en15031227 - 8 Feb 2022
Cited by 13 | Viewed by 2052
Abstract
The effect of manufacturing geometry deviations on the flow past a NACA 64(3)-618 asymmetric airfoil is studied. This airfoil is 3D printed according to the coordinates from a public database. An optical high-precision 3D scanner, GOM Atos, measures the difference from the idealized [...] Read more.
The effect of manufacturing geometry deviations on the flow past a NACA 64(3)-618 asymmetric airfoil is studied. This airfoil is 3D printed according to the coordinates from a public database. An optical high-precision 3D scanner, GOM Atos, measures the difference from the idealized model. Based on this difference, another model is prepared with a physical output closer to the ideal model. The velocity in the near wake (0–0.4 chord) is measured by using the Particle Image Velocimetry (PIV) technique. This work compares the wakes past three airfoil realizations, which differ in their similarity to the original design (none of the realizations is identical to the original design). The chord-based Reynolds number ranges from 1.6×104 to 1.6×105. The ensemble average velocity is used for the determination of the wake width and for the rough estimation of the drag coefficient. The lift coefficient is measured directly by using force balance. We discuss the origin of turbulent kinetic energy in terms of anisotropy (at least in 2D) and the length-scales of fluctuations across the wake. The spatial power spectral density is shown. The autocorrelation function of the cross-stream velocity detects the regime of the von Karmán vortex street at lower velocities. Full article
(This article belongs to the Special Issue Investigation, Optimization, and Discussion of Turbulence)
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Graphical abstract

Graphical abstract
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<p>(<b>a</b>) Comparison of 3D scans of three manufactured realizations of the NACA 64-618 airfoil, which is depicted by a solid black line. The production details are in text. Panels (<b>b</b>) and (<b>c</b>) show the enlarged details of the leading and trailing edge, respectively.</p>
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<p>(<b>a</b>) Position of the area studied by using Particle Image Velocimetry, depicted as a blue square just behind the trailing edge of the airfoil. The PIV area is a square of side <math display="inline"><semantics> <mrow> <mn>32</mn> <mspace width="0.166667em"/> <mi>mm</mi> <mo>=</mo> <mn>0.4</mn> <mi>c</mi> </mrow> </semantics></math>. The estimated thickness of the illuminated plane is <math display="inline"><semantics> <mrow> <mo>∼</mo> <mn>1</mn> <mspace width="0.166667em"/> <mi>mm</mi> </mrow> </semantics></math>. The laser light comes from the counter-stream direction. (<b>b</b>–<b>d</b>) Photograph of the 3D printed realizations of the airfoil. Their trailing parts are blacked in order to suppress the laser reflections.</p>
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<p>The example of instantaneous velocity fields at chord-based Reynolds numbers <math display="inline"><semantics> <mrow> <mn>1.63</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>4</mn> </msup> </mrow> </semantics></math> (<b>a</b>) and <math display="inline"><semantics> <mrow> <mn>6.13</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>4</mn> </msup> </mrow> </semantics></math> (<b>b</b>), both at zero angle of attack. Only every second velocity vector is displayed. The color in the background represents the <span class="html-italic">z</span>-component of vorticity <math display="inline"><semantics> <mrow> <mi>ω</mi> <mo>=</mo> <mo>∇</mo> <mo>×</mo> <mover accent="true"> <mi>u</mi> <mo>→</mo> </mover> </mrow> </semantics></math>. The QR codes link to <a href="http://home.zcu.cz/~dudad/PIV_uplav_3ms.gif" target="_blank">http://home.zcu.cz/~dudad/PIV_uplav_3ms.gif</a> and <a href="http://home.zcu.cz/~dudad/PIV_uplav_12ms.gif" target="_blank">http://home.zcu.cz/~dudad/PIV_uplav_12ms.gif</a> (accessed on 20 July 2021) show the pair of photos used to draw this figure.</p>
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<p>First three columns show the map of average stream-wise velocity <span class="html-italic">u</span> normalized by the reference velocity <math display="inline"><semantics> <msub> <mi>U</mi> <mi>ref</mi> </msub> </semantics></math> past the slightly different samples A, B, and C. The fourth column displays isotachs together. The green tips on the left edge of the figure represent the positions of the airfoil trailing edge. The Reynolds number is based on the chord length and the reference velocity. The reference velocity is measured in the empty wind tunnel under otherwise similar conditions. Angle of attack <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <msup> <mn>0</mn> <mo>∘</mo> </msup> </mrow> </semantics></math> in all panels.</p>
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<p>The cross-stream profile of average stream-wise velocity <math display="inline"><semantics> <mfenced open="&#x2329;" close="&#x232A;"> <mi>u</mi> </mfenced> </semantics></math> at stream-wise distance <math display="inline"><semantics> <mrow> <mn>10</mn> <mspace width="0.166667em"/> <mi>mm</mi> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mn>8</mn> <mi>c</mi> </mrow> </semantics></math> past the trailing edge. Angle of attack <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <msup> <mn>0</mn> <mo>∘</mo> </msup> </mrow> </semantics></math> in all panels. The uncertainty is displayed via the area.</p>
