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Aerospace
Volume 8
Issue 12
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Aerospace, Volume 8, Issue 12 (December 2021) – 45 articles

Cover Story (view full-size image): In this study, we compare noise exposure measurements with calculations of several thousand single flights at the Zurich and Geneva airports, Switzerland, of three aircraft noise calculation programs: sonAIR, a next-generation aircraft noise calculation program, and two best-practice programs, FLULA2 and AEDT. Overall, all three programs show good results, with mean differences between calculations and measurements smaller than ±0.5 dB. sonAIR performs clearly better than the two best-practice programs if additional flight parameter data are available. However, in situations without these data, sonAIR performs similarly well to FLULA2 and AEDT. In conclusion, all three programs are well suited to determine averaged noise metrics resulting from scenarios consisting of many flights, while sonAIR is additionally capable of accurately simulating single flights in greater detail. View this paper.
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16 pages, 4215 KiB  
Article
Multi-Mode Interferometry: Application to TiO2–SiO2 Sol-Gel Waveguide-Based Sensing in the Aerospace Domain
by Maxime Royon, Thomas Blanchet, Muhammad Adnan, Damien Jamon, François Royer, Francis Vocanson, Emmanuel Marin, Adriana Morana, Aziz Boukenter, Youcef Ouerdane, Yves Jourlin, Rolf Evenblij, Thijs Van Leest, Aditya Wankhade, Marie-Anne De Smet, Kathryn Atherton and Sylvain Girard
Aerospace 2021, 8(12), 401; https://doi.org/10.3390/aerospace8120401 - 18 Dec 2021
Cited by 1 | Viewed by 2644
Abstract
The optimization of a 2D optical sensor based on TiO2–SiO2 sol-gel waveguides for damage detection in the aerospace domain was performed in the framework of the ADD-ON European project. The sensor is based on the transportation of visible light along [...] Read more.
The optimization of a 2D optical sensor based on TiO2–SiO2 sol-gel waveguides for damage detection in the aerospace domain was performed in the framework of the ADD-ON European project. The sensor is based on the transportation of visible light along numerous waveguides, and damage is detected and localized through the monitoring of the output light from the waveguide grid. In this work, we have developed an architecture, inspired by a multi-mode interferometer (MMI), allowing us to efficiently multiply the number of waveguides that can be probed by a single optical source. For this, the beam propagation method (BPM) was used to model a rectangular MMI coupler (40 × 5624 µm2) operating in the visible region (600 nm), ensuring the propagation of light into three waveguides. The conceived device was then manufactured by UV photolithography (direct laser writing technique). The simulations and experimental results show that light transport into this architecture allows for the successful simultaneous probing of three waveguides. By complexifying the device structure, successful MMI couplers were easily manufactured, allowing us to probe 9, 15, or 45 TiO2–SiO2 waveguides with a unique light source. Finally, a further investigation regarding 24 consecutive thermal cycles from −40 °C to 60 °C, representative of the temperature changes during aircraft cruising, was performed. This study reveals that TiO2–SiO2 sol-gel waveguides are not mechanically damaged by temperature changes, while the light guidance remains unaffected, confirming that this sensor is very promising for aerospace applications. Since a single source can monitor several guides, the production of more compact, low-cost, and less intrusive sensors can be achieved by fulfilling structural health monitoring requirements. Full article
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<p>Two-dimensional sol-gel matrix sensor for damage or delamination detection. (<b>a</b>) Sensor principle where damage leads to the rupture of one or several TiO<sub>2</sub>–SiO<sub>2</sub> guides. Its localization can be deduced thanks to the 2D structure. (<b>b</b>) Example of a 2D grid waveguide obtained on soda-lime substrate glass. (<b>c</b>) TiO<sub>2</sub>–SiO<sub>2</sub> grid observed with an optical microscope. This figure is adapted from [<a href="#B10-aerospace-08-00401" class="html-bibr">10</a>].</p> Full article ">Figure 2
<p>Setup used for the light injection (top-view). An SMF28 fiber is placed in a V-groove platform and fixed with magnets. The light injection, performed through a butt-coupling configuration, is optimized by moving the fiber in the X, Y, and Z directions. A camera (not shown here) records the top-view (XZ-plane) for optimal light injection while a second camera (on the right) is used for imaging the output light of the TiO<sub>2</sub>–SiO<sub>2</sub> sol-gel waveguides.</p> Full article ">Figure 3
<p>Schematic of the different designs computed with the BPM. (<b>a</b>) YZ-plane representing the TiO<sub>2</sub>–SiO<sub>2</sub> waveguide (n = 1.58) deposited on soda-lime glass (n = 1.52) and surrounded by air (n = 1). (<b>b</b>) Corresponding XZ-plane (top-view). (<b>c</b>) Modeling of the 1 × 3 MMI coupler (top-view) with a fixed width of 40 µm. In any case, all simulations were performed at 600 nm. The laser is injected through a butt-coupling approach using an SMF28 optical fiber.</p> Full article ">Figure 4
<p>Microscope observation (top view) obtained in reflection mode (×63) for (<b>a</b>) a waveguide written using a low scanning speed (lateral dimension = 20 µm) and (<b>b</b>) a higher scanning speed (lateral dimension = 10 µm). (<b>c</b>) Corresponding profilometer measurements. Output light distribution at 638 nm for the (<b>d</b>) 20 µm and (<b>e</b>) 10 µm waveguides, respectively. In any case, the blue line is related to the 20 µm waveguide in width while the red color refers to the 10 µm one.</p> Full article ">Figure 5
<p>Spatial distribution in the XY-plane of the output light intensity from a waveguide (4 µm × 8 µm) at 600 nm under excitation with an SMF28 (butt-coupling configuration) for different X-axis misalignments of: (<b>a</b>) 0 µm (perfect case), (<b>b</b>) 1 µm, (<b>c</b>) 4 µm, and (<b>d</b>) 8 µm.</p> Full article ">Figure 6
<p>BPM simulation (at 600 nm) of the light field propagation along a strongly multi-mode waveguide (width: 40 µm) in the XZ-plane. Self-imaging can be observed for one or three images at periodic intervals along the Z-axis. The waveguide is excited by an SMF28 optical fiber (left) through a butt-coupling configuration.</p> Full article ">Figure 7
<p>(<b>a</b>) BPM simulation (at 600 nm) of light field propagation along a 40 µm × 5624 µm MMI coupler, allowing for the simultaneous probing of 3 waveguides with an SMF28 (XZ-plane). (<b>b</b>) Corresponding output light distribution (XY-plane) where the distance between two consecutive waveguides is 12.5 µm.</p> Full article ">Figure 8
<p>Influence of the filling factor (FF), ranging from 0% to 75% on the shape of TiO<sub>2</sub>–SiO<sub>2</sub> MMI-like structures (square shape and three waveguides with a 6 µm lateral dimension). (<b>a</b>) Microscope images (× 40) of an MMI-like structure obtained with an FF of 0%, (<b>b</b>) corresponding profile, (<b>c</b>) MMI at an FF of 50%, (<b>d</b>) corresponding profile, (<b>e</b>) MMI at an FF of 75%, and (<b>f</b>) corresponding profile.</p> Full article ">Figure 9
<p>(<b>a</b>) Microscopic observation of a 1 × 3 MMI coupler obtained by micro-structuration of a TiO<sub>2</sub>–SiO<sub>2</sub> layer. (<b>b</b>) Output light intensity distribution under excitation at 638 nm.</p> Full article ">Figure 10
<p>(<b>a</b>) Overview of the photonic device composed of 4 MMI couplers (1 × 3): one source can monitor 9 waveguides. (<b>b</b>) Microscopic images of the device, showing the end of the first MMI coupler (left), the transition from waveguides to 3 MMI couplers (center), and the 9 output waveguides (right). (<b>c</b>) Corresponding output light distribution under excitation at 638 nm.</p> Full article ">Figure 11
<p>A 1 × 15 MMI sol-gel coupler obtained by direct laser writing technique. (<b>a</b>) Overview of the splitter. (<b>b</b>) Corresponding microscopic image and (<b>c</b>) output light at 638 nm under excitation with an SMF28. The coupler corresponds to a 200 µm × 3720 µm rectangular structure.</p> Full article ">Figure 12
<p>A 1 × 45 MMI coupler. (<b>a</b>) Overview of the MMI. (<b>b</b>) Output light distribution under excitation at 638 nm.</p> Full article ">Figure 13
<p>Description of the thermal cycle performed on TiO<sub>2</sub>–SiO<sub>2</sub> architectures between two extreme temperatures: 60 °C and −40 °C. Each temperature level lasts 1 h, while a ramp of 1 °C/min is applied between two consecutive values.</p> Full article ">Figure 14
<p>Influence of 24 thermal cycles on TiO<sub>2</sub>–SiO<sub>2</sub> sol-gel waveguides. Microscope observations of a waveguide grid (<b>a</b>) before the thermal cycle and (<b>b</b>) after 24 cycles. (<b>c</b>) Output light observations after injection of white light (supercontinuum) in straight waveguides after several thermal cycles.</p> Full article ">
14 pages, 7558 KiB  
Article
Optimal Geno-Fuzzy Lateral Control of Powered Parachute Flying Vehicles
by Hanafy M. Omar
Aerospace 2021, 8(12), 400; https://doi.org/10.3390/aerospace8120400 - 17 Dec 2021
Cited by 7 | Viewed by 2790
Abstract
In this work, we propose a systematic procedure to design a fuzzy logic controller (FLC) to control the lateral motion of powered parachute (PPC) flying vehicles. The design process does not require knowing the details of vehicle dynamics. Moreover, the physical constraints of [...] Read more.
In this work, we propose a systematic procedure to design a fuzzy logic controller (FLC) to control the lateral motion of powered parachute (PPC) flying vehicles. The design process does not require knowing the details of vehicle dynamics. Moreover, the physical constraints of the system, such as the maximum error of the yaw angle and the maximum allowed steering angle, are naturally included in the designed controller. The effectiveness of the proposed controller was assessed using the nonlinear six degrees of freedom (6DOF) mathematical model of the PPC. The genetic algorithm (GA) optimization technique was used to optimize the distribution of the fuzzy membership functions in order to improve the performance of the suggested controller. The robustness of the proposed controller was evaluated by changing the values of the parafoil aerodynamic coefficients and the initial flight conditions. Full article
(This article belongs to the Section Aeronautics)
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<p>Powered parachute flying vehicle.</p> Full article ">Figure 2
<p>PPC coordinate system.</p> Full article ">Figure 3
<p>Guidance and control loop.</p> Full article ">Figure 4
<p>Look-ahead guidance algorithm.</p> Full article ">Figure 5
<p>Structure of the PPC lateral FLC.</p> Full article ">Figure 6
<p>The normalized membership functions for the FLC inputs and output.</p> Full article ">Figure 7
<p>Equilibrium operating points.</p> Full article ">Figure 8
<p>PPC lateral path using the non-optimized FLC.</p> Full article ">Figure 9
<p>Yaw angle error using the non-optimized FLC.</p> Full article ">Figure 10
<p>Steering angle using the non-optimized FLC.</p> Full article ">Figure 11
<p>Inertial position using the non-optimized FLC.</p> Full article ">Figure 12
<p>Attitude angles using the non-optimized FLC.</p> Full article ">Figure 13
<p>GA algorithm flow chart.</p> Full article ">Figure 14
<p>GA evolution.</p> Full article ">Figure 15
<p>Optimized distribution of MFs.</p> Full article ">Figure 16
<p>Lateral path using the optimized FLC.</p> Full article ">Figure 17
<p>Effect of changing the aerodynamics coefficients.</p> Full article ">Figure 18
<p>Effect of changing the initial flight direction.</p> Full article ">
12 pages, 3397 KiB  
Article
Thermoplastic Mandrel for Manufacturing Composite Components with Complex Structure
by Xishuang Jing, Siyu Chen, Jiuzhi An, Chengyang Zhang and Fubao Xie
Aerospace 2021, 8(12), 399; https://doi.org/10.3390/aerospace8120399 - 16 Dec 2021
Cited by 2 | Viewed by 3894
Abstract
This study was to solve the mandrel demolding problem after curing the composite component with complex structure. In this paper, a reusable thermoplastic mandrel with heating softening characteristics was developed by resin transfer molding (RTM). The glass transition temperature (Tg), surface roughness, and [...] Read more.
This study was to solve the mandrel demolding problem after curing the composite component with complex structure. In this paper, a reusable thermoplastic mandrel with heating softening characteristics was developed by resin transfer molding (RTM). The glass transition temperature (Tg), surface roughness, and reusability of the mandrel, as well as the shape, surface roughness, thickness uniformity, and internal quality of the formed structure, were tested. The result showed that the Tg of the mandrel was between 80 and 90 °C and the surface roughness was less than Ra 0.5 μm. Additionally, the mandrel can be recycled and can still maintain a good shape after 20 times of deformation. By using this method, the demolding process can be realized by heating and softening the mandrel. The profile error of the formed structure was within 0.5 mm, the surface roughness was less than Ra 0.5 μm, the thickness error was within 0.2 mm, and the average porosity of the upper and lower halves of composite parts was 0.72% and 0.61%. All those data showed that the formed part was in good shape and of good quality. The thermoplastic mandrel can solve the demolding problem of composite materials with complex shapes. Full article
(This article belongs to the Special Issue Advanced Aerospace Composite Materials)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Demolding principle of the thermoplastic mandrel.</p> Full article ">Figure 2
<p>RTM process for manufacturing mandrel: (<b>a</b>) The flow chart of mandrel preparation; (<b>b</b>) Mandrel prepared by RTM process.</p> Full article ">Figure 3
<p>The manufacturing process by vacuum bag technique: (<b>a</b>) Schematic diagram of vacuum bag curing process; (<b>b</b>) The manufacturing process of composite parts; (<b>c</b>) the illustration of air pressure balance during part forming; (<b>d</b>) Formed composite parts.</p> Full article ">Figure 4
<p>DMA test results of mandrel material.</p> Full article ">Figure 5
<p>Roughness measurement process of each area of mandrel surface.</p> Full article ">Figure 6
<p>Shape of mandrel after deformations: (<b>a</b>) Mandrel after the first deformation; (<b>b</b>) Mandrel after the 10th deformation; (<b>c</b>) Mandrel after the 20th deformation.</p> Full article ">Figure 7
<p>Parts formed for the first time by the mandrel and parts prepared after 20 times of mandrel deformation.</p> Full article ">Figure 8
<p>Comparison results between composite parts and design digital simulation: (<b>a</b>) Composite part first formed by mandrel; (<b>b</b>) Composite part prepared after 20 times of mandrel deformation.</p> Full article ">Figure 9
<p>Manufacturing error of parts: (<b>a</b>) Error distribution diagram of digital analog comparison between parts and design; (<b>b</b>) error statistics diagram of digital analog comparison between parts and design.</p> Full article ">Figure 10
<p>The distribution of internal pores in the composite part measured by industrial CT: (<b>a</b>) The upper parts; (<b>b</b>) The lower parts.</p> Full article ">
34 pages, 5525 KiB  
Article
Multi-Fidelity Optimization of a Composite Airliner Wing Subject to Structural and Aeroelastic Constraints
by Angelos Kafkas, Spyridon Kilimtzidis, Athanasios Kotzakolios, Vassilis Kostopoulos and George Lampeas
Aerospace 2021, 8(12), 398; https://doi.org/10.3390/aerospace8120398 - 15 Dec 2021
Cited by 10 | Viewed by 3683
Abstract
Efficient optimization is a prerequisite to realize the full potential of an aeronautical structure. The success of an optimization framework is predominately influenced by the ability to capture all relevant physics. Furthermore, high computational efficiency allows a greater number of runs during the [...] Read more.