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<p>The ratio of cross-stream fluctuations to stream-wise fluctuations obtained behind three different manufactured samples, denoted A, B, and C. Angle of attack <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <msup> <mn>0</mn> <mo>∘</mo> </msup> </mrow> </semantics></math> in all panels. Redder colors signify a dominance of stream-wise fluctuations, which is typical for areas of continuing boundary layer, while the bluer colors highlight areas of stronger cross-stream fluctuations, which is typical for the so-called <span class="html-italic">true wake</span>.</p>
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<p>The cross-stream profile of average cross-stream velocity <math display="inline"><semantics> <mfenced open="&#x2329;" close="&#x232A;"> <mi>v</mi> </mfenced> </semantics></math> at stream-wise distance <math display="inline"><semantics> <mrow> <mn>10</mn> <mspace width="0.166667em"/> <mi>mm</mi> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mn>8</mn> <mi>c</mi> </mrow> </semantics></math> past the trailing edge. Angle of attack <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <msup> <mn>0</mn> <mo>∘</mo> </msup> </mrow> </semantics></math> in all panels.</p>
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<p>Sketch and photograph of the balance used for lift force measurement. The metallic flexible elements are connected to the wind tunnel walls at their upper end. The airfoil is reinforced by using an aluminum tube, which connects the airfoil to the movable table as well. The eddy-current sensor is used to electrically measure the displacement of the entire table.</p>
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<p>Balance measurement of the lift coefficient. The theoretical results for ideal geometry are displayed as well. Angle of attack <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <msup> <mn>0</mn> <mo>∘</mo> </msup> </mrow> </semantics></math>.</p>
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<p>(<b>a</b>–<b>d</b>) Lines of extrema of <math display="inline"><semantics> <mrow> <mo>〈</mo> <mi>u</mi> <mo>〉</mo> </mrow> </semantics></math> along cross-stream direction <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>y</mi> <mo>)</mo> </mrow> </semantics></math>. The number in the top-right corner of each panel is the chord-based Reynolds number, and k denotes <math display="inline"><semantics> <mrow> <mo>·</mo> <msup> <mn>10</mn> <mn>3</mn> </msup> </mrow> </semantics></math>. (<b>e</b>) Direction of centerline as a function of the Reynolds number. It is obtained as a linear fit of the coordinates of the minima of transverse profiles. Angle of attack <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <msup> <mn>0</mn> <mo>∘</mo> </msup> </mrow> </semantics></math> in all panels.</p>
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<p>Solid lines indicate the wake width as a function of distance past the airfoil trailing edge, <span class="html-italic">x</span>; the corresponding axis is on the left-hand-side of the plots. Dashed lines represent the maximum deficit velocity as a function of <span class="html-italic">x</span>; the axes for these values are on the right-hand-side of the plots. Different panels contain data at different Reynolds numbers and angle of attack <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <msup> <mn>0</mn> <mo>∘</mo> </msup> </mrow> </semantics></math>.</p>
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<p>(<b>Top left</b>) Dependence of the wake width <math display="inline"><semantics> <msub> <mi>δ</mi> <mi>w</mi> </msub> </semantics></math> at a fixed stream-wise distance <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mn>0.4</mn> <mi>c</mi> </mrow> </semantics></math> past the trailing edge on the chord-based Reynolds number. (<b>Top right</b>) Dependence of the velocity deficit at a certain distance <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mn>0.4</mn> <mi>c</mi> </mrow> </semantics></math> on the Reynolds number. (<b>Bottom left</b>) Dependence of the growth rate of the wake width <span class="html-italic">a</span> on the Reynolds number. (<b>Bottom right</b>) Decay rate of velocity deficit, if a hyperbolic decrease is expected. Angle of attack <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <msup> <mn>0</mn> <mo>∘</mo> </msup> </mrow> </semantics></math> in all panels.</p>
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<p>Turbulent kinetic energy (TKE) based on in-plane velocities only (thus, it is thought to be underestimated) obtained behind three different manufactured samples denoted A, B, and C. The scale is adapted to <math display="inline"><semantics> <mrow> <mstyle scriptlevel="0" displaystyle="false"> <mfrac> <mn>1</mn> <mn>10</mn> </mfrac> </mstyle> <msubsup> <mi>U</mi> <mi>ref</mi> <mn>2</mn> </msubsup> </mrow> </semantics></math> except for the last line, where the scale is <math display="inline"><semantics> <mrow> <mstyle scriptlevel="0" displaystyle="false"> <mfrac> <mn>1</mn> <mn>80</mn> </mfrac> </mstyle> <msubsup> <mi>U</mi> <mi>ref</mi> <mn>2</mn> </msubsup> </mrow> </semantics></math>. Angle of attack <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <msup> <mn>0</mn> <mo>∘</mo> </msup> </mrow> </semantics></math> in all panels.</p>