Efficient optimization is a prerequisite to realize the full potential of an aeronautical structure. The success of an optimization framework is predominately influenced by the ability to capture all relevant physics. Furthermore, high computational efficiency allows a greater number of runs during the design optimization process to support decision-making. The efficiency can be improved by the selection of highly optimized algorithms and by reducing the dimensionality of the optimization problem by formulating it using a finite number of significant parameters. A plethora of variable-fidelity tools, dictated by each design stage, are commonly used, ranging from costly high-fidelity to low-cost, low-fidelity methods. Unfortunately, despite rapid solution times, an optimization framework utilizing low-fidelity tools does not necessarily capture the physical problem accurately. At the same time, high-fidelity solution methods incur a very high computational cost. Aiming to bridge the gap and combine the best of both worlds, a multi-fidelity optimization framework was constructed in this research paper. In our approach, the low-fidelity modules and especially the equivalent-plate methodology structural representation, capable of drastically reducing the associated computational time, form the backbone of the optimization framework and a MIDACO optimizer is tasked with providing an initial optimized design. The higher fidelity modules are then employed to explore possible further gains in performance. The developed framework was applied to a benchmark airliner wing. As demonstrated, reasonable mass reduction was obtained for a current state of the art configuration. Full article
(This article belongs to the Special Issue 11th EASN International Conference on Innovation in Aviation & Space to the Satisfaction of the European Citizens)
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Figure 1
<p>Wing cross-section.</p> Full article ">Figure 2
<p>Optimization zones.</p> Full article ">Figure 3
<p>Low-Fidelity Module Optimization Flowchart (<a href="#aerospace-08-00398-f0A4" class="html-fig">Figure A4</a>).</p> Full article ">Figure 4
<p>Finite Volume C-Grid Mesh for the coarse density (<a href="#aerospace-08-00398-t0A4" class="html-table">Table A4</a>).</p> Full article ">Figure 5
<p>Finite Element Structural Mesh.</p> Full article ">Figure 6
<p>Transfer of pressure loads upon a common boundary with GGI.</p> Full article ">Figure 7
<p>Steady Airloads High-Fidelity Module (<a href="#aerospace-08-00398-f0A4" class="html-fig">Figure A4</a>).</p> Full article ">Figure 8
<p>Staggered coupling scheme.</p> Full article ">Figure 9
<p>Aeroelastic reduced order model.</p> Full article ">Figure 10
<p>Implementation of aeroelasticity using order reduction to the optimization framework (<a href="#aerospace-08-00398-f0A4" class="html-fig">Figure A4</a>).</p> Full article ">Figure 11
<p>Stepped cantilever beam benchmark problem.</p> Full article ">Figure 12
<p>Stepped cantilever beam benchmark problem.</p> Full article ">Figure 13
<p>Stepped cantilever beam benchmark problem.</p> Full article ">Figure 14
<p>CRM wing OML and internal structure.</p> Full article ">Figure 15
<p>Wing mass convergence.</p> Full article ">Figure 16
<p>Constraint satisfaction, expressed via the L-1 norm.</p> Full article ">Figure 17
<p>CRM wing components optimal thickness distribution.</p> Full article ">Figure 18
<p>CRM wing components optimal spanwise thickness distribution.</p> Full article ">Figure 19
<p>CRM wing components spanwise layups percentages.</p> Full article ">Figure 20
<p>Deflection to static aerodynamic RANS load. EPM (<b>left</b>); 3D Shell Model (<b>right</b>).</p> Full article ">Figure 21
<p>Comparison of static deflection distribution versus aeroelastic solution (<span class="html-italic">z</span>-axis scale not actual). Note that a higher angle of attack was required for the aeroelastic solution to attain the same coefficient of lift. Only deformation is shown in the figure; angle of attack is not plotted.</p> Full article ">Figure A1
<p>FEA &amp; EPM mesh.</p> Full article ">Figure A2
<p>Eigenmodes comparison—Modes 1–3.</p> Full article ">Figure A3
<p>Eigenmodes comparison—Modes 4–6.</p> Full article ">Figure A4
<p>Flowchart of the optimization framework.</p> Full article ">Figure A5
<p>Action list of the developed optimization framework for the CRM case.</p> Full article ">
19 pages, 2321 KiB  
Article
A System Dynamics Prediction Model of Airport Environmental Carrying Capacity: Airport Development Mode Planning and Case Study
by Qiuping Peng, Lili Wan, Tianci Zhang, Zhan Wang and Yong Tian
Aerospace 2021, 8(12), 397; https://doi.org/10.3390/aerospace8120397 - 14 Dec 2021
Cited by 7 | Viewed by 3019
Abstract
Airport environmental carrying capacity (AECC) provides the fundamental conditions for airport development and operation activities. The prediction of AECC is a necessary condition for planning an appropriate development mode for the airport. This paper studies the dynamic prediction method of the AECC to [...] Read more.
Airport environmental carrying capacity (AECC) provides the fundamental conditions for airport development and operation activities. The prediction of AECC is a necessary condition for planning an appropriate development mode for the airport. This paper studies the dynamic prediction method of the AECC to explore the development characteristics of AECC in different airports. Based on the driving force-pressure-state-response (DPSR) framework, the method selects 17 main variables from economic, social, environmental and operational dimensions, and then combines the drawing of causal loop diagrams and the establishment of system flow diagrams to construct the system dynamics (SD) model of AECC. The predicted values of AECC are obtained through SD model simulation and accelerated genetic algorithm projection pursuit (AGA-PP) model calculation. Considering sustainable development needs, different scenarios are set to analyze the appropriate development mode of the airport. The case study of the Pearl River Delta airports resulted in two main conclusions. First, in the same economic zone, different airports with similar aircraft movements have similar development characteristics of AECC. Second, the appropriate development modes for different airports are different, and the appropriate development modes for the airport in different periods are also different. The case study also proves that the AECC prediction based on SD model and AGA-PP model can realize short-term policy formulation and long-term planning for the airport development mode, and provide decision-making support for relevant departments of airport. Full article
(This article belongs to the Collection Air Transportation—Operations and Management)
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<p>The flow chart of methodology.</p> Full article ">Figure 2
<p>Causality diagram of the four subsystems.</p> Full article ">Figure 3
<p>Airport environmental carrying capacity system flow diagram.</p> Full article ">Figure 4
<p>The development trend of AECC in three airports.</p> Full article ">Figure 5
<p>The <span class="html-italic">α<sub>j</sub></span> of the main variables.</p> Full article ">Figure 6
<p>Simulation results of key variables in different scenarios.</p> Full article ">Figure 7
<p>Predicted values of AECC in different scenarios.</p> Full article ">
20 pages, 9407 KiB  
Article
Dynamic Burst Actuation to Enhance the Flow Control Authority of Plasma Actuators
by Takuto Ogawa, Kengo Asada, Satoshi Sekimoto, Tomoaki Tatsukawa and Kozo Fujii
Aerospace 2021, 8(12), 396; https://doi.org/10.3390/aerospace8120396 - 13 Dec 2021
Cited by 10 | Viewed by 3315
Abstract
A computational study was conducted on flows over an NACA0015 airfoil with dielectric barrier discharge (DBD) plasma. The separated flows were controlled by a DBD plasma actuator installed at the 5% chord position from the leading edge, where operated AC voltage was modulated [...] Read more.
A computational study was conducted on flows over an NACA0015 airfoil with dielectric barrier discharge (DBD) plasma. The separated flows were controlled by a DBD plasma actuator installed at the 5% chord position from the leading edge, where operated AC voltage was modulated with the duty cycle not given a priori but dynamically changed based on the flow fluctuations over the airfoil surface. A single-point pressure sensor was installed at the 40% chord position of the airfoil surface and the DBD plasma actuator was activated and deactivated based on the strength of the measured pressure fluctuations. The Reynolds number was set to 63,000 and flows at angles of attack of 12 and 16 degrees were considered. The three-dimensional compressible Navier–Stokes equations including the DBD plasma actuator body force were solved using an implicit large-eddy simulation. Good flow control was observed, and the burst frequency proven to be effective in previous fixed burst frequency studies is automatically realized by this approach. The burst frequency is related to the characteristic pressure fluctuation; our approach was improved based on the findings. This improved approach realizes the effective burst frequency with a lower control cost and is robust to changing the angle of attack. Full article
(This article belongs to the Special Issue Large Eddy Simulation in Aerospace Engineering)
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<p>Schematic diagram of the DBD plasma actuator and an example of the practical application.</p> Full article ">Figure 2
<p>Waveform for burst actuation.</p> Full article ">Figure 3
<p>Schematic of the dynamic burst actuation.</p> Full article ">Figure 4
<p>Waveform for the dynamic burst actuation.</p> Full article ">Figure 5
<p>Distribution of body force in the <span class="html-italic">x</span> direction based on the Suzen–Huang model [<a href="#B31-aerospace-08-00396" class="html-bibr">31</a>] and body force vectors near the actuator. Gray and orange areas represent the airfoil surface and exposed electrode, respectively.</p> Full article ">Figure 6
<p>Computational grids and computational domain. The grids were visualized for every four points. (<b>a</b>) Perspective view. (<b>b</b>) Cross-sectional view. (<b>c</b>) Computational domain.</p> Full article ">Figure 7
<p>Control flowchart of FTM.</p> Full article ">Figure 8
<p>Aerodynamic characteristics in each FTM and reference cases at <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>12</mn> </mrow> </semantics></math>. (<b>a</b>) Lift-to-drag ratio (<math display="inline"><semantics> <mrow> <mi>L</mi> <mo>/</mo> <mi>D</mi> </mrow> </semantics></math>). (<b>b</b>) Lift and drag coefficients (<math display="inline"><semantics> <msub> <mi>C</mi> <mi mathvariant="normal">L</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>C</mi> <mi mathvariant="normal">D</mi> </msub> </semantics></math>).</p> Full article ">Figure 9
<p>Instantaneous flow field (top, <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>1250</mn> </mrow> </semantics></math>) and spanwise-averaged flow field (bottom, <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>250</mn> </mrow> </semantics></math>) with isosurfaces of the second invariant of the velocity gradient tensor (<span class="html-italic">Q</span>) colored based on the pressure coefficient (<math display="inline"><semantics> <msub> <mi>C</mi> <mi mathvariant="normal">p</mi> </msub> </semantics></math>) in FTM1 at <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>12</mn> </mrow> </semantics></math>. The red circles denote the locations of representative spanwise vortices.</p> Full article ">Figure 10
<p>Time histories of <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>/</mo> <mi>D</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi mathvariant="normal">p</mi> <mo>,</mo> <mi>var</mi> </mrow> </msub> </semantics></math> at the pressure sensor and the drive status of the plasma actuator in FTM1 from top to bottom. The top figure overlaps with the distribution of the spanwise-averaged <math display="inline"><semantics> <msub> <mi>C</mi> <mi mathvariant="normal">p</mi> </msub> </semantics></math> on the upper surface of the airfoil in the <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>/</mo> <mi>c</mi> </mrow> </semantics></math>-<span class="html-italic">t</span> domain.</p> Full article ">Figure 11
<p>Instantaneous flow field (top, <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>1250</mn> </mrow> </semantics></math>) and spanwise-averaged flow field (bottom, <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>250</mn> </mrow> </semantics></math>) with isosurfaces of the second invariant of the velocity gradient tensor (<span class="html-italic">Q</span>) colored based on <math display="inline"><semantics> <msub> <mi>C</mi> <mi mathvariant="normal">p</mi> </msub> </semantics></math> in FTM4 at <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>12</mn> </mrow> </semantics></math>. The red circles and squares denote the locations of representative spanwise vortices.</p> Full article ">Figure 12
<p>The time histories of <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>/</mo> <mi>D</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi mathvariant="normal">p</mi> <mo>,</mo> <mi>var</mi> </mrow> </msub> </semantics></math> at the pressure sensor and the drive status of the plasma actuator in FTM4 from the top to bottom. The top figure overlaps with the distribution of the spanwise-averaged <math display="inline"><semantics> <msub> <mi>C</mi> <mi mathvariant="normal">p</mi> </msub> </semantics></math> on the upper surface of the airfoil in the <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>/</mo> <mi>c</mi> </mrow> </semantics></math>-<span class="html-italic">t</span> domain. The red symbols and arrows represent the characteristic vortex positions and their trajectories.</p> Full article ">Figure 13
<p>Temporary burst frequency (<math display="inline"><semantics> <msubsup> <mi>F</mi> <mi>tmp</mi> <mo>+</mo> </msubsup> </semantics></math>) histogram in each FTM case at <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>12</mn> </mrow> </semantics></math>.</p> Full article ">Figure 14
<p>Control flowchart of the DTM.</p> Full article ">Figure 15
<p>Aerodynamic characteristics in the DTM, FTM4, and the reference cases at <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>12</mn> </mrow> </semantics></math>. (<b>a</b>) Lift- to-drag ratio (<math display="inline"><semantics> <mrow> <mi>L</mi> <mo>/</mo> <mi>D</mi> </mrow> </semantics></math>). (<b>b</b>) Lift and drag coefficients (<math display="inline"><semantics> <msub> <mi>C</mi> <mi mathvariant="normal">L</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>C</mi> <mi mathvariant="normal">D</mi> </msub> </semantics></math>).</p> Full article ">Figure 16
<p>Instantaneous flow field (top, <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>1250</mn> </mrow> </semantics></math>) and spanwise-averaged flow field (bottom, <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>250</mn> </mrow> </semantics></math>) with isosurfaces of the second invariant of the velocity gradient tensor (<span class="html-italic">Q</span>) colored based on <math display="inline"><semantics> <msub> <mi>C</mi> <mi mathvariant="normal">p</mi> </msub> </semantics></math> in the DTM at <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>12</mn> </mrow> </semantics></math>. The red symbols denote the locations of representative spanwise vortices.</p> Full article ">Figure 17
<p>Time histories of <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>/</mo> <mi>D</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <msub> <mi>C</mi> <mi mathvariant="normal">p</mi> </msub> </semantics></math> at the pressure sensor, and the drive status of the plasma actuator in FTM1 from the top to bottom. The top figure overlaps with the distribution of the spanwise-averaged <math display="inline"><semantics> <msub> <mi>C</mi> <mi mathvariant="normal">p</mi> </msub> </semantics></math> on the upper surface of the airfoil in the <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>/</mo> <mi>c</mi> </mrow> </semantics></math>-<span class="html-italic">t</span> domain. The red symbols and arrows represent the characteristic vortex positions and their trajectories.</p> Full article ">Figure 18
<p>Temporary burst frequency (<math display="inline"><semantics> <msubsup> <mi>F</mi> <mi>tmp</mi> <mo>+</mo> </msubsup> </semantics></math>) histograms in the DTM and FTM4 at <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>12</mn> </mrow> </semantics></math>.</p> Full article ">Figure 19
<p>Aerodynamic characteristics in the DTM and the reference cases at <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>16</mn> </mrow> </semantics></math>. (<b>a</b>) Lift- to-drag ratio (<math display="inline"><semantics> <mrow> <mi>L</mi> <mo>/</mo> <mi>D</mi> </mrow> </semantics></math>). (<b>b</b>) Lift and drag coefficients (<math display="inline"><semantics> <msub> <mi>C</mi> <mi mathvariant="normal">L</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>C</mi> <mi mathvariant="normal">D</mi> </msub> </semantics></math>).</p> Full article ">Figure 20
<p>Instantaneous flow field (top, <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>625</mn> </mrow> </semantics></math>) and spanwise-averaged flow field (bottom, <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>125</mn> </mrow> </semantics></math>) with isosurfaces of the second invariant of the velocity gradient tensor (<span class="html-italic">Q</span>) colored based on <math display="inline"><semantics> <msub> <mi>C</mi> <mi mathvariant="normal">p</mi> </msub> </semantics></math> in the DTM at <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>16</mn> </mrow> </semantics></math>. The red symbols denote the locations of representative spanwise vortices.</p> Full article ">Figure 21
<p>The time histories of <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>/</mo> <mi>D</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi mathvariant="normal">p</mi> <mo>,</mo> <mi>var</mi> </mrow> </msub> </semantics></math> at the pressure sensor and the drive status of the plasma actuator in FTM4 from top to bottom. The top figure overlaps with the distribution of the spanwise-averaged <math display="inline"><semantics> <msub> <mi>C</mi> <mi mathvariant="normal">p</mi> </msub> </semantics></math> on the upper surface of the airfoil in the <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>/</mo> <mi>c</mi> </mrow> </semantics></math>-<span class="html-italic">t</span> domain. The red symbols and arrows represent the characteristic vortex positions and their trajectories.</p> Full article ">Figure 22
<p>Temporary burst frequency (<math display="inline"><semantics> <msubsup> <mi>F</mi> <mi>tmp</mi> <mo>+</mo> </msubsup> </semantics></math>) histogram in the DTM at <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>16</mn> </mrow> </semantics></math>.</p> Full article ">
22 pages, 1700 KiB  
Article
Multi-Objective and Multi-Phase 4D Trajectory Optimization for Climate Mitigation-Oriented Flight Planning
by Alessio Vitali, Manuela Battipede and Angelo Lerro
Aerospace 2021, 8(12), 395; https://doi.org/10.3390/aerospace8120395 - 13 Dec 2021
Cited by 8 | Viewed by 3439
Abstract
Aviation contribution to global warming and anthropogenic climate change is increasing every year. To reverse this trend, it is crucial to identify greener alternatives to current aviation technologies and paradigms. Research in aircraft operations can provide a swift response to new environmental requirements, [...] Read more.
Aviation contribution to global warming and anthropogenic climate change is increasing every year. To reverse this trend, it is crucial to identify greener alternatives to current aviation technologies and paradigms. Research in aircraft operations can provide a swift response to new environmental requirements, being easier to exploit on current fleets. This paper presents the development of a multi-objective and multi-phase 4D trajectory optimization tool to be integrated within a Flight Management System of a commercial aircraft capable of performing 4D trajectory tracking in a Free Route Airspace context. The optimization algorithm is based on a Chebyshev pseudospectral method, adapted to perform a multi-objective optimization with the two objectives being the Direct Operating Cost and the climate cost of a climb-cruise-descent trajectory. The climate cost function applies the Global Warming Potential metric to derive a comprehensive cost index that includes the climate forcing produced by CO2 and non-CO2 emissions, and by the formation of aircraft-induced clouds. The output of the optimization tool is a set of Pareto-optimal 4D trajectories among which the aircraft operator can choose the best solution that satisfies both its economic and environmental goals. Full article
(This article belongs to the Special Issue Aerospace Guidance, Navigation and Control)
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<p>Scheme of the averaging method used to pass along-trajectory weather information to the CGL nodes <span class="html-italic">p</span> of the optimization algorithms. The number of sub-CGL nodes <span class="html-italic">s</span> is chosen to have a spatial resolution similar to the GFS grid with weather data. The gradient color map represents different values of a generic GFS data variable.</p> Full article ">Figure 2
<p>Pareto-optimal (green) and dominated sub-optimal solutions (blue) of the three multi-objective optimization problems, with environmental cost function based on: (<b>a</b>) GWP<sub>20</sub>, (<b>b</b>) GWP<sub>50</sub>, (<b>c</b>) GWP<sub>100</sub>.</p> Full article ">Figure 3
<p>Individual climate forcing components of the environmental cost <math display="inline"><semantics> <msub> <mi>J</mi> <mrow> <mi>E</mi> <mi>N</mi> <mi>V</mi> </mrow> </msub> </semantics></math>, expressed in CO<sub>2</sub>-equivalent Ton, for environmental cost function based on: (<b>a</b>) GWP<sub>20</sub>, (<b>b</b>) GWP<sub>50</sub>, (<b>c</b>) GWP<sub>100</sub>. Negative values for SO<sub>2</sub> indicate a cooling forcing.</p> Full article ">Figure 4
<p>Some of the Pareto-optimal trajectories for the GWP<sub>100</sub> case, ranging from the optimal DOC trajectory (purple) to the climate-optimal trajectory (light green): (<b>a</b>) lateral path plotted on a map, with cyan segments indicating along track AIC formation, blue areas representing ISSR, and wind speeds expressed by a contour and vector map; (<b>b</b>) altitude profile plotted against time with along track AIC formation; (<b>c</b>) TAS profile. The atmospheric conditions represented in (<b>a</b>) are evaluated at an indicative cruise level, given by the average altitude of each plotted trajectory as a function of the longitude.</p> Full article ">Figure 5
<p>The plots show trajectory data of the Pareto-optimal solutions for the environmental cost function based on GWP<sub>100</sub>: (<b>a</b>) route extension with respect to the great circle distance between WP1 and WP2; (<b>b</b>) flight duration; (<b>c</b>) fuel consumption; (<b>d</b>) fraction of AIC length with respect to route length, plotted against <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>J</mi> <mrow> <mi>D</mi> <mi>O</mi> <mi>C</mi> </mrow> </msub> </mrow> </semantics></math>. Green dots represent trajectories plotted in <a href="#aerospace-08-00395-f004" class="html-fig">Figure 4</a>.</p> Full article ">
15 pages, 8294 KiB  
Article
Strength Evaluation and Failure Analysis of the Vortex Reducer under Overspeed Condition
by Mengdi Ma, Dasheng Wei, Yanrong Wang, Di Li and Hui Zhang
Aerospace 2021, 8(12), 394; https://doi.org/10.3390/aerospace8120394 - 13 Dec 2021
Cited by 3 | Viewed by 2494
Abstract
Rotating parts of aeroengines need to have a high speed margin according to the civil aviation airworthiness regulations. Previous studies on burst speed are based on mechanical properties of standard specimens. In this paper, a new method for predicting burst speed by means [...] Read more.
Rotating parts of aeroengines need to have a high speed margin according to the civil aviation airworthiness regulations. Previous studies on burst speed are based on mechanical properties of standard specimens. In this paper, a new method for predicting burst speed by means of a tensile test of a simulative specimen is proposed, and the predicted results are compared with the traditional method. The results show that the stress gradient of the designed simulative specimen and the assessment location of vortex reducer are in good agreement, which indicates that they have similar stress characteristics. The burst speed predicted by the new method is greater than the traditional method. Both prediction methods can provide a reference for such a structure in the design stage. In addition, the overspeed test of a vortex reducer is carried out, and the results verify that it still has sufficient strength reserves at 120% relative speed. Full article
(This article belongs to the Section Aeronautics)
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<p>Schematic diagram of vortex reducer.</p> Full article ">Figure 2
<p>Vortex reducer and its installation position: (<b>a</b>) Three dimensional model; (<b>b</b>) Position diagram.</p> Full article ">Figure 3
<p>FE model of the vortex reducer and the disk (1/18 sector).</p> Full article ">Figure 4
<p>Temperature field (unit: °C, NT11 represents the node temperature).</p> Full article ">Figure 5
<p>Pressure in the disk cavity.</p> Full article ">Figure 6
<p>Stress distribution of support ring under working condition (120% relative speed, unit: MPa): (<b>a</b>) Hoop stress; (<b>b</b>) Equivalent stress.</p> Full article ">Figure 7
<p>Schematic diagram of the vortex reducer and its connecting structure.</p> Full article ">Figure 8
<p>FE model of the vortex reducer and connecting structure under the test condition (1/6 sector).</p> Full article ">Figure 9
<p>Stress distribution of support ring under test condition (120% relative speed, unit: MPa): (<b>a</b>) Hoop stress; (<b>b</b>) Equivalent stress.</p> Full article ">Figure 10
<p>Test equipment: (<b>a</b>) The rotor high-speed rotating tester; (<b>b</b>) Installation schematic.</p> Full article ">Figure 11
<p>Change in relative speed with time.</p> Full article ">Figure 12
<p>Fluorescence nondestructive examination: (<b>a</b>) The air tubes; (<b>b</b>) The support ring.</p> Full article ">Figure 13
<p>Diagram of dangerous section of the supporting ring.</p> Full article ">Figure 14
<p>Diagram of dangerous section of the simulative specimen.</p> Full article ">Figure 15
<p>Diagram of the simulative specimen.</p> Full article ">Figure 16
<p>FE model and boundary conditions of the simulative specimen.</p> Full article ">Figure 17
<p>Comparison of stress gradient between the structure and the simulative specimen: (<b>a</b>) path of stress gradient extracted from the structure; (<b>b</b>) path of stress gradient extracted from the simulative specimen; (<b>c</b>) results of the normalized stress gradient.</p> Full article ">Figure 18
<p>Simulative specimen with different clamping methods: (<b>a</b>) Clamped by pin holes; (<b>b</b>) Clamped by hydraulic pressure.</p> Full article ">Figure 19
<p>The fracture of the specimens after testing: (<b>a</b>) Clamped by pin holes; (<b>b</b>) Clamped by hydraulic pressure.</p> Full article ">Figure 20
<p>True stress–strain curve of the high-strength GH4169 alloy at 500 °C.</p> Full article ">Figure 21
<p>The variation of equivalent plastic strain with tensile load.</p> Full article ">Figure 22
<p>The variation of average equivalent stress with tensile load.</p> Full article ">Figure 23
<p>The variation of average tensile direction stress with tensile load.</p> Full article ">Figure 24
<p>The variation of equivalent plastic strain of the dangerous section of the vortex reducer with rotational speed.</p> Full article ">Figure 25
<p>The variation of average equivalent stress of the dangerous section of the vortex reducer with rotational speed.</p> Full article ">Figure 26
<p>The variation of average hoop stress of the dangerous section of vortex reducer with rotational speed.</p> Full article ">
13 pages, 4573 KiB  
Article
Design of a High Uniformity Laser Sheet Optical System for Particle Image Velocimetry
by Kewei Yin, Jun Zhang and Shuang Chen
Aerospace 2021, 8(12), 393; https://doi.org/10.3390/aerospace8120393 - 10 Dec 2021
Cited by 3 | Viewed by 3313
Abstract
Particle image velocimetry (PIV) is a non-contact, instantaneous and full-flow velocity measurement method based on cross-correlation analysis of particle image. It is widely used in fluid mechanics and aerodynamics. Laser sheet optical system is one of the key equipment of PIV, and it [...] Read more.
Particle image velocimetry (PIV) is a non-contact, instantaneous and full-flow velocity measurement method based on cross-correlation analysis of particle image. It is widely used in fluid mechanics and aerodynamics. Laser sheet optical system is one of the key equipment of PIV, and it is an important guarantee to obtain high definition particle image. In the PIV measurement task of large low speed wind tunnel, in order to solve the problem of sheet light illumination uniformity of large size model and take into account the requirements of PIV technology on the thickness of the sheet light, a hybrid algorithm is used to design a high uniformity laser sheet optical system based on the theory of physical optics. The simulation results show that the size of the sheet light is 400 mm × 1 mm, the diffraction efficiency reaches 97.77%, and the non-uniformity is only 0.03%. It is helpful to acquire high-resolution images of particles in the full field of view. It also can be applied to a series of non-contact flow field measurement techniques such as plane laser induced fluorescence, filtered Rayleigh scattering and two-color plane laser induced fluorescence temperature measurement. Full article
(This article belongs to the Special Issue Advanced Flow Diagnostic Tools)
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<p>The light intensity distribution with a cylindrical lens group.</p> Full article ">Figure 2
<p>Schematic diagram of the laser sheet optical system.</p> Full article ">Figure 3
<p>Schematic diagram of the DOE fabrication process.</p> Full article ">Figure 4
<p>Schematic of the hybrid algorithm.</p> Full article ">Figure 5
<p>Schematic of the iterative algorithm.</p> Full article ">Figure 6
<p>The comparison of convergence properties of three algorithms.</p> Full article ">Figure 7
<p>Phase distribution of the DOE.</p> Full article ">Figure 8
<p>Intensity distribution of the sheet light at Z = 2000 mm. (<b>a</b>) Intensity distribution on the image plane; (<b>b</b>) intensity distribution along the X axis; (<b>c</b>) intensity distribution along the Y axis.</p> Full article ">Figure 8 Cont.
<p>Intensity distribution of the sheet light at Z = 2000 mm. (<b>a</b>) Intensity distribution on the image plane; (<b>b</b>) intensity distribution along the X axis; (<b>c</b>) intensity distribution along the Y axis.</p> Full article ">Figure 9
<p>The DOE phase distribution along Y axis.</p> Full article ">Figure 10
<p>Intensity distribution at Z = 1900 mm and Z = 2100 mm. (<b>a</b>) Intensity distribution along X axis at Z = 1900 mm; (<b>b</b>) intensity distribution along Y axis at Z = 1900 mm; (<b>c</b>) intensity distribution along X axis at Z = 2100 mm; (<b>d</b>) intensity distribution along Y axis at Z = 2100 mm.</p> Full article ">Figure 10 Cont.
<p>Intensity distribution at Z = 1900 mm and Z = 2100 mm. (<b>a</b>) Intensity distribution along X axis at Z = 1900 mm; (<b>b</b>) intensity distribution along Y axis at Z = 1900 mm; (<b>c</b>) intensity distribution along X axis at Z = 2100 mm; (<b>d</b>) intensity distribution along Y axis at Z = 2100 mm.</p> Full article ">
25 pages, 4628 KiB  
Review
Challenges and Solutions for High-Speed Aviation Piston Pumps: A Review
by Chenchen Zhang, Chenhang Zhu, Bin Meng and Sheng Li
Aerospace 2021, 8(12), 392; https://doi.org/10.3390/aerospace8120392 - 10 Dec 2021
Cited by 22 | Viewed by 8551
Abstract
As a core power component, aviation piston pumps are widely used in aircraft hydraulic systems. The piston pump’s power-to-weight ratio is extremely crucial in the aviation industry, and the “ceiling effect” of the PV value (product of compressive stress and linear velocity) limits [...] Read more.
As a core power component, aviation piston pumps are widely used in aircraft hydraulic systems. The piston pump’s power-to-weight ratio is extremely crucial in the aviation industry, and the “ceiling effect” of the PV value (product of compressive stress and linear velocity) limits the piston pump’s ability to increase working pressure. Therefore, increasing the piston pump’s speed has been a real breakthrough in terms of further enhancing the power-to-weight ratio. However, the piston pump’s design faces several challenges under the extreme operating conditions at high speeds. This study reviews several problems aviation axial piston pumps face under high-speed operating conditions, including friction loss, cavitation, cylinder overturning, flow pressure pulsation, and noise. It provides a detailed description of the research state of the art of these problems and potential solutions. The axial piston pump’s inherent sliding friction pair, according to the report, considerably restricts further increasing of its speed and power-to-weight ratio. With its mature technology and deep research base, the axial piston pump will continue to dominate the aviation pumps. Furthermore, breaking the limitation of the sliding friction pair on speed and power density, thus innovating a novel structure of the piston pump, is also crucial. Therefore, this study also elaborates on the working principle and development process of the two-dimensional (2D) piston pump, which is a representative of current high-speed pump structure innovation. Full article
(This article belongs to the Section Aeronautics)
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<p>Schematic diagram of three friction pairs of the axial piston pump. 1. Valve plate. 2. Cylinder. 3. Piston. 4. Slipper. 5. Swashplate. 6. Transmission shaft.</p> Full article ">Figure 2
<p>Supercharger turbine (Obtained from [<a href="#B83-aerospace-08-00392" class="html-bibr">83</a>]).</p> Full article ">Figure 3
<p>Drag reduction runner (Obtained from [<a href="#B83-aerospace-08-00392" class="html-bibr">83</a>]).</p> Full article ">Figure 4
<p>Pre-expansion cavity (Obtained from [<a href="#B83-aerospace-08-00392" class="html-bibr">83</a>]).</p> Full article ">Figure 5
<p>Orifice (Obtained from [<a href="#B83-aerospace-08-00392" class="html-bibr">83</a>]).</p> Full article ">Figure 6
<p>Tilting movement of the cylinder due to centrifugal effect (Obtained from [<a href="#B64-aerospace-08-00392" class="html-bibr">64</a>]).</p> Full article ">Figure 7
<p>Plane cylinder and spherical cylinder (Obtained from [<a href="#B83-aerospace-08-00392" class="html-bibr">83</a>]).</p> Full article ">Figure 8
<p>Textured cylinder and valve plate (Obtained from [<a href="#B83-aerospace-08-00392" class="html-bibr">83</a>]).</p> Full article ">Figure 9
<p>Orifice and groove combined valve plate (Reproduced from [<a href="#B119-aerospace-08-00392" class="html-bibr">119</a>]).</p> Full article ">Figure 10
<p>A380 EDP shape (Obtained from [<a href="#B122-aerospace-08-00392" class="html-bibr">122</a>]).</p> Full article ">Figure 11
<p>RC principle built-in attenuator (Obtained from [<a href="#B124-aerospace-08-00392" class="html-bibr">124</a>]).</p> Full article ">Figure 12
<p>Mechanism of axial piston pump noise (Reproduced from [<a href="#B127-aerospace-08-00392" class="html-bibr">127</a>]): (<b>a</b>) the generation and transmission of noise excitation source of axial piston pump; (<b>b</b>) transmission path of noise excitation source of axial piston pump.</p> Full article ">Figure 13
<p>Schematic diagram of two-dimensional single unit piston pump. 1. Cone roller. 2. Saddle cam. 3. High-pressure distribution window. 4. Cylinder. 5. Concentric ring. 6. Fork–roller coupling. 7. Low-pressure distribution window. 8. Distribution groove. 9. Piston.</p> Full article ">Figure 14
<p>Comparison chart of two-dimensional and axial piston pumps with the same displacement.</p> Full article ">Figure 15
<p>Schematic diagram of two-dimensional piston double-unit pump: (<b>a</b>) schematic; (<b>b</b>) physical map. 1. Cone roller. 2. Saddle cam. 3. Cylinder. 4. Low-pressure distribution window. 5. Distribution trough. 6. Fork–roller coupling. 7. Concentric ring. 8. High-pressure distribution window. 9. Distribution trough.</p> Full article ">Figure 16
<p>Force-balanced two-dimensional piston pump: (<b>a</b>) schematic; (<b>b</b>) prototype. 1. Shift fork–cone roller coupling. 2. Left balance suspension. 3. Left drive suspension. 4. Left constant acceleration and deceleration cam. 5. Cylinder. 6. Inner piston. 7. Right constant acceleration and deceleration cam. 8. Outer piston. 9. Right balance suspension. 10. Right drive suspension. 11. Fork–cone roller.</p> Full article ">Figure 17
<p>Force-balanced two-dimensional piston pump with cam rotation: (<b>a</b>) schematic; (<b>b</b>) prototype. 1. Left fork shaft. 2. Left rail group. 3. Cylinder. 4. Suction column. 5. Right guide rail group. 6. Right fork. 7. Case. 8. Right working cavity. 9. Cone roller. 10. Oil drain. 11. Two-dimensional piston. 12. Left working cavity.</p> Full article ">Figure 18
<p>Stacked two-dimensional piston pump: (<b>a</b>) schematic; (<b>b</b>) prototype. 1. Transmission shaft. 2. Stacked cone roller set. 3. Case. 4. Variable loop. 5. Inner piston. 6. Outer piston. 7. Pushrod device. 8. Lever.</p> Full article ">Figure 19
<p>Schematic diagram of two-dimensional piston pump variable device. 1. Variable loop. 2. Lever. 3. Pushrod device.</p> Full article ">
14 pages, 2617 KiB  
Article
Aircraft Assembly Snags: Human Errors or Lack of Production Design?
by Ageel Abdulaziz Alogla and Mansoor Alruqi
Aerospace 2021, 8(12), 391; https://doi.org/10.3390/aerospace8120391 - 10 Dec 2021
Cited by 7 | Viewed by 4169
Abstract
To err is an intrinsic human trait, which means that human errors, at some point, are inevitable. Business improvement tools and practices neglect to deal with the root causes of human error; hence, they ignore certain design considerations that could possibly prevent or [...] Read more.