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<p>Turbulent kinetic energy colored by the length-scale of fluctuations producing it at angle of attack <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <msup> <mn>0</mn> <mo>∘</mo> </msup> </mrow> </semantics></math>. TKE is divided into three channels: the red channel represents fluctuations of sizes 0.7–1.0% of chord width; the green channel shows fluctuations of sizes between 2.0% and 2.7% of chord width; while the blue represents the largest fluctuations of sizes 5.3–8.0% of chord width. The color scale for different color channels is normalized in such a way that an ideal Kolmogorov turbulence would be displayed in shades of gray. Among different Re and variants, the color scale is automatically adapted (for differences in amount of TKE, look to <a href="#energies-15-01227-f013" class="html-fig">Figure 13</a> or <a href="#energies-15-01227-f015" class="html-fig">Figure 15</a>).</p>
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<p>The cross-stream profile of the turbulent kinetic energy (TKE) at stream-wise distance <math display="inline"><semantics> <mrow> <mn>10</mn> <mspace width="0.166667em"/> <mi>mm</mi> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mn>8</mn> <mi>c</mi> </mrow> </semantics></math> past the trailing edge. Angle of attack <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <msup> <mn>0</mn> <mo>∘</mo> </msup> </mrow> </semantics></math> in all panels. Note the logarithmic scale.</p>
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<p>Spatial spectra of turbulent kinetic energy. The different products are distinguished by color, the Reynolds number by the line style; the angle of attack is zero in all cases. Thin lines represent scalings <math display="inline"><semantics> <msup> <mi>k</mi> <mrow> <mo>−</mo> <mn>5</mn> <mo>/</mo> <mn>3</mn> </mrow> </msup> </semantics></math>, <math display="inline"><semantics> <msup> <mi>k</mi> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </semantics></math>, and <math display="inline"><semantics> <msup> <mi>k</mi> <mrow> <mo>−</mo> <mn>11</mn> <mo>/</mo> <mn>3</mn> </mrow> </msup> </semantics></math>.</p>
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<p>Autocorrelation function of cross-stream velocity component <span class="html-italic">v</span> with a reference point in the middle of the field of view; i.e., <math display="inline"><semantics> <mrow> <mn>0.21</mn> <mi>c</mi> </mrow> </semantics></math> past the trailing edge at <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <msup> <mn>0</mn> <mo>∘</mo> </msup> </mrow> </semantics></math>.</p>
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<p>The stream-wise profile of the autocorrelation function of the cross-stream velocity component at <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <msup> <mn>0</mn> <mo>∘</mo> </msup> </mrow> </semantics></math>.</p>
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<p>Integral length-scale of cross-stream velocity component <span class="html-italic">v</span>. Panel (<b>a</b>) shows the integral length-scale along the stream-wise axis; however, it is different in upstream and downstream directions, and panel (<b>b</b>) shows this asymmetry. Panel (<b>c</b>) shows the integral length-scale of <span class="html-italic">v</span> along the cross-stream axis. Angle of attack <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <msup> <mn>0</mn> <mo>∘</mo> </msup> </mrow> </semantics></math> in all panels.</p>
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<p>Rough estimation of the drag coefficient based only on the momentum deficit and stress term; the pressure term is ignored. Panel (<b>a</b>) is processed via the methodology of Terra et al. [<a href="#B28-energies-15-01227" class="html-bibr">28</a>], while panel (<b>b</b>) shows the drag coefficient estimated according to Antonia and Rajagopalan [<a href="#B32-energies-15-01227" class="html-bibr">32</a>] based on the same PIV data. Black points denote data obtained from the public database Airfoiltools [<a href="#B3-energies-15-01227" class="html-bibr">3</a>] calculated by using the Xfoil [<a href="#B5-energies-15-01227" class="html-bibr">5</a>,<a href="#B26-energies-15-01227" class="html-bibr">26</a>,<a href="#B27-energies-15-01227" class="html-bibr">27</a>] for the <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mi>crit</mi> </msub> <mo>=</mo> <mn>9</mn> </mrow> </semantics></math>. Angle of attack <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <msup> <mn>0</mn> <mo>∘</mo> </msup> </mrow> </semantics></math> in both panels.</p>
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<p>Map of ensemble average of stream-wise velocity <span class="html-italic">u</span> at angle of attack <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <msup> <mn>10</mn> <mo>∘</mo> </msup> </mrow> </semantics></math>. In respect to <a href="#energies-15-01227-f004" class="html-fig">Figure 4</a>, here is an added row denoted (*) containing data at <math display="inline"><semantics> <mrow> <mi>Re</mi> <mo>=</mo> <mn>1.23</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>5</mn> </msup> </mrow> </semantics></math> in order to show that the flow is adhered at this velocity.</p>
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<p>Lines of extrema of <math display="inline"><semantics> <mrow> <mo>〈</mo> <mi>u</mi> <mo>〉</mo> </mrow> </semantics></math> in cross-stream direction <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>y</mi> <mo>)</mo> </mrow> </semantics></math>. The number in the top-right corner of each panel is the chord-based Reynolds number, and k denotes <math display="inline"><semantics> <mrow> <mo>·</mo> <msup> <mn>10</mn> <mn>3</mn> </msup> </mrow> </semantics></math>. Angle of attack <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <msup> <mn>0</mn> <mo>∘</mo> </msup> </mrow> </semantics></math> in all panels.</p>
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<p>Map of turbulent kinetic energy at angle of attack <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <msup> <mn>10</mn> <mo>∘</mo> </msup> </mrow> </semantics></math>. The first row is added; it is denoted (*) and contains data at <math display="inline"><semantics> <mrow> <mi>Re</mi> <mo>=</mo> <mn>1.63</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>4</mn> </msup> </mrow> </semantics></math> in order to show that the wake past product A at <math display="inline"><semantics> <mrow> <mi>Re</mi> <mo>=</mo> <mn>2.04</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>4</mn> </msup> </mrow> </semantics></math> belongs to the previous regime.</p>
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<p>Turbulent kinetic energy colored by the length-scale of fluctuations producing it. TKE is divided into three channels: the red channel represents the smallest length-scale of fluctuations, the green channel shows fluctuations of middle sizes, and the blue represents the largest fluctuations. The color scale for different color channels is normalized in such a way that an ideal Kolmogorov turbulence would be displayed in shades of gray. Among different Re and variants, the color scale is automatically adapted (for differences in amounts of TKE, look to <a href="#energies-15-01227-f0A3" class="html-fig">Figure A3</a>. Rows denoted by (*) are added.</p>
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<p>Autocorrelation function of fluctuating cross-stream velocity component <span class="html-italic">v</span> at two Reynolds numbers <math display="inline"><semantics> <mrow> <mn>1.23</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>5</mn> </msup> </mrow> </semantics></math> (<b>a</b>) and <math display="inline"><semantics> <mrow> <mn>1.63</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>5</mn> </msup> </mrow> </semantics></math> (<b>b</b>), where the flow is attached at <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <msup> <mn>10</mn> <mo>∘</mo> </msup> </mrow> </semantics></math>. First, the autocorrelation with the reference point in the suction part of the wake (<b>top</b>) and second with the reference point in the pressure part of the wake (<b>bottom</b>).</p>
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<p>Spatial spectrum of the turbulent kinetic energy at <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <msup> <mn>10</mn> <mo>∘</mo> </msup> </mrow> </semantics></math>. Products are distinguished with colors, Reynolds numbers wuith different line styles. The thin black lines represent the <math display="inline"><semantics> <msup> <mi>k</mi> <mrow> <mo>−</mo> <mn>5</mn> <mo>/</mo> <mn>3</mn> </mrow> </msup> </semantics></math> scaling.</p>
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21 pages, 5899 KiB  
Article
A Quantitative Evaluation Method of Anti-Sloughing Drilling Fluid Inhibition for Deep Mudstone
by Kehao Bo, Yan Jin, Yunhu Lu, Hongtao Liu and Jinzhi Zhu
Energies 2022, 15(3), 1226; https://doi.org/10.3390/en15031226 - 8 Feb 2022
Cited by 1 | Viewed by 1880
Abstract
Wellbore instability resulting from deep mudstone hydration severely restricts the development of oil and gas resources from deep reservoir in western China. Accurate evaluation of drilling fluid inhibition properties plays an important role in selecting drilling fluid that can control deep mudstone hydration [...] Read more.
Wellbore instability resulting from deep mudstone hydration severely restricts the development of oil and gas resources from deep reservoir in western China. Accurate evaluation of drilling fluid inhibition properties plays an important role in selecting drilling fluid that can control deep mudstone hydration and then sustain wellbore stability. The previous evaluations are conducted by qualitative analysis and cannot consider the influence of complex hydration conditions of deep mudstone (high temperature, high pressure and flushing action). The study proposes a quantitative method to evaluate drilling fluid’s inhibition property for deep mudstone under natural drilling conditions. In this method, the cohesive strength of mudstone after hydration is adopted as the inhibition index of the tested drilling fluid. An experimental platform containing a newly designed HPHT (High pressure and high temperature) hydration experiment apparatus and mechanics characterization of mudstone after hydration based on scratch test is proposed to obtain the current inhibition index of tested drilling fluid under deep well drilling environments. Based on the mechanical–chemical wellbore stability model considering strength weakening characteristics of deep mudstone after hydration, a cross-correlation between drilling fluid density (collapse pressure) and required inhibition index (cohesive strength) for deep mudstone is provided as the quantitative evaluation criterion. Once the density of tested mud is known, one can confirm whether the inhibition property of tested mud is sufficient. In this study, the JDK mudstone of a K block in western China is selected as the application object of the proposed evaluation method. Firstly, the evaluation chart, which can