To err is an intrinsic human trait, which means that human errors, at some point, are inevitable. Business improvement tools and practices neglect to deal with the root causes of human error; hence, they ignore certain design considerations that could possibly prevent or minimise such errors from occurring. Recognising this gap, this paper seeks to conceptualise a model that incorporates cognitive science literature based on a mistake-proofing concept, thereby offering a deeper, more profound level of human error analysis. An exploratory case study involving an aerospace assembly line was conducted to gain insights into the model developed. The findings of the case study revealed four different causes of human errors, as follows: (i) description similarity error, (ii) capture errors, (iii) memory lapse errors, and (iv) interruptions. Based on this analysis, error-proofing measures have been proposed accordingly. This paper lays the foundation for future work on the psychology behind human errors in the aerospace industry and highlights the importance of understanding human errors to avoid quality issues and rework in production settings, where labour input is of paramount importance. Full article
(This article belongs to the Special Issue Aircraft Fault Detection)
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<p>Framework designed to help identify and manage the root cause of aircraft snags (developed based on the work of Zhang [<a href="#B36-aerospace-08-00391" class="html-bibr">36</a>] and Norman [<a href="#B26-aerospace-08-00391" class="html-bibr">26</a>]).</p> Full article ">Figure 2
<p>Workflow of the implementation of the proposed framework.</p> Full article ">Figure 3
<p>The SOP process confirming temporary tightening.</p> Full article ">Figure 4
<p>The SOP process confirming hand tightening of screws.</p> Full article ">Figure 5
<p>Error-proofing marking torque wrench.</p> Full article ">Figure 6
<p>Marked bolt (red φ9).</p> Full article ">Figure 7
<p>Successive SOPs overlap with similar action.</p> Full article ">Figure 8
<p>An SOP that consists of two different clip numbers.</p> Full article ">
18 pages, 11921 KiB  
Article
The Development of a Flight Test Platform to Study the Body Freedom Flutter of BWB Flying Wings
by Pengtao Shi, Feng Liu, Yingsong Gu and Zhichun Yang
Aerospace 2021, 8(12), 390; https://doi.org/10.3390/aerospace8120390 - 10 Dec 2021
Cited by 5 | Viewed by 3364
Abstract
A flight test platform is designed to conduct an experimental study on the body freedom flutter of a BWB flying wing, and a flight test is performed by using the proposed platform. A finite element model of structural dynamics is built, and unsteady [...] Read more.
A flight test platform is designed to conduct an experimental study on the body freedom flutter of a BWB flying wing, and a flight test is performed by using the proposed platform. A finite element model of structural dynamics is built, and unsteady aerodynamics and aeroelastic characteristics of the flying wing are analyzed by the doublet lattice method and g-method, respectively. Based on the foregoing analyses, a low-cost and low-risk flying-wing test platform is designed and manufactured. Then, the ground vibration test is implemented, and according to its results, the structural dynamics model is updated. The flight test campaign shows that the body freedom flutter occurs at low flight speed, which is consistent with the updated analytical result. Finally, an active flutter suppression controller is designed using a genetic algorithm for the developed flying wing for future tests, considering the gains and sensor location as design parameters. The open- and closed-loop analyses in time- and frequency-domain analyses demonstrate that the designed controller can improve the instability boundary of the closed-loop system effectively. Full article
(This article belongs to the Special Issue Flutter Phenomena – Modeling, Identification and Control)
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<p>Sketch of grid division used in DLM.</p> Full article ">Figure 2
<p>Aerodynamic configuration of the flying wing for BFF flight test platform.</p> Full article ">Figure 3
<p>Coefficients of (<b>a</b>) lift and (<b>b</b>) pitching moment versus with angle of attack (with Alpha in degrees).</p> Full article ">Figure 4
<p>Structural configuration of flying wing for BFF flight test platform.</p> Full article ">Figure 5
<p>FEM of flying wing for BFF flight test platform.</p> Full article ">Figure 6
<p>Mode shapes of first four elastic modes: (<b>a</b>) symmetric 1st wing bending; (<b>b</b>) antisymmetric 1st wing bending; (<b>c</b>) symmetric 2nd wing bending; (<b>d</b>) antisymmetric 2nd wing bending.</p> Full article ">Figure 7
<p>Lifting surface model of flying wing for BFF flight test platform.</p> Full article ">Figure 8
<p>Interpolated mode shapes of aerodynamic mesh: (<b>a</b>) symmetric 1st wing bending; (<b>b</b>) antisymmetric 1st wing bending; (<b>c</b>) symmetric 2nd wing bending; (<b>d</b>) antisymmetric 2nd wing bending.</p> Full article ">Figure 9
<p>Aeroelastic analysis results.</p> Full article ">Figure 10
<p>Assembly details of test model.</p> Full article ">Figure 11
<p>Test model including airborne equipment.</p> Full article ">Figure 12
<p>Layout scheme of sensors.</p> Full article ">Figure 13
<p>GVT setup.</p> Full article ">Figure 14
<p>Mode shapes of first four elastic modes obtained from GVT: (<b>a</b>) elastic heaving; (<b>b</b>) symmetric 1st wing bending; (<b>c</b>) antisymmetric 1st wing bending; (<b>d</b>) symmetric 2nd wing bending.</p> Full article ">Figure 15
<p>First four elastic mode shapes of G-FEM: (<b>a</b>) elastic heaving; (<b>b</b>) symmetric 1st wing bending; (<b>c</b>) antisymmetric 1st wing bending; (<b>d</b>) symmetric 2nd wing bending.</p> Full article ">Figure 16
<p>Aeroelastic characteristic of updated F-FEM.</p> Full article ">Figure 17
<p>BFF occurred in flight test.</p> Full article ">Figure 18
<p>Flight test results of BFF#1: (<b>a</b>) time-domain data and (<b>b</b>) frequency spectrum.</p> Full article ">Figure 19
<p>Flight test results of BFF#2: (<b>a</b>) time-domain data and (<b>b</b>) frequency spectrum.</p> Full article ">Figure 20
<p>Flight test results of low-speed level flight: (<b>a</b>) time-domain data and (<b>b</b>) frequency spectrum.</p> Full article ">Figure 21
<p>Schematic block diagram of closed-loop aeroservoelastic system.</p> Full article ">Figure 22
<p>Genetic algorithm optimization process.</p> Full article ">Figure 23
<p>Stability of closed-loop system with optimal configuration parameters: (<b>a</b>) root locus diagram; (<b>b</b>) g-method results.</p> Full article ">
20 pages, 11979 KiB  
Article
Oil Fumes, Flight Safety, and the NTSB
by Judith Anderson and Dieter Scholz
Aerospace 2021, 8(12), 389; https://doi.org/10.3390/aerospace8120389 - 10 Dec 2021
Cited by 2 | Viewed by 8018
Abstract
During its investigations into a series of ten aircraft crashes from 1979 to 1981, US National Transportation Safety Board (NTSB) officials were presented with a hypothesis that “several” of the crashes could have been caused by pilot impairment from breathing oil fumes inflight. [...] Read more.
During its investigations into a series of ten aircraft crashes from 1979 to 1981, US National Transportation Safety Board (NTSB) officials were presented with a hypothesis that “several” of the crashes could have been caused by pilot impairment from breathing oil fumes inflight. The NTSB and their industry partners ultimately dismissed the hypothesis. The authors reviewed the crash reports, the mechanics of the relevant engine oil seals, and some engine bleed air data to consider whether the dismissal was justified. Four of the nine aircraft crash reports include details which are consistent with pilot impairment caused by breathing oil fumes. None of the tests of ground-based bleed air measurements of a subset of oil-based contaminants generated in the engine type on the crashed aircraft reproduced the inflight conditions that the accident investigators had flagged as potentially unsafe. The NTSB’s conclusion that the hypothesis of pilot incapacitation was “completely without validity” was inconsistent with the evidence. Parties with a commercial conflict of interest should not have played a role in the investigation of their products. There is enough evidence that pilots can be impaired by inhaling oil fumes to motivate more stringent design, operation, and reporting regulations to protect safety of flight. Full article
(This article belongs to the Special Issue Aircraft Design (SI-3/2021))
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<p>Garrett TPE331 turboprop engine (based on [<a href="#B18-aerospace-08-00389" class="html-bibr">18</a>], p. 15-3). A. main shaft (engine shaft) with the gas turbine. B. propeller shaft with the reduction gear. 1,2. bearings that support the main shaft. 3,4. bearings that support the propeller shaft. 1. compressor bearing.</p> Full article ">Figure 2
<p>A typical ball-bearing [<a href="#B19-aerospace-08-00389" class="html-bibr">19</a>].</p> Full article ">Figure 3
<p>Garrett TPE331 turboprop engine cutaway drawing [<a href="#B20-aerospace-08-00389" class="html-bibr">20</a>].</p> Full article ">Figure 4
<p>Garrett TPE331 turboprop engine cutaway drawing (detail based on [<a href="#B20-aerospace-08-00389" class="html-bibr">20</a>]). A. main shaft. B. first-stage centrifugal compressor. 1. compressor bearing (a ball-bearing). 2. carbon seal. 3. labyrinth seal.</p> Full article ">Figure 5
<p>Mechanical (carbon) seal assembly next to the main shaft compressor bearing on the TPE331 engine (based on Figure 3 in [<a href="#B16-aerospace-08-00389" class="html-bibr">16</a>]).</p> Full article ">Figure 6
<p>Labyrinth seal close to the main shaft compressor bearing (based on Figure 11 in [<a href="#B16-aerospace-08-00389" class="html-bibr">16</a>]). Note: The carbon seal is located in between the compressor bearing (<b>on the left</b>) and the labyrinth seal (<b>on the right</b>) in the empty space but is not shown in this figure.</p> Full article ">Figure 7
<p>Flow through the labyrinth seal in the TPE331 engine next to the compressor bearing and resulting pressure. The flow to the left pushes back the oil that still comes through the carbon seal (based on Figure 13 in [<a href="#B16-aerospace-08-00389" class="html-bibr">16</a>]).</p> Full article ">Figure 8
<p>Mitsubishi MU-2B (Photograph by Alan Lebeda [<a href="#B25-aerospace-08-00389" class="html-bibr">25</a>], trimmed, GFDL 1.2).</p> Full article ">
16 pages, 4583 KiB  
Article
Comparison of the Aircraft Noise Calculation Programs sonAIR, FLULA2 and AEDT with Noise Measurements of Single Flights
by Jonas Meister, Stefan Schalcher, Jean-Marc Wunderli, David Jäger, Christoph Zellmann and Beat Schäffer
Aerospace 2021, 8(12), 388; https://doi.org/10.3390/aerospace8120388 - 10 Dec 2021
Cited by 20 | Viewed by 3748
Abstract
As aircraft noise affects large areas around airports, noise exposure calculations need to be highly accurate. In this study, we compare noise exposure measurements with calculations of several thousand single flights at Zurich and Geneva airports, Switzerland, of three aircraft noise calculation programs: [...] Read more.
As aircraft noise affects large areas around airports, noise exposure calculations need to be highly accurate. In this study, we compare noise exposure measurements with calculations of several thousand single flights at Zurich and Geneva airports, Switzerland, of three aircraft noise calculation programs: sonAIR, a next-generation aircraft noise calculation program, and the two current best-practice programs FLULA2 and AEDT. For one part of the flights, we had access to flight data recorder (FDR) data, which contain flight configuration information that sonAIR can account for. For the other part, only radar data without flight configuration information were available. Overall, all three programs show good results, with mean differences between calculations and measurements smaller than ±0.5 dB in the close range of the airports. sonAIR performs clearly better than the two best-practice programs if FDR data are available. However, in situations without FDR data (reduced set of input data), sonAIR cannot exploit its full potential and performs similarly well as FLULA2 and AEDT. In conclusion, all three programs are well suited to determine averaged noise metrics resulting from complex scenarios consisting of many flights (e.g., yearly air operations), while sonAIR is additionally capable to highly accurately reproduce single flights in greater detail. Full article
(This article belongs to the Special Issue Aircraft Noise)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Noise monitoring terminals (NMT) in close range to ZRH with all flight trajectories used for this study, colored by procedure. The black circles around each terminal represent spatial gates, which the flight trajectories have to penetrate to be considered (basemap: swissALTI3D LV95, swisstopo; source: Federal Office of Topography).</p> Full article ">Figure 2
<p>Noise monitoring terminals (NMT) in close range to GVA with all flight trajectories used for this study, colored by procedure. The black circles around each terminal represent spatial gates, which the flight trajectories have to penetrate to be considered (basemap: swissALTI3D LV95, swisstopo; source: Federal Office of Topography).</p> Full article ">Figure 3
<p>Measuring stations of the measurement campaign in the far range to ZRH with all flight trajectories used for this study. Approaches on runway 28 (A28) are depicted in blue and on runway 34 (A34) in purple. The black lines represent spatial gates, which the flight trajectories have to penetrate to be considered (basemap: swissALTI3D LV95, swisstopo; source: Federal Office of Topography).</p> Full article ">Figure 4
<p>Scatter plots of simulation vs. measurements for aircraft with FDR data in the close range to the airports (ZRH and GVA combined), grouped by procedure (D: Departure, A: Approach, SD: Standard deviation). (<b>a</b>) sonAIR, (<b>b</b>) FLULA2, (<b>c</b>) AEDT.</p> Full article ">Figure 5
<p>Scatter plots of simulation vs. measurements for aircraft without FDR data in the close range to the airports (ZRH and GVA combined), grouped by procedure (D: Departure, A: Approach, SD: Standard deviation). (<b>a</b>) sonAIR, (<b>b</b>) FLULA2, (<b>c</b>) AEDT.</p> Full article ">Figure 6
<p>Scatter plots of simulation vs. measurements for approaches of aircraft with FDR data in the far range of ZRH. A: Approach, SD: Standard deviation. Note that no departures were recorded (<a href="#sec2dot3-aerospace-08-00388" class="html-sec">Section 2.3</a>). (<b>a</b>) sonAIR, (<b>b</b>) FLULA2, (<b>c</b>) AEDT.</p> Full article ">Figure 7
<p>Box-whisker-plots of differences ∆L<sub>AE,t10</sub> for sonAIR and FLULA2 and ∆L<sub>AE</sub> for AEDT (simulation minus measurements) for all scenarios, grouped by procedure (D: Departure, A: Approach). (<b>a</b>) ZRH &amp; GVA, FDR, close range; (<b>b</b>) ZRH &amp; GVA, nonFDR, close range; (<b>c</b>) ZRH, FDR, far range.</p> Full article ">Figure 8
<p>Scatter plots of the calculated event levels L<sub>AEt10</sub> (L<sub>AE</sub> for AEDT) between the three models for aircraft with FDR data in the close range (ZRH and GVA combined), grouped by procedure (D: Departure, A: Approach, SD: Standard deviation). (<b>a</b>) sonAIR vs. FLULA2; (<b>b</b>) FLULA2 vs. AEDT; (<b>c</b>) sonAIR vs. AEDT.</p> Full article ">Figure 9
<p>Scatter plots of the calculated event levels L<sub>AE, t10</sub> (L<sub>AE</sub> for AEDT) between the three models for aircraft without FDR data in the close range (ZRH and GVA combined), grouped by procedure (D: Departure, A: Approach, SD: Standard deviation). (<b>a</b>) sonAIR vs. FLULA2; (<b>b</b>) FLULA2 vs. AEDT; (<b>c</b>) sonAIR vs. AEDT.</p> Full article ">
33 pages, 78560 KiB  
Article
A Simplified FE Modeling Strategy for the Drop Process Simulation Analysis of Light and Small Drone
by Yongjie Zhang, Yingjie Huang, Zhiwen Li, Ke Liang, Kang Cao and Yazhou Guo
Aerospace 2021, 8(12), 387; https://doi.org/10.3390/aerospace8120387 - 9 Dec 2021
Cited by 5 | Viewed by 5097
Abstract
The numerical accuracy of drop process simulation and collision response for drones is primarily determined by the finite element modeling method and simplified method of drone airframe structure. For light and small drones exhibiting diverse shapes and configurations, mixed materials and structures, deformation [...] Read more.