demonstrate the required inhibition indexes of the tested fluids quantitatively with various densities for JDK mudstone, is constructed. Furthermore, the experimental evaluations of inhibition indexes of drilling fluids taken from two wells in K block are conducted under ambient and deep-well drilling conditions, respectively. In order to show the validity and advantage of the proposed method, a comparison between the laboratory evaluation results and field data is made. Results show that the laboratory evaluation results under deep-well drilling conditions are consistent with the field data. However, the evaluation under ambient conditions overestimates the inhibition property of the tested fluid and brings a risk of wellbore instability. The developed quantitative method can be a new way to evaluate and optimize the inhibition property of drilling fluid for deep mudstone. Full article
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<p>Framework of evaluation method of drilling fluid inhibition for deep mudstone.</p>
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<p>HPHT experimental apparatus for studying rock–fluid interaction (only use hydration experiment section in this study).</p>
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<p>TerraTek continuous scratch test system.</p>
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<p>Detailed procedure of evaluation of the drilling fluid inhibition property for deep mudstone.</p>
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<p>Returned fall-blocks of JDK mudstone in the K block (western China).</p>
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<p>Photos of two samples of JDK mudstones after hydration. (<b>a</b>) 1<sup>#</sup>—After 0 h, (<b>b</b>) 1<sup>#</sup>—After 4 h, (<b>c</b>) 1<sup>#</sup>—After 24 h, (<b>d</b>) 2<sup>#</sup>—After 0 h, (<b>e</b>) 2<sup>#</sup>—After 4 h, (<b>f</b>) 2<sup>#</sup>—After 24 h.</p>
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<p>Hydration characteristics of JDK mudstones. (<b>a</b>) Uptake water content, (<b>b</b>) Uniaxial compressive strength, (<b>c</b>) Internal friction angle, (<b>d</b>) Cohesive strength.</p>
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<p>The weakening equations of mechanical properties of JDK mudstones after hydration. (<b>a</b>) Internal friction angle, (<b>b</b>) Cohesive strength.</p>
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<p>Wellbore collapse regions of JDK mudstones at different hydration levels with drilling fluid density of 1.80 g/cm<sup>3</sup> (<b>a</b>–<b>f</b>), the hydration level of mudstone continues to gradually increase; graph (<b>a</b>) represents the mudstone without hydration; red areas denote wellbore collapse region.</p>
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<p>Wellbore collapse regions of JDK mudstones at different hydration levels with drilling fluid density of 1.80 g/cm<sup>3</sup> (<b>a</b>–<b>f</b>), the hydration level of mudstone continues to gradually increase; graph (<b>a</b>) represents the mudstone without hydration; red areas denote wellbore collapse region.</p>
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<p>The cross-correlation chart of drilling fluid density and matching inhibition index for JDK mudstone.</p>
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<p>JDK mudstone used in inhibition evaluation of drilling fluid in well K 8-13 under deep-well drilling conditions. (<b>a</b>) Before hydration, (<b>b</b>) After hydration.</p>
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<p>JDK mudstone used in inhibition evaluation of drilling fluid in well K 8-13 under ambient conditions. (<b>a</b>) Before hydration, (<b>b</b>) After hydration.</p>
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<p>Inhibition evaluation results of drilling fluid in well K 8-13 under different hydration conditions (1<sup>#</sup>, 2<sup>#</sup> and 3<sup>#</sup> are the inhibition evaluation results of drilling fluid under deep-well drilling conditions; 4<sup>#</sup> and 5<sup>#</sup> are the inhibition evaluation results of drilling fluid under ambient conditions).</p>
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<p>Drilling history of well K 8-13 (the orange area denotes the JDK mudstone formation).</p>
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<p>JDK mudstone used in inhibition evaluation of drilling fluid in well K 9-3 under deep-well drilling conditions. (<b>a</b>) Before hydration, (<b>b</b>) After hydration.</p>
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<p>Inhibition evaluation result of drilling fluid in well K 9-3 under deep-well drilling conditions.</p>
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<p>Drilling history of well K 9-3 (the orange area denotes the JDK mudstone formation).</p>
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18 pages, 2659 KiB  
Article
Analysis and Design of the Energy Storage Requirement of Hybrid Modular Multilevel Converters Using Numerical Integration and Iterative Solution
by Kailun Wang, Qiang Song and Shukai Xu
Energies 2022, 15(3), 1225; https://doi.org/10.3390/en15031225 - 8 Feb 2022
Cited by 4 | Viewed by 1572
Abstract
Increasing the modulation index by utilizing the negative voltage states of full-bridge submodules (FBSMs) can greatly reduce capacitor usage of modular multilevel converters (MMCs), thereby optimizing the cost and volume. The hybrid MMC is composed of half-bridge submodules (HBSMs) and FBSMs, and the [...] Read more.