The numerical accuracy of drop process simulation and collision response for drones is primarily determined by the finite element modeling method and simplified method of drone airframe structure. For light and small drones exhibiting diverse shapes and configurations, mixed materials and structures, deformation and complex destruction behaviors, the way of developing a reasonable and easily achieved high-precision simplified modeling method by ensuring the calculation accuracy and saving the calculation cost has aroused increasing concern in impact dynamics simulation. In the present study, the full-size modeling and simplified modeling methods that are specific to different components of a relatively popular light and small drone were analyzed in an LS-DYNA software environment. First, a full-size high-precision model of the drone was built, and the model accuracy was verified by performing the drop tests at the component level as well as the whole machine level. Subsequently, based on the full-size high-precision model, the property characteristics of the main components of the light and small drone and their common simplification methods were classified, a series of simplified modeling methods for different components were developed, several single simplified models and combined simplified models were built, and a method to assess the calculation error of the peak impact load in the simplified models was proposed. Lastly, by comparing and analyzing the calculation accuracy of various simplified models, the high-precision simplified modeling strategy was formulated, and the suggestions were proposed for the impact dynamics simulation of the light and small drone falling. Given the analysis of the calculation scale and solution time of the simplified model, the high-precision simplified modeling method developed here is capable of noticeably reducing the modeling difficulty, the solution scale and the calculation time while ensuring the calculation accuracy. Moreover, it shows promising applications in several fields (e.g., structure design, strength analysis and impact process simulation of drone). Full article
(This article belongs to the Collection Unmanned Aerial Systems)
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<p>(<b>a</b>) Drone size. (<b>b</b>) Internal structure. (<b>c</b>) Drone materials.</p> Full article ">Figure 2
<p>(<b>a</b>) Bolted connection. (<b>b</b>) Spring connection. (<b>c</b>) Revolute. (<b>d</b>) Tie-Break.</p> Full article ">Figure 3
<p>(<b>a</b>) Schematic diagram of drop test. (<b>b</b>) Drop test layout. (<b>c</b>) Schematic diagram of drop-weight test. (<b>d</b>) Drop-weight test layout.</p> Full article ">Figure 4
<p>(<b>a</b>) Drop test results. (<b>b</b>) Drop-weight test results.</p> Full article ">Figure 5
<p>(<b>a</b>) Drone positive posture. (<b>b</b>) Drone vertical posture. (<b>c</b>) Drop test results of drone positive posture. (<b>d</b>) Drop test results of drone vertical posture.</p> Full article ">Figure 6
<p>Comparison of deformation response of full-size model. (<b>a</b>) Drop test of drone components. (<b>b</b>) Drop-weight test of drone components. (<b>c</b>) Drop test of drone positive posture. (<b>d</b>) Drop test of drone vertical posture.</p> Full article ">Figure 7
<p>Comparison of impact load between simulation and experiment. (<b>a</b>) Drop test of drone components. (<b>b</b>) Drop-weight test of drone components. (<b>c</b>) Drop test of drone positive posture. (<b>d</b>) Drop test of drone vertical posture.</p> Full article ">Figure 8
<p>(<b>a</b>) Battery full-size model. (<b>b</b>) Battery simplified model. (<b>c</b>) Simplified model-1.</p> Full article ">Figure 9
<p>(<b>a</b>) Motor full-size model. (<b>b</b>) Motor simplified model. (<b>c</b>) Simplified model-2.</p> Full article ">Figure 10
<p>(<b>a</b>) Camera full-size model. (<b>b</b>) Camera simplified model. (<b>c</b>) Simplified model-3.</p> Full article ">Figure 11
<p>(<b>a</b>) Circuit board full-size model. (<b>b</b>) Circuit board simplified model. (<b>c</b>) Simplified model-4.</p> Full article ">Figure 12
<p>(<b>a</b>) Full-size model of front arm. (<b>b</b>) Full-size model of rear arm. (<b>c</b>) Simplified model-5 arm model. (<b>d</b>) Simplified model-6 arm model. (<b>e</b>) Simplified model-5. (<b>f</b>) Simplified model-6.</p> Full article ">Figure 13
<p>(<b>a</b>) Comparison of full-size model and simplified model of upper fuselage. (<b>b</b>) Comparison of full-size model and simplified model of lower fuselage. (<b>c</b>) Comparison of full-size model and simplified model of baseplate (<b>d</b>) Comparison of full-size model and simplified model of battery case (<b>e</b>) Simplified model-7.</p> Full article ">Figure 14
<p>(<b>a</b>) Preventer plate. (<b>b</b>) Pedestal (<b>c</b>) Simplified model-8. (<b>d</b>) Elevation view of support arm. (<b>e</b>) Section view of support arm. (<b>f</b>) Simplified model-9. (<b>g</b>) Front shroud. (<b>h</b>) Simplified model-10.</p> Full article ">Figure 15
<p>Comparison of impact loads in single simplified model.</p> Full article ">Figure 16
<p>Comparison of deformation response of some simplified models.</p> Full article ">Figure 17
<p>Combined simplified model. (<b>a</b>) Model after assembly. (<b>b</b>) Model before assembly.</p> Full article ">Figure 18
<p>Comparison of deformation response of combined simplified model. (<b>a</b>) Drop test of drone components. (<b>b</b>) Drop-weight test of drone components. (<b>c</b>) Drop test of drone positive posture. (<b>d</b>) Drop test of drone vertical posture.</p> Full article ">Figure 19
<p>Peak load and error of combined simplified model. (<b>a</b>) Drop test of drone components. (<b>b</b>) Drop-weight test of drone components. (<b>c</b>) Drop test of drone positive posture. (<b>d</b>) Drop test of drone vertical posture.</p> Full article ">Figure 20
<p>Comparison of stress distribution between the simplified model and the full-scale model at the typical time of the complete machine positive posture drop test. (<b>a</b>) 2 × 10<sup>−3</sup> s time. (<b>b</b>) 5 × 10<sup>−3</sup> s time. (<b>c</b>) 8 × 10<sup>−3</sup> s time.</p> Full article ">Figure 21
<p>Comparison of stress distribution between the simplified model and the full-scale model at the typical time of the complete machine vertical posture drop test. (<b>a</b>) 4 × 10<sup>−3</sup> s time. (<b>b</b>) 11 × 10<sup>−3</sup> s time.</p> Full article ">
18 pages, 5775 KiB  
Article
Investigation of a Portable Wind Tunnel for Energy Harvesting
by Haigang Tian, Tianyi Hao, Chao Liu, Han Cao and Xiaobiao Shan
Aerospace 2021, 8(12), 386; https://doi.org/10.3390/aerospace8120386 - 9 Dec 2021
Cited by 2 | Viewed by 3663
Abstract
Current wind tunnels possess a large space volume and high manufacturing cost, which are not suitable for investigating micro energy harvesters. This paper aims to design and fabricate a small, portable and low-speed wind tunnel for energy harvesting. A wind tunnel structure was [...] Read more.
Current wind tunnels possess a large space volume and high manufacturing cost, which are not suitable for investigating micro energy harvesters. This paper aims to design and fabricate a small, portable and low-speed wind tunnel for energy harvesting. A wind tunnel structure was first designed, a finite element analyses is then utilized to obtain the airflow velocity and turbulence intensity at the testing section, and the influence of the structural parameters of the wind tunnel on the flow field performance is finally investigated to achieve better performance. An experimental prototype of the wind tunnel was fabricated to verify the simulation results. Results demonstrated that the distribution uniformity and average turbulence intensity at the test section decrease first and then increase with the increase of both the diffuser and contraction lengths. The rectifying and damping effect of the honeycomb increase with increasing porosity and thickness. When the diffuser and contraction lengths are 850 mm and 480 mm, respectively, a better distribution uniformity and a lower turbulence intensity can be achieved. Experimental results were in good agreement with the simulation values. The maximum airflow velocity can reach up to 24.74 m/s, and the minimum error was only 1.23%. The designed wind tunnel achieved low-speed, small, portable and stable functions. This work provides an important guidance for further investigating the piezoelectric energy harvesting. Full article
(This article belongs to the Section Aeronautics)
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<p>Structural diagram of the designed wind tunnel.</p> Full article ">Figure 2
<p>Vitoshinsky curve.</p> Full article ">Figure 3
<p>Velocity distribution characteristics at different diffuser lengths.</p> Full article ">Figure 4
<p>Velocity contour at various diffuser lengths.</p> Full article ">Figure 5
<p>Variation of wind velocity distribution with axial distance under different contraction lengths.</p> Full article ">Figure 6
<p>Velocity contour at various contraction lengths.</p> Full article ">Figure 7
<p>Finite element model of the honeycomb based on porous grid.</p> Full article ">Figure 8
<p>Variation of velocity distribution characteristics of the test section with axial distance under different honeycomb porosities.</p> Full article ">Figure 9
<p>Velocity contour (<b>a</b>) and the turbulence intensity contour (<b>b</b>) at different honeycomb porosities.</p> Full article ">Figure 10
<p>Variation of velocity distribution characteristics with axial distance under different honeycomb thicknesses.</p> Full article ">Figure 11
<p>Velocity contour (<b>a</b>) and turbulence intensity contour (<b>b</b>) at various honeycomb thicknesses.</p> Full article ">Figure 12
<p>Wind tunnel experimental system.</p> Full article ">Figure 13
<p>Variation of velocity with the converter frequency obtained experimentally and numerically.</p> Full article ">Figure 14
<p>Variation of velocity with axial distance obtained experimentally and numerically.</p> Full article ">
13 pages, 3257 KiB  
Article
Burn Time Correction of Start-Up Transients for CAMUI Type Hybrid Rocket Engine
by Tor Viscor, Hikaru Isochi, Naoto Adachi and Harunori Nagata
Aerospace 2021, 8(12), 385; https://doi.org/10.3390/aerospace8120385 - 9 Dec 2021
Cited by 2 | Viewed by 2724
Abstract
Burn time errors caused by various start-up transient effects have a significant influence on the regression modelling of hybrid rockets. Their influence is especially pronounced in the simulation model of the Cascaded Multi Impinging Jet (CAMUI) hybrid rocket engine. This paper analyses these [...] Read more.
Burn time errors caused by various start-up transient effects have a significant influence on the regression modelling of hybrid rockets. Their influence is especially pronounced in the simulation model of the Cascaded Multi Impinging Jet (CAMUI) hybrid rocket engine. This paper analyses these transient burn time errors and their effect on the regression simulations for short burn time engines. To address these errors, the equivalent burn time is introduced and is defined as the time the engine would burn if it were burning at its steady-state level throughout the burn time to achieve the measured total impulse. The accuracy of the regression simulation with and without the use of equivalent burn time is then finally compared. Equivalent burn time is shown to address the burn time issue successfully for port regression and, therefore, also for other types of cylindrical port hybrid rocket engines. For the CAMUI-specific impinging jet fore-end and back-end surfaces, though, the results are inconclusive. Full article
(This article belongs to the Special Issue Hybrid Rocket(Volume II))
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<p>CAMUI fuel geometry concept.</p> Full article ">Figure 2
<p>LOX flow and Thrust.</p> Full article ">Figure 3
<p>Burn time error analysis with 30% error introduced of the 230 Hi engine.</p> Full article ">Figure 4
<p>CAMUI simulator concept: Constants analyser Module (<b>top</b>) and Regression Simulator Module (<b>bottom</b>).</p> Full article ">Figure 5
<p>Equivalent burn time concept (230 Hi).</p> Full article ">Figure 6
<p><b>Top</b>: overshoot and undershoot errors, using nominal burn time derived constants (conceptual). <b>Bottom</b>: overshoot error, using equivalent burn time derived constants (conceptual).</p> Full article ">Figure 7
<p>Calculation flow with nominal vs. equivalent burn times.</p> Full article ">Figure 8
<p>Accuracy of simulation for analysis of Low and High Reynold’s number engines using nominal <math display="inline"><semantics> <mrow> <mi>t</mi> <mi>b</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>t</mi> <msub> <mi>b</mi> <mrow> <mi>e</mi> <mi>q</mi> </mrow> </msub> </mrow> </semantics></math>, port only: (<b>a</b>) 100 Lo, (<b>b</b>) 200 Lo, (<b>c</b>) 230 Hi and (<b>d</b>) 200 Hi.</p> Full article ">Figure 9
<p>Accuracy of simulation for analysis of Low and High Reynold’s number engines using nominal <math display="inline"><semantics> <mrow> <mi>t</mi> <mi>b</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>t</mi> <msub> <mi>b</mi> <mrow> <mi>e</mi> <mi>q</mi> </mrow> </msub> </mrow> </semantics></math>: (<b>a</b>) 100 Lo, (<b>b</b>) 200 Lo, (<b>c</b>) 230 Hi, (<b>d</b>) 200 Hi.</p> Full article ">Figure 10
<p>Equivalent burn time effect.</p> Full article ">Figure 11
<p>Equivalent burn time effect (top view).</p> Full article ">
22 pages, 43436 KiB  
Article
Rope-Hook Recovery Controller Designed for a Flying-Wing UAV
by Zhao Deng, Fuqiang Bing, Zhiming Guo and Liaoni Wu
Aerospace 2021, 8(12), 384; https://doi.org/10.3390/aerospace8120384 - 7 Dec 2021
Cited by 5 | Viewed by 3948
Abstract
Due to the complexity of landing environments, precision guidance and high-precision control technology have become key to the rope-hook recovery of shipborne unmanned aerial vehicles (UAVs). The recovery process was divided into three stages and a reasonable guidance strategy had been designed for [...] Read more.
Due to the complexity of landing environments, precision guidance and high-precision control technology have become key to the rope-hook recovery of shipborne unmanned aerial vehicles (UAVs). The recovery process was divided into three stages and a reasonable guidance strategy had been designed for them, respectively. This study separated the guidance and control issues into an outer guidance loop and an inner control loop. The inner loop (attitude control loop) controled the UAV to follow the acceleration commands generated by the outer loop (trajectory tracking loop). The inner loop of the longitudinal controller and the lateral controller were designed based on active disturbance rejection control (ADRC), which has strong anti-interference ability. In the last phase, the outer loop of the longitudinal controller switched from a total energy control system (TECS), which greatly decoupled the altitude channel and speed channel, to the proportional navigation (PN) guidance law, while the outer loop of lateral controller switches from the proportional control law based on the L1 guidance law, which can reduce the tracking error and deviation, to the PN guidance law, which considerably enhances the tracking precision. Finally, the simulation data and flight test data show that the controller has strong robustness and good tracking precision, which ensures safe rope-hook recovery. Full article
(This article belongs to the Section Aeronautics)
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Figure 1

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<p>Characteristics of typical recovery methods.</p> Full article ">Figure 2
<p>Vertical rope-hook recovery system.</p> Full article ">Figure 3
<p>Flying-wing UAV.</p> Full article ">Figure 4
<p>Trajectory plan for rope-hook recovery.</p> Full article ">Figure 5
<p>Relative positioning data in ALIGN mode.</p> Full article ">Figure 6
<p>GPS positioning accuracy comparison test diagram.</p> Full article ">Figure 7
<p>Pitch data contrast.</p> Full article ">Figure 8
<p>Baseline data contrast.</p> Full article ">Figure 9
<p>Different trajectories for different initial positions and headings.</p> Full article ">Figure 10
<p>The relationship between the rope hook and the center of the circle.</p> Full article ">Figure 11
<p>Controller structure: (<b>a</b>) longitudinal controller; (<b>b</b>) late controller.</p> Full article ">Figure 12
<p>Pitch angle control loop.</p> Full article ">Figure 13
<p>Roll angle channel loop.</p> Full article ">Figure 14
<p>TECS block diagram.</p> Full article ">Figure 15
<p><span class="html-italic">L</span><sub>1</sub> guidance for following circular paths.</p> Full article ">Figure 16
<p>Schematic diagram of PN.</p> Full article ">Figure 17
<p>The normal and lateral overload data of the PN phase.</p> Full article ">Figure 18
<p>Longitudinal PN motion diagram.</p> Full article ">Figure 19
<p>Height in pursuit of the moving rope-hook.</p> Full article ">Figure 20
<p>Simulation data: (<b>a</b>) 3D trajectory; (<b>b</b>) trajectory tracking curves.</p> Full article ">Figure 21
<p>Launch and recovery system.</p> Full article ">Figure 22
<p>Test route information.</p> Full article ">Figure 23
<p>UAV hits the rope-hook.</p> Full article ">Figure 24
<p>3D trajectory.</p> Full article ">Figure 25
<p>Trajectory tracking: (<b>a</b>) height trajectory tracking; (<b>b</b>) lateral trajectory tracking.</p> Full article ">Figure 26
<p>Pitch angle and angle of sight curves.</p> Full article ">Figure 27
<p>Air speed and ground speed curves.</p> Full article ">Figure 28
<p>Trajectory tracking: (<b>a</b>) height trajectory tracking; (<b>b</b>) lateral trajectory tracking.</p> Full article ">Figure 29
<p>Longitudinal and lateral miss distance.</p> Full article ">
18 pages, 5724 KiB  
Article
Controller Fatigue State Detection Based on ES-DFNN
by Haijun Liang, Changyan Liu, Kuanming Chen, Jianguo Kong, Qicong Han and Tiantian Zhao
Aerospace 2021, 8(12), 383; https://doi.org/10.3390/aerospace8120383 - 7 Dec 2021
Cited by 6 | Viewed by 2968
Abstract
The fatiguing work of air traffic controllers inevitably threatens air traffic safety. Determining whether eyes are in an open or closed state is currently the main method for detecting fatigue in air traffic controllers. Here, an eye state recognition model based on deep-fusion [...] Read more.
The fatiguing work of air traffic controllers inevitably threatens air traffic safety. Determining whether eyes are in an open or closed state is currently the main method for detecting fatigue in air traffic controllers. Here, an eye state recognition model based on deep-fusion neural networks is proposed for determination of the fatigue state of controllers. This method uses transfer learning strategies to pre-train deep neural networks and deep convolutional neural networks and performs network fusion at the decision-making layer. The fused network demonstrated an improved ability to classify the target domain dataset. First, a deep-cascaded neural network algorithm was used to realize face detection and eye positioning. Second, according to the eye selection mechanism, the pictures of the eyes to be tested were cropped and passed into the deep-fusion neural network to determine the eye state. Finally, the PERCLOS indicator was combined to detect the fatigue state of the controller. On the ZJU, CEW and ATCE datasets, the accuracy, F1 score and AUC values of different networks were compared, and, on the ZJU and CEW datasets, the recognition accuracy and AUC values among different methods were evaluated based on a comparative experiment. The experimental results show that the deep-fusion neural network model demonstrated better performance than the other assessed network models. When applied to the controller eye dataset, the recognition accuracy was 98.44%, and the recognition accuracy for the test video was 97.30%. Full article
(This article belongs to the Special Issue Controlling Speech Understanding and Air Traffic Safety Enhancement Based on AI)
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Figure 1

Figure 1
<p>Fatigue detection flow chart.</p> Full article ">Figure 2
<p>MTCNN network structure diagram.</p> Full article ">Figure 3
<p>Schematic diagram of transfer learning.</p> Full article ">Figure 4
<p>Schematic diagram of the eye selection mechanism.</p> Full article ">Figure 5
<p>DCNN structure diagram.</p> Full article ">Figure 6
<p>DNN structure diagram.</p> Full article ">Figure 7
<p>DFNN structure diagram.</p> Full article ">Figure 8
<p>Fusion flow chart.</p> Full article ">Figure 9
<p>The ZJU dataset.</p> Full article ">Figure 10
<p>The CEW dataset.</p> Full article ">Figure 11
<p>The ATCE dataset.</p> Full article ">Figure 12
<p>Comparison results of DFNN and the other three models on the ZJU dataset.</p> Full article ">Figure 13
<p>Comparison results of DFNN and the other three models on the CEW dataset.</p> Full article ">Figure 14
<p>Comparison results of DFNN and the other three models on the ATCE dataset.</p> Full article ">
16 pages, 3408 KiB  
Article
Thermal Effect on the Instability of Annular Liquid Jet
by Xiao Cui and Boqi Jia
Aerospace 2021, 8(12), 382; https://doi.org/10.3390/aerospace8120382 - 7 Dec 2021
Cited by 3 | Viewed by 2512
Abstract
The linear instability of an annular liquid jet with a radial temperature gradient in an inviscid gas steam is investigated theoretically. A physical model of an annular liquid jet with a radial temperature gradient is established, dimensionless governing equations and boundary conditions are [...] Read more.
The linear instability of an annular liquid jet with a radial temperature gradient in an inviscid gas steam is investigated theoretically. A physical model of an annular liquid jet with a radial temperature gradient is established, dimensionless governing equations and boundary conditions are given, and numerical solutions are obtained using the spectral collocation method. The correctness of the results is verified to a certain extent. The liquid surface tension coefficient is assumed to be a linear function of temperature. The effects of various dimensionless parameters (including the Marangoni number/Prandtl number, Reynolds number, temperature gradient, Weber number, gas-to-liquid density ratio and velocity ratio) on the instability of the annular liquid jet are discussed. A decreasing Weber number destabilizes the annular liquid jet when the Weber number is lower than a critical value. It is found that the effects of the Marangoni effect are related to the Weber number. The Marangoni effect enhances instability when the Weber number is small, while the Marangoni effect weakens instability when the Weber number is large. In addition, because the thermal effect is considered, a decreasing Reynolds number enhances the instability when the Weber number is lower than a critical value, which is similar to the results of a viscous liquid sheet with a temperature difference between two planar surfaces. Furthermore, the effects of other dimensionless parameters are also investigated. Full article
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Figure 1

Figure 1
<p>Schematic of the profile of the annular liquid jet.</p> Full article ">Figure 2
<p>The results compared with Shen and Li in different modes: (<b>a</b>) “para-sinuous” mode and (<b>b</b>) “para-varicose” mode (<span class="html-italic">α</span> = 1.25, <span class="html-italic">Re</span> = 250, ε<sub>1</sub> = ε<sub>2</sub> = 0.001).</p> Full article ">Figure 3
<p>Different modes of annular liquid jet: (<b>a</b>) “para-sinuous” mode and (<b>b</b>) “para-varicose” mode.</p> Full article ">Figure 4
<p>Effect of Weber number on the annular liquid jet instability (<span class="html-italic">Re</span> = 25, <span class="html-italic">We</span><sub>2</sub> = <span class="html-italic">We</span><sub>1</sub>/(1 − ∆<span class="html-italic">T</span>*), <span class="html-italic">γ</span><sub>1</sub> = <span class="html-italic">γ</span><sub>2</sub> = 0, <span class="html-italic">ε</span><sub>1</sub> = <span class="html-italic">ε</span><sub>2</sub> = 0.001, <span class="html-italic">Pr</span> = 0.5, ∆<span class="html-italic">T</span>* = 0.2, <span class="html-italic">α</span> = 1.25, <span class="html-italic">Ma</span> = 0.25).</p> Full article ">Figure 5
<p>Thermal effects when Weber number is small (<span class="html-italic">Re</span> = 5, <span class="html-italic">γ</span><sub>1</sub> = <span class="html-italic">γ</span><sub>2</sub> = 0, <span class="html-italic">ε</span><sub>1</sub> = <span class="html-italic">ε</span><sub>2</sub> = 0.001, <span class="html-italic">Pr</span> = 0.5, ∆<span class="html-italic">T</span>* = 0.2, <span class="html-italic">α</span> = 1.25).</p> Full article ">Figure 6
<p>Marangoni effect when Weber number is large (<span class="html-italic">Re</span> = 5, <span class="html-italic">γ</span><sub>1</sub> = <span class="html-italic">γ</span><sub>2</sub> = 0, <span class="html-italic">ε</span><sub>1</sub> = <span class="html-italic">ε</span><sub>2</sub> = 0.001, <span class="html-italic">Pr</span> = 0.5, ∆<span class="html-italic">T</span>* = 0.2, <span class="html-italic">α</span> = 1.25).</p> Full article ">Figure 7
<p>Effect of Prandtl number on the annular liquid jet instability when <span class="html-italic">Re</span> = 0.5 (<span class="html-italic">Re</span> = 0.5, <span class="html-italic">We</span><sub>1</sub> = 10, <span class="html-italic">We</span><sub>2</sub> = 12.5, <span class="html-italic">γ</span><sub>1</sub> = <span class="html-italic">γ</span><sub>2</sub> = 0, <span class="html-italic">ε</span><sub>1</sub> = <span class="html-italic">ε</span><sub>2</sub> = 0.001, ∆<span class="html-italic">T</span>* = 0.2, <span class="html-italic">α</span> = 1.25, <span class="html-italic">Ma</span> = 0.25).</p> Full article ">Figure 8
<p>Effect of Prandtl number on the annular liquid jet instability when <span class="html-italic">Re</span> = 5 (<span class="html-italic">Re</span> = 5, <span class="html-italic">We</span><sub>1</sub> = 10, <span class="html-italic">We</span><sub>2</sub> = 12.5, <span class="html-italic">γ</span><sub>1</sub> = <span class="html-italic">γ</span><sub>2</sub> = 0, <span class="html-italic">ε</span><sub>1</sub> = <span class="html-italic">ε</span><sub>2</sub> = 0.001, ∆<span class="html-italic">T</span>* = 0.2, <span class="html-italic">α</span> = 1.25, <span class="html-italic">Ma</span> = 0.25).</p> Full article ">Figure 9
<p>Variation in maximum growth rate with <span class="html-italic">Re</span> (<span class="html-italic">We</span><sub>1</sub> = 10, <span class="html-italic">We</span><sub>2</sub> = 12.5, <span class="html-italic">γ</span><sub>1</sub> = <span class="html-italic">γ</span><sub>2</sub> = 0, <span class="html-italic">ε</span><sub>1</sub> = <span class="html-italic">ε</span><sub>2</sub> = 0.001, ∆<span class="html-italic">T</span>* = 0.2, <span class="html-italic">α</span> = 1.25, <span class="html-italic">Ma</span> = 0.25).</p> Full article ">Figure 10
<p>Effect of heat transfer direction on the annular liquid jet instability (<span class="html-italic">Re</span> = 25, <span class="html-italic">γ</span><sub>1</sub> = <span class="html-italic">γ</span><sub>2</sub> =0, <span class="html-italic">ε</span><sub>1</sub> = <span class="html-italic">ε</span><sub>2</sub> = 0.001, <span class="html-italic">Pr</span> = 0.5, <span class="html-italic">α</span> = 1.25, <span class="html-italic">Ma</span> = 2.5).</p> Full article ">Figure 11
<p>Effect of Reynolds number on the annular liquid jet instability when Weber number is large (<span class="html-italic">We</span><sub>1</sub> = 1000, <span class="html-italic">We</span><sub>2</sub> = 1250, <span class="html-italic">γ</span><sub>1</sub> = <span class="html-italic">γ</span><sub>2</sub> =0, ε<sub>1</sub> = ε<sub>2</sub> = 0.001, <span class="html-italic">Pr</span> = 0.5, <span class="html-italic">α</span> = 1.25, <span class="html-italic">Ma</span> = 0.25, ∆<span class="html-italic">T</span>* = 0.2). (<b>a</b>) For a low Reynolds number. (<b>b</b>) For a large Reynolds number.</p> Full article ">Figure 12
<p>Effect of temperature difference (<span class="html-italic">We</span><sub>1</sub> = 10, <span class="html-italic">We</span><sub>2</sub> = 12.5, <span class="html-italic">Re</span> = 0.5, <span class="html-italic">γ</span><sub>1</sub> = <span class="html-italic">γ</span><sub>2</sub> = 0, <span class="html-italic">ε</span><sub>1</sub> = <span class="html-italic">ε</span><sub>2</sub> = 0.001, <span class="html-italic">Pr</span> = 0.5, <span class="html-italic">α</span> = 1.25, <span class="html-italic">Ma</span> = 2.5).</p> Full article ">Figure 13
<p>Effect of gas-to-liquid density ratio (<span class="html-italic">We</span><sub>1</sub> = 100, <span class="html-italic">We</span><sub>2</sub> = 125, <span class="html-italic">Re</span> = 25, <span class="html-italic">γ</span><sub>1</sub> = <span class="html-italic">γ</span><sub>2</sub> =0, <span class="html-italic">Pr</span> = 0.5, <span class="html-italic">α</span> = 1.25, <span class="html-italic">Ma</span> = 2.5, ∆<span class="html-italic">T</span>* = 0.2).</p> Full article ">Figure 14
<p>Effect of gas-to-liquid velocity ratio (<span class="html-italic">We</span><sub>1</sub> = 10, <span class="html-italic">We</span><sub>2</sub> = 12.5, <span class="html-italic">Re</span> = 2.5, <span class="html-italic">ε</span><sub>1</sub> = <span class="html-italic">ε</span><sub>2</sub> = 0, <span class="html-italic">Pr</span> = 0.5, <span class="html-italic">α</span> = 1.25, <span class="html-italic">Ma</span> = 0.25, ∆<span class="html-italic">T</span>* = 0.2).</p> Full article ">
29 pages, 5472 KiB  
Article
Dynamic Modeling and Analysis of Impact in Space Operation Tasks
by Yaxing Cai, Yujun Chen, Yazhong Luo and Xinglong Wang
Aerospace 2021, 8(12), 381; https://doi.org/10.3390/aerospace8120381 - 6 Dec 2021
Cited by 1 | Viewed by 2390
Abstract
For the rigid impact and flexible impact in space operation tasks, impact dynamic models between two objects are established in this paper, laying the model foundation for controlling or suppressing the impact. For the capture task between a grapple shaft and a rigid [...] Read more.
For the rigid impact and flexible impact in space operation tasks, impact dynamic models between two objects are established in this paper, laying the model foundation for controlling or suppressing the impact. For the capture task between a grapple shaft and a rigid body, the impact dynamic model is established based on the Zhiying–Qishao model. Moreover, by introducing a friction factor into the original impact model, an improved dynamic model between two rigid bodies is proposed. For the capture task with flexible impact, an impact dynamic model between the grapple shaft and a flexible wire rope is established based on the dynamic model of the flexible wire rope. The ground experiments and simulations are carried out with two objects on an air flow table. The experiment results validate the impact dynamic model proposed in this paper. Full article
(This article belongs to the Section Aeronautics)
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Figure 1
<p>The space snare capture.</p> Full article ">Figure 2
<p>The discrete model of the flexible wire rope.</p> Full article ">Figure 3
<p>The coordinate system of the flexible wire rope.</p> Full article ">Figure 4
<p>Force analysis on nodes <math display="inline"><semantics> <mrow> <msub> <mi>B</mi> <mi>i</mi> </msub> <mo>,</mo> <msub> <mi>B</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 5
<p>The initial position of the flexible wire rope.</p> Full article ">Figure 6
<p>The stress analysis of the flexible wire rope at initial state.</p> Full article ">Figure 7
<p>Determination of the distance range between the beginning and end of the flexible wire rope.</p> Full article ">Figure 8
<p>Virtual spring impact model of the flexible wire rope.</p> Full article ">Figure 9
<p>Force analysis of the flexible wire rope (including impact force).</p> Full article ">Figure 10
<p>Fitting process of contact stiffness coefficient of the flexible wire rope.</p> Full article ">Figure 11
<p>The general scheme of the ground experimental testbed.</p> Full article ">Figure 12
<p>The passive satellite simulator.</p> Full article ">Figure 13
<p>The active satellite simulator.</p> Full article ">Figure 14
<p>Relative displacement.</p> Full article ">Figure 15
<p>Impact force during the impact period.</p> Full article ">Figure 16
<p>Relative contact velocity.</p> Full article ">Figure 17
<p>Relative contact velocity.</p> Full article ">Figure 18
<p>Impact force.</p> Full article ">Figure 19
<p>The simulator displacement.</p> Full article ">Figure 20
<p>The simulator velocity.</p> Full article ">Figure 21
<p>The simulator acceleration.</p> Full article ">Figure 22
<p>The relative contact velocity.</p> Full article ">Figure 23
<p>Impact force.</p> Full article ">Figure 24
<p>The star target structure.</p> Full article ">Figure 25
<p>Physical map of target satellite.</p> Full article ">Figure 26
<p>Capture configuration of the manipulator. (<b>a</b>) The 3D simulation drawing and (<b>b</b>) physical map.</p> Full article ">Figure 27
<p>Contact with the flexible wire rope at the same point at different speeds. Relative speed: (<b>a</b>) 0.06 m/s, (<b>b</b>) 0.08 m/s, (<b>c</b>) 0.1 m/s.</p> Full article ">Figure 28
<p>Contact with the flexible wire rope at different points with the same speed. (<b>a</b>) Impact with the 5th node, (<b>b</b>) impact with the 10th node, (<b>c</b>) impact with the 20th node, (<b>d</b>) impact with the 25th node.</p> Full article ">
16 pages, 11035 KiB  
Article
Prediction and Validation of Landing Stability of a Lunar Lander by a Classification Map Based on Touchdown Landing Dynamics’ Simulation Considering Soft Ground
by Yeong-Bae Kim, Hyun-Jae Jeong, Shin-Mu Park, Jae Hyuk Lim and Hoon-Hee Lee
Aerospace 2021, 8(12), 380; https://doi.org/10.3390/aerospace8120380 - 6 Dec 2021
Cited by 7 | Viewed by 3768
Abstract
In this paper, a method for predicting the landing stability of a lunar lander by a classification map of the landing stability is proposed, considering the soft soil characteristics and the slope angle of the lunar surface. First, the landing stability condition in [...] Read more.