Increasing the modulation index by utilizing the negative voltage states of full-bridge submodules (FBSMs) can greatly reduce capacitor usage of modular multilevel converters (MMCs), thereby optimizing the cost and volume. The hybrid MMC is composed of half-bridge submodules (HBSMs) and FBSMs, and the capacitor voltages of the two types of submodules (SMs) have different shapes as long as negative voltage states exist. This condition greatly complicates the analysis and design of the energy storage requirement of the hybrid MMC, which utilizes the negative voltage states of FBSMs to boost the AC voltage. A numerical calculation method for solving the capacitor voltages and designing the capacitances of FBSMs and HBSMs is proposed in order to accurately determine the minimum energy storage requirement considering the difference between the energy variations in FBSMs and HBSMs. In the numerical calculation, the energy storage and voltage of the arm are decomposed into FBSM and HBSM parts. According to the physical switching process, the output voltages of FBSM and HBSM parts are determined separately. The one-cycle waveforms of the capacitor voltages are then obtained by numerical integration of the power flows in FBSM and HBSM parts. An iterative solution procedure and the termination criterion that can ensure the accuracy of the obtained one-cycle waveforms are also proposed. Using the numerical integration and iterative solution procedure as the kernel algorithm, the proposed method can accurately analyze the capacitor voltages of the FBSMs and HBSMs and determine the minimum energy storage requirement of the hybrid MMC. Furthermore, the proposed method is applicable for various operating working conditions and various proportions of FBSMs. The simulation results verify the feasibility and accuracy of the analysis and design method. Full article
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<p>Illustration of the hybrid MMC.</p>
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<p>Equivalent circuit of the MMC connected to the AC grid.</p>
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<p>Relation between the number of FBSMs and the arm voltage: (<b>a</b>) HB-MMC; (<b>b</b>) hybrid MMC.</p>
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<p><span class="html-italic">P</span>/<span class="html-italic">Q</span> operating region of MMC considering maximum reactive power requirement.</p>
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<p>Flowchart of the sorting algorithm.</p>
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<p>Illustration of capacitor voltage variation process: (<b>a</b>) capacitor voltages; (<b>b</b>) arm voltage; (<b>c</b>) arm current; (<b>d</b>) input submodule number.</p>
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<p>Computing process of capacitor voltages.</p>
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<p>Computing process of capacitor usage and FBSM quantity.</p>
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<p>Schematic of the main circuit of the case study.</p>
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<p>Simulation and calculation waveform of capacitor voltages in different operating points: (<b>a</b>) pure active output (inverting) (φ = 0); (<b>b</b>) pure active input (rectifying) (φ = π); (<b>c</b>) pure inductive reactive power (φ = −π/2); (<b>d</b>) pure capacitive reactive power (φ = π/2).</p>
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<p>Capacitor usage of hybrid MMC varying with <span class="html-italic">M</span><sub>0</sub> and <span class="html-italic">Q</span><sub>max</sub>.</p>
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20 pages, 4456 KiB  
Article
A Model of DC-DC Converter with Switched-Capacitor Structure for Electric Vehicle Applications
by Filippo Pellitteri, Vincenzo Di Dio, Christian Puccio and Rosario Miceli
Energies 2022, 15(3), 1224; https://doi.org/10.3390/en15031224 - 8 Feb 2022
Cited by 13 | Viewed by 3048
Abstract
In this paper, a DC-DC converter with an innovative topology for automotive applications is proposed. The goal of the presented power converter is the electrical storage system management of an electric vehicle (EV). The presented converter is specifically compliant with a 400 V [...] Read more.
In this paper, a DC-DC converter with an innovative topology for automotive applications is proposed. The goal of the presented power converter is the electrical storage system management of an electric vehicle (EV). The presented converter is specifically compliant with a 400 V battery, which represents the high-voltage primary source of the system. This topology is also able to act as a bidirectional power converter, so that in this case, the output section is an active stage, which is able to provide power as, for example, in the case of a low-voltage battery or a supercapacitor. The proposed topology can behave either in step-down or in step-up mode, presenting in both cases a high gain between the input and output voltage. Simulation results concerning the proposed converter, demonstrating the early feasibility of the system, were obtained in a PowerSIM environment and are described in this paper. Full article
(This article belongs to the Special Issue Power Converters Design, Control and Applications)
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<p>Schematic of the proposed switched-capacitor converter.</p>
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<p>The proposed converter in step-down operation: Mode 1.</p>
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<p>The proposed converter in step-down operation: Mode 2.</p>
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<p>Schematic of the conventional buck converter.</p>
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<p>Gain vs. duty cycle for conventional and proposed step-down converter.</p>
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<p>The proposed converter in step-up operation: Mode 1.</p>
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<p>The proposed converter in step-up operation: Mode 2.</p>
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<p>Schematic of the conventional boost converter.</p>
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<p>Gain vs. duty cycle for conventional and proposed step-up converter.</p>
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<p>The main current waveforms of the proposed converter during the switching period <span class="html-italic">T<sub>s</sub></span>.</p>
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<p>The main voltage waveforms of the proposed converter during the switching period <span class="html-italic">T<sub>s</sub></span>.</p>
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<p>Current ripple on <span class="html-italic">L</span>1 and <span class="html-italic">L</span>2 as function of the duty cycle <span class="html-italic">D</span>.</p>
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<p>Gate signals, output voltage, and inductor current waveforms of the proposed converter for case (a) at maximum achieved gain: <span class="html-italic">V<sub>LV</sub></span> = 48 V; <span class="html-italic">V<sub>HV</sub></span> = 528 V; <span class="html-italic">D</span> = 0.1.</p>
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<p>Gate signal, output voltage, and inductor current waveforms of the conventional boost converter for case (a) at maximum achieved gain: <span class="html-italic">V<sub>in</sub></span> = 48 V; <span class="html-italic">V<sub>out</sub></span> = 297 V; <span class="html-italic">D</span> = 0.85.</p>
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<p>Gate signals, output voltage, and inductor current waveforms of the proposed converter for case (b) at maximum achieved gain: <span class="html-italic">V<sub>LV</sub></span> = 48 V; <span class="html-italic">V<sub>HV</sub></span> = 170 V; <span class="html-italic">D</span> = 0.25.</p>
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<p>Gate signal, output voltage, and inductor current waveforms of the conventional boost converter for case (b) at maximum achieved gain: <span class="html-italic">V<sub>in</sub></span> = 48 V; <span class="html-italic">V<sub>out</sub></span> = 165 V; <span class="html-italic">D</span> = 0.8.</p>
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<p>Gain vs. duty cycle for conventional and proposed step-up converter for parasitic effects (a).</p>
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<p>Gain vs. duty cycle for conventional and proposed step-up converter for parasitic effects (b).</p>
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<p>Transferred power vs. duty cycle for conventional and proposed step-up converter for parasitic effects (a).</p>
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16 pages, 3332 KiB  
Article
Hosting Capacity Calculation Deploying a Hybrid Methodology: A Case Study Concerning the Intermittent Nature of Photovoltaic Distributed Generation and the Variable Nature of Energy Consumption in a Medium Voltage Distribution Network
by Ezequiel Junio Lima and Luiz Carlos Gomes Freitas
Energies 2022, 15(3), 1223; https://doi.org/10.3390/en15031223 - 8 Feb 2022
Cited by 2 | Viewed by 2412
Abstract
The main methods employed for Hosting Capacity (HC) calculations are basically classified into deterministic, stochastic and time series. In this scenario, the authors herein propose a hybrid methodology, which shows efficiency and ease of implementation. Besides the method presented, it is also calculated [...] Read more.
The main methods employed for Hosting Capacity (HC) calculations are basically classified into deterministic, stochastic and time series. In this scenario, the authors herein propose a hybrid methodology, which shows efficiency and ease of implementation. Besides the method presented, it is also calculated a hosting capacity of a real feeder which was modeled and analyzed taking into consideration variations in load and power injected by distributed generation sources. The proposed hybrid method deploys just one time series with the feeder power demand data, which are easily obtained from the feeder’s origin substation. Low voltage loads were modeled by the ratio between their maximum demands and the feeder maximum demand, making easier to start up the grid model implementation. Hence, the advantages of the proposed methodology can be summarized in: (a) easy to obtain the input parameters; (b) agility in implementing the study; (c) higher processing speed and (d) results consistent with the time series method. Finally, in view of the advantages and obtained results, the proposed hybrid methodology shows itself as a promising and attractive tool for the studies of hosting capacity by the utilities. Full article
(This article belongs to the Topic Power Distribution Systems)
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<p>Feeder 19 single line diagram.</p>
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<p>Google Earth view of Feeder 19 and its loads groups.</p>
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<p>Average daily power demands between January and December 2019 and associated standard deviation of Feeder 19.</p>
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<p>Average hourly production for (<b>a</b>) January, (<b>b</b>) March, (<b>c</b>) July and (<b>d</b>) November of 2019 from the PVP at the IFSULDEMINAS Campus Poços de Caldas. Noteworthy is the monthly hourly average and the maximum and minimum reported values over the period.</p>
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<p>Voltages at bus 3 throughout the day 17th of July 2019: Comparison between measured and simulated values.</p>
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<p>The identification of the scenario of interest is possible when overlapping and comparing the PV generation and the feeder loading charts.</p>
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<p>Simplified flowchart of the Hosting Capacity calculation.</p>
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<p>Case 00: Voltages in buses 6, 5, 3 and 1. The curves are given by the time series method and the dots, by the proposed hybrid method.</p>
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<p>Case 01: PVP installed on bus 6 (indicated by the solid line in the graph). The curves are given by the time series method and the dots, by the proposed hybrid method.</p>
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<p>Case 02: PVP installed on bus 5 (indicated by the solid line in the graph). The curves are given by the time series method and the dots, by the proposed hybrid method.</p>
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<p>Case 03: PVP installed on bus 3 (indicated by the solid line in the graph). The curves are given by the time series method and the dots, by the proposed hybrid method.</p>
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<p>Case 04: PVP installed on bus 1 (indicated by the solid line in the graph). The curves are given by the time series method and the dots, by the proposed hybrid method.</p>
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17 pages, 11231 KiB  
Article
An Improved Finite Control Set Model Predictive Current Control for a Two-Phase Hybrid Stepper Motor Fed by a Three-Phase VSI
by Chunlei Wang, Dongxing Cao, Xiangxu Qu and Chen Fan
Energies 2022, 15(3), 1222; https://doi.org/10.3390/en15031222 - 8 Feb 2022
Cited by 4 | Viewed by 1895
Abstract
In this paper, an improved finite control set model predictive current control (FCS-MPCC) is proposed for a two-phase hybrid stepper motor fed by a three-phase voltage source inverter (VSI). The conventional FCS-MPCC selects an optimal voltage vector (VV) from six active and one [...] Read more.