In this paper, a method for predicting the landing stability of a lunar lander by a classification map of the landing stability is proposed, considering the soft soil characteristics and the slope angle of the lunar surface. First, the landing stability condition in terms of the safe (=stable), sliding (=unstable), and tip-over (=statically unstable) possibilities was checked by dropping a lunar lander onto flat lunar surfaces through finite-element (FE) simulation according to the slope angle, friction coefficient, and soft/rigid ground, while the vertical touchdown velocity was maintained at 3 m/s. All of the simulation results were classified by a classification map with the aid of logistic regression, a machine-learning classification algorithm. Finally, the landing stability status was efficiently predicted by Monte Carlo (MC) simulation by just referring to the classification map for 10,000 input datasets, consisting of the friction coefficient, slope angles, and rigid/soft ground. To demonstrate the performance, two virtual lunar surfaces were employed based on a 3D terrain map of the LRO mission. Then, the landing stability was validated through landing simulation of an FE model of a lunar lander requiring high computation cost. The prediction results showed excellent agreement with those of landing simulations with a negligible computational cost of around a few seconds. Full article
(This article belongs to the Special Issue Vibration Control for Space Application)
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Figure 1
<p>Flow chart of landing stability prediction.</p> Full article ">Figure 2
<p>Lunar lander model: (<b>a</b>) CAD model; (<b>b</b>) FE model.</p> Full article ">Figure 3
<p>True stress and plastic strain curve of Al-HC.</p> Full article ">Figure 4
<p>Compressive load and displacement curve of Al-HC.</p> Full article ">Figure 5
<p>Landing configuration of lunar lander: (<b>a</b>) 1-2-1 landing; (<b>b</b>) 2-2 landing.</p> Full article ">Figure 6
<p>Classification of stability condition.</p> Full article ">Figure 7
<p>Parameterization of the lunar lander on the slope of the lunar surface.</p> Full article ">Figure 8
<p>Classification map of landing stability by logistic regression: (<b>a</b>) 1-2-1 landing on rigid ground; (<b>b</b>) 2-2 landing on rigid ground; (<b>c</b>) 1-2-1 landing on soft ground; (<b>d</b>) 2-2 landing on soft ground.</p> Full article ">Figure 9
<p>Configuration of ground at touchdown: (<b>a</b>) rigid ground; (<b>b</b>) soft ground (digging effect).</p> Full article ">Figure 10
<p>Two virtual lunar surfaces: (<b>a</b>) synthetic lunar surfaces of zone 1; (<b>b</b>) FE model of zone 1 (FE mesh not shown); (<b>c</b>) RSS of slope angles of zone 1; (<b>d</b>) synthetic lunar surfaces of zone 2; (<b>e</b>) FE model of zone 2 (FE mesh not shown); (<b>f</b>) RSS of slope angles of zone 2.</p> Full article ">Figure 11
<p>Combination of two prediction results: (<b>a</b>) zone 1 (rigid ground); (<b>b</b>) zone 2 (rigid ground); (<b>c</b>) zone 1 (soft ground); (<b>d</b>) zone 2 (soft ground).</p> Full article ">Figure 12
<p>Combination of two landing simulation results: (<b>a</b>) zone 1 (rigid ground); (<b>b</b>) zone 2 (rigid ground); (<b>c</b>) zone 1 (soft ground); (<b>d</b>) zone 2 (soft ground).</p> Full article ">Figure 13
<p>Prediction of landing success status and validation with FE simulation considering 100 landing sites: (<b>a</b>) zone 1 (rigid ground); (<b>b</b>) zone 2 (rigid ground); (<b>c</b>) zone 1 (soft ground); (<b>d</b>) zone 2 (soft ground).</p> Full article ">Figure 14
<p>Comparison of landing stability of predictions and landing simulations: (<b>a</b>) zone 1 (rigid ground); (<b>b</b>) zone 2 (rigid ground); (<b>c</b>) zone 1 (soft ground); (<b>d</b>) zone 2 (soft ground).</p> Full article ">
2 pages, 161 KiB  
Editorial
Special Issue “Technologies for Future Distributed Engine Control Systems”
by Radoslaw Przysowa
Aerospace 2021, 8(12), 379; https://doi.org/10.3390/aerospace8120379 - 6 Dec 2021
Viewed by 2174
Abstract
Current trends in aviation greatly expand the use of highly integrated, increasingly autonomous air vehicles, with distributed engine control systems (DECS) [...] Full article
(This article belongs to the Special Issue Technologies for Future Distributed Engine Control Systems)
10 pages, 4436 KiB  
Article
Aerodynamic Performance of a Coaxial Hex-Rotor MAV in Hover
by Yao Lei, Jiading Wang and Wenjie Yang
Aerospace 2021, 8(12), 378; https://doi.org/10.3390/aerospace8120378 - 5 Dec 2021
Cited by 2 | Viewed by 2986
Abstract
Micro aerial vehicles (MAVs) usually suffer from several challenges, not least of which are unsatisfactory hover efficiency and limited fly time. This paper discusses the aerodynamic characteristics of a novel Hex-rotor MAV with a coaxial rotor capable of providing higher thrust in a [...] Read more.
Micro aerial vehicles (MAVs) usually suffer from several challenges, not least of which are unsatisfactory hover efficiency and limited fly time. This paper discusses the aerodynamic characteristics of a novel Hex-rotor MAV with a coaxial rotor capable of providing higher thrust in a compact structure. To extend the endurance during hover, flow field analysis and aerodynamic performance optimization are conducted by both experiments and numerical simulations with different rotor spacing ratios (i = 0.56, 0.59, 0.63, 0.67, 0.71, 0.77, 0.83, 0.91). The measured parameters are thrust, power, and hover efficiency during the experiments. Retip ranged from 0.7 × 105 to 1.3 × 105 is also studied by Spalart–Allmaras simulations. The test results show that the MAV has the optimum aerodynamic performance at i = 0.56 with Retip = 0.85 × 105. Compared to the MAV with i = 0.98 for Retip = 0.85 × 105, thrust is increased by 5.18% with a reduced power of 3.8%, and hover efficiency is also improved by 12.14%. The simulated results indicate a weakness in inter-rotor interference with the increased rotor spacing. Additionally, the enlarged pressure difference, reduced turbulence, and weakened vortices are responsible for the aerodynamic improvement. This provides an alternative method for increasing the MAV fly time and offers inspiration for future structural design. Full article
(This article belongs to the Special Issue Aerodynamic Shape Optimization for Aerospace Engineering Applications)
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Figure 1

Figure 1
<p>Sketch of the coaxial hex-rotor MAV. (<b>a</b>) Vehicle Structure; (<b>b</b>) flowfield model.</p> Full article ">Figure 2
<p>Interaction area of the hex-rotor MAV with increased rotor spacing. (<b>a</b>) Small spacing; (<b>b</b>) mid spacing; and (<b>c</b>) large spacing. (The deeper the blue, the stronger the interaction).</p> Full article ">Figure 3
<p>Experimental setup (<b>a</b>) rotor configuration; and (<b>b</b>) sketch of setup.</p> Full article ">Figure 4
<p>Test section.</p> Full article ">Figure 5
<p>Thrust variation in hover (compared with <span class="html-italic">i</span> = 0.98).</p> Full article ">Figure 6
<p>Power Variation in hover (compared with <span class="html-italic">i</span> = 0.98).</p> Full article ">Figure 7
<p>FM variation in hover (compared with <span class="html-italic">i</span> = 0.98).</p> Full article ">Figure 8
<p>Computational grid.</p> Full article ">Figure 9
<p>Pressure distribution at Re = 0.89 × 10<sup>5</sup> (<b>a</b>) <span class="html-italic">i</span> = 0.71; (<b>b</b>) <span class="html-italic">i</span> = 0.63; (<b>c</b>) <span class="html-italic">i</span> = 0.56.</p> Full article ">Figure 10
<p>Velocity contours at Re = 0.89 × 10<sup>5</sup>. (<b>a</b>) <span class="html-italic">i</span> = 0.71; (<b>b</b>) <span class="html-italic">i</span> = 0.63; (<b>c</b>) <span class="html-italic">i</span> = 0.56.</p> Full article ">Figure 11
<p>Vectors distribution at Re = 0.89 × 10<sup>5</sup>. (<b>a</b>) <span class="html-italic">i</span> = 0.71; (<b>b</b>) <span class="html-italic">i</span> = 0.63; (<b>c</b>) <span class="html-italic">i</span> = 0.56.</p> Full article ">Figure 12
<p>Velocity and pressure distribution of MAV compared with isolated quad/coaxial rotors at Re = 0.89 × 10<sup>5</sup> for <span class="html-italic">i</span> = 0.56. (<b>a</b>) velocity variation; and (<b>b</b>) pressure variation.</p> Full article ">
24 pages, 2507 KiB  
Article
Sensitivity-Based Non-Linear Model Predictive Control for Aircraft Descent Operations Subject to Time Constraints
by Ramon Dalmau, Xavier Prats and Brian Baxley
Aerospace 2021, 8(12), 377; https://doi.org/10.3390/aerospace8120377 - 4 Dec 2021
Cited by 3 | Viewed by 2619
Abstract
The ability to meet a controlled time of arrival while also flying a continuous descent operation will enable environmentally friendly and fuel efficient descent operations while simultaneously maintaining airport throughput. Previous work showed that model predictive control, a guidance strategy based on a [...] Read more.
The ability to meet a controlled time of arrival while also flying a continuous descent operation will enable environmentally friendly and fuel efficient descent operations while simultaneously maintaining airport throughput. Previous work showed that model predictive control, a guidance strategy based on a reiterated update of the optimal trajectory during the descent, provides excellent environmental impact mitigation figures while meeting operational constraints in the presence of modeling errors. Despite that, the computational delay associated with the solution of the trajectory optimization problem could lead to performance degradation and stability issues. This paper proposes two guidance strategies based on the theory of neighboring extremals that alleviate this problem. Parametric sensitivities are obtained by linearization of the necessary conditions of optimality along the active optimal trajectory plan to rapidly update it for small perturbations, effectively converting the complex and time consuming non-linear programming problem into a manageable quadratic programming problem. Promising results, derived from more than 4000 simulations, show that the performance of this method is comparable to that of instantaneously recalculating the optimal trajectory at each time sample. Full article
(This article belongs to the Special Issue Closing the Gap in Aircraft Trajectories: Enhancing Optimization and Prediction Approaches)
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<p>BOSSS TWO standard arrival procedure.</p> Full article ">Figure 2
<p>RAP wind forecast and analysis (21 April 2018 00:00 with a look-ahead time of +1 h).</p> Full article ">Figure 3
<p>Optimal trajectory updates for perturbations in the parameters vector (21 April 2018 00:00 with a look-ahead time of +1 h). (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>δ</mi> <mi>c</mi> </mrow> </semantics></math> (actual minus forecast of <a href="#aerospace-08-00377-f002" class="html-fig">Figure 2</a>). (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>δ</mi> <mi>CTA</mi> <mo>=</mo> <mo>−</mo> <mn>30</mn> <mspace width="0.166667em"/> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>.</p> Full article ">Figure 4
<p>Planned and executed trajectories by control method (21 April 2018 00:00 with a look-ahead time of +1 h). (<b>a</b>) Open-loop. (<b>b</b>) INMPC.</p> Full article ">Figure 5
<p>Time deviation at the metering/merging fix. (<b>a</b>) NMPC. (<b>b</b>) SbNMPC. (<b>c</b>) AsNMPC. (<b>d</b>) Open-loop.</p> Full article ">Figure 6
<p>Specific energy deviation at the metering/merging fix. (<b>a</b>) NMPC. (<b>b</b>) SbNMPC. (<b>c</b>) AsNMPC. (<b>d</b>) Open-loop.</p> Full article ">Figure 7
<p>Fuel consumption difference with respect to the initial plan. (<b>a</b>) NMPC. (<b>b</b>) SbNMPC. (<b>c</b>) AsNMPC. (<b>d</b>) Open-loop.</p> Full article ">Figure 8
<p>Time and specific energy error at the metering/merging fix. (<b>a</b>) Time error. (<b>b</b>) Specific energy error.</p> Full article ">Figure 9
<p>Fuel consumption difference with respect to initial plan.</p> Full article ">
19 pages, 2994 KiB  
Article
GA Optimization of Variable Angle Tow Composites in Buckling and Free Vibration Analysis through Layerwise Theory
by Nasim Fallahi
Aerospace 2021, 8(12), 376; https://doi.org/10.3390/aerospace8120376 - 3 Dec 2021
Cited by 13 | Viewed by 3217
Abstract
In the current research, variable angle tow composites are used to improve the buckling and free vibration behavior of a structure. A one-dimensional (1D) Carrera Unified Formulation (CUF) is employed to determine the buckling loads and natural frequencies in Variable Angle Tow (VAT) [...] Read more.
In the current research, variable angle tow composites are used to improve the buckling and free vibration behavior of a structure. A one-dimensional (1D) Carrera Unified Formulation (CUF) is employed to determine the buckling loads and natural frequencies in Variable Angle Tow (VAT) square plates by taking advantage of the layerwise theory (LW). Subsequently, the Genetic Algorithm (GA) optimization method is applied to maximize the first critical buckling load and first natural frequency using the definition of linear fiber orientation angles. To show the power of the genetic algorithm for the VAT structure, a surrogate model using Response Surface (RS) method was used to demonstrate the convergence of the GA approach. The results showed the cost reduction for optimized VAT performance through GA optimization in combination with the 1D CUF procedure. Additionally, a Latin hypercube sampling (LHS) method with RS was used for buckling analysis. The capability of LHS sampling confirmed that it could be employed for the next stages of research along with GA. Full article
(This article belongs to the Special Issue Control and Optimization Problems in Aerospace Engineering)
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<p>The modeling of a beam structure using a 1D model where y is the along the beam axis, and the cross-section lies on the x–z plane through thickness.</p> Full article ">Figure 2
<p>Linear and cubic ESL and LW cases.</p> Full article ">Figure 3
<p>Variable angle tow plate.</p> Full article ">Figure 4
<p>SSSS boundary condition of the VAT plate.</p> Full article ">Figure 5
<p>Flowchart of genetic algorithm procedure (MATLAB) in combination with CUF approach (FORTRAN).</p> Full article ">Figure 6
<p>1D CUF cross-section refinement with <math display="inline"><semantics> <mrow> <mn>10</mn> <mi>B</mi> <mn>3</mn> </mrow> </semantics></math> influence on buckling load in <math display="inline"><semantics> <mrow> <mi>V</mi> <mi>A</mi> <msub> <mi>T</mi> <mn>1</mn> </msub> </mrow> </semantics></math> in terms of FEM and degrees of freedom. (<b>a</b>) Cross-section refinement. (<b>b</b>) DOF based on cross-section refinement.</p> Full article ">Figure 7
<p>Refinement of a 1D CUF beam element with a <math display="inline"><semantics> <mrow> <mn>160</mn> <mi>L</mi> <mn>9</mn> </mrow> </semantics></math> effect on the buckling load in <math display="inline"><semantics> <mrow> <mi>V</mi> <mi>A</mi> <msub> <mi>T</mi> <mn>1</mn> </msub> </mrow> </semantics></math> in relation to the FEM and the number of degrees of freedom. (<b>a</b>) Beam refinement. (<b>b</b>) DOF based on beam refinement.</p> Full article ">Figure 8
<p>Comparison of different layup designs with the optimum outcome.</p> Full article ">Figure 9
<p>The first buckling modes in different laminates. (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>Q</mi> <mi>I</mi> </mrow> </semantics></math>: 13.79 kN. (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>C</mi> <msub> <mi>S</mi> <mn>1</mn> </msub> </mrow> </semantics></math>: 16.51 kN. (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>C</mi> <msub> <mi>S</mi> <mn>2</mn> </msub> </mrow> </semantics></math>: 10.12 kN. (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>V</mi> <mi>A</mi> <msub> <mi>T</mi> <mn>1</mn> </msub> </mrow> </semantics></math>: 13.77 kN. (<b>e</b>) <math display="inline"><semantics> <mrow> <mi>V</mi> <mi>A</mi> <msub> <mi>T</mi> <mrow> <mi>O</mi> <mi>P</mi> <mi>T</mi> </mrow> </msub> </mrow> </semantics></math>: 17.38 kN.</p> Full article ">Figure 10
<p>The first five buckling modes of optimum VAT laminates. (<b>a</b>) Mode 1: 17.38 kN. (<b>b</b>) Mode 2: 27.38 kN. (<b>c</b>) Mode 3: 49.77 kN. (<b>d</b>) Mode 4: 57.36 kN. (<b>e</b>) Mode 5: 71.72 kN.</p> Full article ">Figure 11
<p>Distribution of variables and first critical buckling load, <math display="inline"><semantics> <msub> <mi>T</mi> <mn>0</mn> </msub> </semantics></math>-<math display="inline"><semantics> <msub> <mi>T</mi> <mn>1</mn> </msub> </semantics></math>; first buckling load—<math display="inline"><semantics> <msub> <mi>T</mi> <mn>0</mn> </msub> </semantics></math>, and first buckling load—<math display="inline"><semantics> <msub> <mi>T</mi> <mn>1</mn> </msub> </semantics></math>.</p> Full article ">Figure 12
<p>Contour plots and response surfaces based on GA and LHS for <math display="inline"><semantics> <msub> <mi>T</mi> <mn>0</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>T</mi> <mn>1</mn> </msub> </semantics></math> as the variables and first critical buckling load as the response. (<b>a</b>) GA contour plot of variables’ sample points (<math display="inline"><semantics> <msub> <mi>F</mi> <mrow> <mi>c</mi> <mi>r</mi> </mrow> </msub> </semantics></math> is represented by the colors). (<b>b</b>) LHS contour plot of variables’ sample points (<math display="inline"><semantics> <msub> <mi>F</mi> <mrow> <mi>c</mi> <mi>r</mi> </mrow> </msub> </semantics></math> is represented by the colors).</p> Full article ">Figure 13
<p>First natural frequency modes in various layup designs. (<b>a</b>) QI. (<b>b</b>) CS1. (<b>c</b>) CS2. (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>V</mi> <mi>A</mi> <msub> <mi>T</mi> <mn>1</mn> </msub> </mrow> </semantics></math>. (<b>e</b>) <math display="inline"><semantics> <mrow> <mi>V</mi> <mi>A</mi> <msub> <mi>T</mi> <mrow> <mi>O</mi> <mi>P</mi> <mi>T</mi> </mrow> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 14
<p>The first five natural frequency modes. (<b>a</b>) Mode 1: 513.15 Hz (<b>b</b>) Mode 2: 809.30 Hz. (<b>c</b>) Mode 3: 1135.00 Hz. (<b>d</b>) Mode 4: 1312.00 Hz (<b>e</b>) Mode 5: 1400.00 Hz.</p> Full article ">Figure 15
<p>Distribution of variables and first natural frequency; first natural frequency—<math display="inline"><semantics> <msub> <mi>T</mi> <mn>0</mn> </msub> </semantics></math>, first natural frequency—<math display="inline"><semantics> <msub> <mi>T</mi> <mn>1</mn> </msub> </semantics></math>.</p> Full article ">Figure 16
<p>During implementation of the GA technique in a free vibration issue, the contour plot and response surface reveal the impacts of variables (fiber orientation angles). (<b>a</b>) Colors represent the <math display="inline"><semantics> <msub> <mi>f</mi> <mn>1</mn> </msub> </semantics></math> in this contour plot of variables. (<b>b</b>) Response surface.</p> Full article ">
19 pages, 2260 KiB  
Article
Statistical Analysis of Dynamic Subgrid Modeling Approaches in Large Eddy Simulation
by Mohammad Khalid Hossen, Asokan Mulayath Variyath and Jahrul M. Alam
Aerospace 2021, 8(12), 375; https://doi.org/10.3390/aerospace8120375 - 3 Dec 2021
Cited by 8 | Viewed by 2855
Abstract
In large eddy simulation (LES) of turbulent flows, dynamic subgrid models would account for an average cascade of kinetic energy from the largest to the smallest scales of the flow. Yet, it is unclear which of the most critical dynamical processes can ensure [...] Read more.