In this paper, an improved finite control set model predictive current control (FCS-MPCC) is proposed for a two-phase hybrid stepper motor fed by a three-phase voltage source inverter (VSI). The conventional FCS-MPCC selects an optimal voltage vector (VV) from six active and one null VVs by evaluating a simple cost function and then applies the optimal VV directly to the VSI. Though the implementation is simple, it features a large current ripple and total harmonic distortion (THD). The proposed improved FCS-MPCC builds an extended control set consisting of 37 VVs to replace the original control set with only seven VVs. The increase in the amount of VVs helps to regulate the current more accurately. In each control period, the improved FCS-MPCC takes advantage of deadbeat control to calculate a reference VV, and only the three VVs adjacent to the reference VV are predicted and evaluated, which decrease the computational workload significantly. Build waveform patterns for all VVs in the unbalanced circuit structure to modulate the optimal VV using discrete space vector modulation, which improves the current quality in reducing current ripple and THD. The comparative simulations and experimental results validate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue High Performance Permanent Magnet Synchronous Motor Drives)
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Figure 1

Figure 1
<p>Three-phase VSI fed two-phase HSM.</p>
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<p>Six sectors and eight VVs in the stationary α-β frame.</p>
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<p>Block diagram of the conventional FCS-MPCC.</p>
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<p>Real and virtual VVs in α-β frame.</p>
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<p>Generalized waveform pattern for VVs in sector Ⅰ.</p>
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<p>Instantiated waveform patterns for each VV in sector Ⅰ. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">V</mi> <mn>0</mn> </msub> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">V</mi> <mrow> <msup> <mstyle mathvariant="bold" mathsize="normal"> <mi>N</mi> </mstyle> <mn>2</mn> </msup> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">V</mi> <mrow> <mstyle mathvariant="bold" mathsize="normal"> <mi>N</mi> </mstyle> <msup> <mn>1</mn> <mn>2</mn> </msup> </mrow> </msub> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">V</mi> <mn>1</mn> </msub> </mrow> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">V</mi> <mrow> <mstyle mathvariant="bold" mathsize="normal"> <mi>N</mi> </mstyle> <mn>12</mn> </mrow> </msub> </mrow> </semantics></math>; (<b>f</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">V</mi> <mrow> <msup> <mn>1</mn> <mn>2</mn> </msup> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>; (<b>g</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">V</mi> <mrow> <msup> <mrow> <mn>12</mn> </mrow> <mn>2</mn> </msup> </mrow> </msub> </mrow> </semantics></math>; (<b>h</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">V</mi> <mn>2</mn> </msub> </mrow> </semantics></math>.</p>
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<p>Generalized waveform patterns in all six sectors.</p>
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<p>Block diagram of the improved FCS-MPCC.</p>
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<p>Speed, torque, and current responses of the HSM without load torque disturbance. (<b>a</b>) Speed response; (<b>b</b>) torque response; (<b>c</b>) current response of conventional FCS-MPCC in α-β frame; (<b>d</b>) current response of improved FCS-MPCC in α-β frame; (<b>e</b>) current response of dual H-bridges PI in α-β frame.</p>
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<p>Speed, torque, and current response of the HSM with load torque disturbance. (<b>a</b>) Speed response; (<b>b</b>) torque response; (<b>c</b>) current response of conventional FCS-MPCC in d-q frame; (<b>d</b>) current response of improved FCS-MPCC in d-q frame; (<b>e</b>) current response of dual H-ridges PI in d-q frame.</p>
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<p>THD comparison at various speeds for all methods.</p>
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<p>Experimental setup.</p>
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<p>Speed and current responses of HSM under load. (<b>a</b>) Speed response; (b) current response of conventional FCS-MPCC in d-q frame; (<b>c</b>) current response of improved FCS-MPCC in d-q frame; (<b>d</b>) current response of the dual H-bridges PI in d-q frame; (<b>e</b>) current response of conventional FCS-MPCC in α-β frame; (<b>f</b>) Current response of improved FCS-MPCC in α-β frame; (<b>g</b>) current response of the dual H-bridges PI in α-β frame.</p>
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<p>Speed and current responses of HSM under load. (<b>a</b>) Speed response; (b) current response of conventional FCS-MPCC in d-q frame; (<b>c</b>) current response of improved FCS-MPCC in d-q frame; (<b>d</b>) current response of the dual H-bridges PI in d-q frame; (<b>e</b>) current response of conventional FCS-MPCC in α-β frame; (<b>f</b>) Current response of improved FCS-MPCC in α-β frame; (<b>g</b>) current response of the dual H-bridges PI in α-β frame.</p>
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