In large eddy simulation (LES) of turbulent flows, dynamic subgrid models would account for an average cascade of kinetic energy from the largest to the smallest scales of the flow. Yet, it is unclear which of the most critical dynamical processes can ensure the criterion mentioned above. Furthermore, evidence of vortex stretching being the primary mechanism of the cascade is not out of the question. In this article, we study essential statistical characteristics of vortex stretching. Our numerical results demonstrate that vortex stretching rate provides the energy dissipation rate necessary for modeling subgrid-scale turbulence. We have compared the interaction of subgrid stresses with the filtered quantities among four models using invariants of the velocity gradient tensor. The individual and the joint probability of vortex stretching and strain amplification show that vortex stretching rate is highly correlated with the energy cascade rate. Sheet-like flow structures are correlated with viscous dissipation, and vortex tubes are more stretched than compressed. The overall results indicate that the stretching mechanism extracts energy from the large-scale straining motion and passes it onto small-scale stretched vortices. Full article
(This article belongs to the Special Issue Large Eddy Simulation in Aerospace Engineering)
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<p>A comparison of the time history of the velocity gradient skewness <math display="inline"><semantics> <msub> <mi>S</mi> <mn>0</mn> </msub> </semantics></math> among four subgrid models.</p> Full article ">Figure 2
<p>Comparisons of the second moment of the velocity field among 4 subgrid models. (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>E</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>/</mo> <mi>E</mi> <mo>(</mo> <mn>0</mn> <mo>)</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <msup> <mi>t</mi> <mrow> <mo>−</mo> <mn>10</mn> <mo>/</mo> <mn>7</mn> </mrow> </msup> </semantics></math>. (<b>b</b>) <math display="inline"><semantics> <msub> <mi>k</mi> <mi>sgs</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msup> <mi>t</mi> <mrow> <mo>−</mo> <mn>10</mn> <mo>/</mo> <mn>7</mn> </mrow> </msup> </semantics></math>. (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>E</mi> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> </semantics></math> at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>/</mo> <mi>T</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <msup> <mi>k</mi> <mrow> <mo>−</mo> <mn>5</mn> <mo>/</mo> <mn>3</mn> </mrow> </msup> </semantics></math>.</p> Full article ">Figure 2 Cont.
<p>Comparisons of the second moment of the velocity field among 4 subgrid models. (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>E</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>/</mo> <mi>E</mi> <mo>(</mo> <mn>0</mn> <mo>)</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <msup> <mi>t</mi> <mrow> <mo>−</mo> <mn>10</mn> <mo>/</mo> <mn>7</mn> </mrow> </msup> </semantics></math>. (<b>b</b>) <math display="inline"><semantics> <msub> <mi>k</mi> <mi>sgs</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msup> <mi>t</mi> <mrow> <mo>−</mo> <mn>10</mn> <mo>/</mo> <mn>7</mn> </mrow> </msup> </semantics></math>. (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>E</mi> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> </semantics></math> at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>/</mo> <mi>T</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <msup> <mi>k</mi> <mrow> <mo>−</mo> <mn>5</mn> <mo>/</mo> <mn>3</mn> </mrow> </msup> </semantics></math>.</p> Full article ">Figure 3
<p>A plot of the time series of the rate of change of the resolved energy, <math display="inline"><semantics> <mrow> <mi>d</mi> <mi>E</mi> <mo>/</mo> <mi>d</mi> <mi>t</mi> </mrow> </semantics></math> (block …), the energy flux <math display="inline"><semantics> <mrow> <mo>〈</mo> <msub> <mi>τ</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <msub> <mi mathvariant="script">S</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>〉</mo> </mrow> </semantics></math> (black <math display="inline"><semantics> <mrow> <mo>−</mo> <mo>−</mo> <mo>−</mo> </mrow> </semantics></math>), the viscous dissipation rate <math display="inline"><semantics> <mi>ϵ</mi> </semantics></math> (red <math display="inline"><semantics> <mrow> <mo>−</mo> <mo>−</mo> <mo>−</mo> </mrow> </semantics></math>), and the mean enstrophy <math display="inline"><semantics> <mrow> <mo>〈</mo> <msup> <mi>ω</mi> <mn>2</mn> </msup> <mo>〉</mo> </mrow> </semantics></math> (black ——).</p> Full article ">Figure 4
<p>(<b>a</b>) A schematic illustration of the invariant map <math display="inline"><semantics> <mrow> <mo>(</mo> <msup> <mi>R</mi> <mi mathvariant="script">G</mi> </msup> <mo>,</mo> <msup> <mi>Q</mi> <mi mathvariant="script">G</mi> </msup> <mo>)</mo> </mrow> </semantics></math>, which illustrates that the vorticity dominates over the rate of strain in the region <math display="inline"><semantics> <mrow> <mi>D</mi> <mo>&gt;</mo> <mn>0</mn> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>D</mi> <mo>≡</mo> <mrow> <mo>(</mo> <mn>27</mn> <mo>/</mo> <mn>4</mn> <mo>)</mo> </mrow> <msup> <mrow> <mo>(</mo> <msup> <mi>R</mi> <mi mathvariant="script">G</mi> </msup> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msup> <mi>Q</mi> <mi mathvariant="script">G</mi> </msup> <mo>)</mo> </mrow> <mn>3</mn> </msup> </mrow> </semantics></math> [<a href="#B34-aerospace-08-00375" class="html-bibr">34</a>]. (<b>b</b>) Isosurface plot of <math display="inline"><semantics> <mrow> <mn>20</mn> <mo>%</mo> </mrow> </semantics></math> positive deviation of <math display="inline"><semantics> <msup> <mi>Q</mi> <mi mathvariant="script">G</mi> </msup> </semantics></math>, which is colored by <math display="inline"><semantics> <msup> <mi>R</mi> <mi mathvariant="script">G</mi> </msup> </semantics></math> for a turbulent flow simulated on <math display="inline"><semantics> <msup> <mn>256</mn> <mn>3</mn> </msup> </semantics></math> grid points using the vortex stretching-based model SGS-A, Equation (<a href="#FD9-aerospace-08-00375" class="html-disp-formula">9</a>).</p> Full article ">Figure 5
<p>(<b>a</b>) A schematic illustration of mesokurtic (Kurtosis = 3), leptokurtic (kurtosis &gt; 3) and platykurtic (kurtosis &lt; 3) distribution. (<b>b</b>–<b>e</b>) A comparison of the probability density function of <math display="inline"><semantics> <msup> <mi mathvariant="script">R</mi> <mi>G</mi> </msup> </semantics></math> – the third invariant of the velocity gradient tensor <math display="inline"><semantics> <mi mathvariant="script">G</mi> </semantics></math> – among the subgrid models SGS-A (<b>b</b>), SGS-B (<b>c</b>), SGS-C (<b>d</b>), and SGS-D (<b>e</b>) (see <a href="#aerospace-08-00375-t001" class="html-table">Table 1</a>).</p> Full article ">Figure 6
<p>A comparison of the probability density function of <math display="inline"><semantics> <msup> <mi mathvariant="script">Q</mi> <mi>G</mi> </msup> </semantics></math>, the second invariant of the velocity gradient tensor <math display="inline"><semantics> <mi mathvariant="script">G</mi> </semantics></math> with respect to subgrid models SGS-A (<b>a</b>), SGS-B (<b>b</b>), SGS-C (<b>c</b>), and SGS-D (<b>d</b>) (see <a href="#aerospace-08-00375-t001" class="html-table">Table 1</a>).</p> Full article ">Figure 7
<p>A comparison of the joint probability density function of two invariants <math display="inline"><semantics> <msup> <mi mathvariant="script">Q</mi> <mi>G</mi> </msup> </semantics></math> and <math display="inline"><semantics> <msup> <mi mathvariant="script">R</mi> <mi>G</mi> </msup> </semantics></math> of the velocity gradient tensor <math display="inline"><semantics> <mi mathvariant="script">G</mi> </semantics></math> with respect to subgrid models SGS-A (<b>a</b>), SGS-B (<b>b</b>), SGS-C (<b>c</b>), and SGS-D (<b>d</b>) (see <a href="#aerospace-08-00375-t001" class="html-table">Table 1</a>).</p> Full article ">Figure 8
<p>A comparison of the joint probability density function of two invariants <math display="inline"><semantics> <msup> <mi mathvariant="script">R</mi> <mi>S</mi> </msup> </semantics></math> and <math display="inline"><semantics> <msup> <mi mathvariant="script">Q</mi> <mi>S</mi> </msup> </semantics></math> of the strain rate tensor <math display="inline"><semantics> <mi mathvariant="script">S</mi> </semantics></math> with respect to subgrid models SGS-A (<b>a</b>), SGS-B (<b>b</b>), SGS-C (<b>c</b>), and SGS-D (<b>d</b>).</p> Full article ">Figure 9
<p>The joint probability density of second invariants <math display="inline"><semantics> <msub> <mo>∑</mo> <mi>ω</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi mathvariant="script">Q</mi> <mi>ω</mi> </msub> </semantics></math> (i.e., <math display="inline"><semantics> <msup> <mi mathvariant="script">Q</mi> <mi>R</mi> </msup> </semantics></math>) of two tensors <math display="inline"><semantics> <mi mathvariant="script">S</mi> </semantics></math> and <math display="inline"><semantics> <mi mathvariant="script">R</mi> </semantics></math>, respectively, where the results are compared among 4 subgrid models: SGS-A (<b>a</b>), SGS-B (<b>b</b>), SGS-C (<b>c</b>), and SGS-D (<b>d</b>).</p> Full article ">
15 pages, 8468 KiB  
Article
A Method for Aero-Engine Gas Path Anomaly Detection Based on Markov Transition Field and Multi-LSTM
by Langfu Cui, Chaoqi Zhang, Qingzhen Zhang, Junle Wang, Yixuan Wang, Yan Shi, Cong Lin and Yang Jin
Aerospace 2021, 8(12), 374; https://doi.org/10.3390/aerospace8120374 - 2 Dec 2021
Cited by 9 | Viewed by 3275
Abstract
There are some problems such as uncertain thresholds, high dimension of monitoring parameters and unclear parameter relationships in the anomaly detection of aero-engine gas path. These problems make it difficult for the high accuracy of anomaly detection. In order to improve the accuracy [...] Read more.
There are some problems such as uncertain thresholds, high dimension of monitoring parameters and unclear parameter relationships in the anomaly detection of aero-engine gas path. These problems make it difficult for the high accuracy of anomaly detection. In order to improve the accuracy of aero-engine gas path anomaly detection, a method based on Markov Transition Field and LSTM is proposed in this paper. The correlation among high-dimensional QAR data is obtained based on Markov Transition Field and hierarchical clustering. According to the correlation analysis of high-dimensional QAR data, a multi-input and multi-output LSTM network is constructed to realize one-step rolling prediction. A Gaussian mixture model of the residuals between predicted value and true value is constructed. The three-sigma rule is applied to detect outliers based on the Gaussian mixture model of the residuals. The experimental results show that the proposed method has high accuracy for aero-engine gas path anomaly detection. Full article
(This article belongs to the Section Aeronautics)
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<p>Aero-engine: (<b>a</b>) Diagram of the aero-engine model; (<b>b</b>) Gas path system.</p> Full article ">Figure 2
<p>Monitoring data of QAR aero-engine gas path in whole flight phase: (<b>a</b>) Temperature of precooler; (<b>b</b>) Speed of engine N1; (<b>c</b>) Speed of engine N2; (<b>d</b>) Pressure of precooler.</p> Full article ">Figure 3
<p>QAR data of aero-engine gas path in cruise phase: (<b>a</b>) Temperature of precooler in cruise phase; (<b>b</b>) Speed of engine N1 in cruise phase; (<b>c</b>) Speed of engine N2 in cruise phase; (<b>d</b>) Pressure of precooler in cruise phase.</p> Full article ">Figure 4
<p>Transition Process of Markov Transition Field.</p> Full article ">Figure 5
<p>Results of Markov Transition Field Analysis: (<b>a</b>) Markov Transition Field analysis of Precool_TMP1; (<b>b</b>) Markov Transition Field analysis of N11; (<b>c</b>) Markov Transition Field analysis of N21; (<b>d</b>) Markov Transition Field analysis of SAT; (<b>e</b>) Markov Transition Field analysis of ALT_STD; (<b>f</b>) Markov Transition Field analysis of PRS1.</p> Full article ">Figure 6
<p>The hierarchical clustering results.</p> Full article ">Figure 7
<p>Multi-LSTM network.</p> Full article ">Figure 8
<p>LSTM prediction flow chart.</p> Full article ">Figure 9
<p>Anomaly Detection Process Based on LSTM and Gaussian Distribution.</p> Full article ">Figure 10
<p>The residual probability distribution based on the Gaussian mixture model: (<b>a</b>) the residual probability distribution function of TMP1; (<b>b</b>) the residual probability distribution function of TMP2; (<b>c</b>) the residual probability distribution function of N11; (<b>d</b>) the residual probability distribution function of N12; (<b>e</b>) the residual probability distribution function of N21; (<b>f</b>) the residual probability distribution function of N22; (<b>g</b>) the residual probability distribution function of PRS1; (<b>h</b>) the residual probability distribution function of PRS2.</p> Full article ">Figure 11
<p>The anomaly detection results of TMP1.</p> Full article ">Figure 12
<p>The anomaly detection results of TMP2.</p> Full article ">Figure 13
<p>The anomaly detection results of PRS1.</p> Full article ">Figure 14
<p>The anomaly detection results of PRS2.</p> Full article ">Figure 15
<p>The anomaly detection results of N11.</p> Full article ">Figure 16
<p>The anomaly detection results of N12.</p> Full article ">Figure 17
<p>The anomaly detection results of N21.</p> Full article ">Figure 18
<p>The anomaly detection results of N22.</p> Full article ">
24 pages, 2252 KiB  
Article
An Unsteady Model for Aircraft Icing Based on Tightly-Coupled Method and Phase-Field Method
by Hao Dai, Chengxiang Zhu, Ning Zhao, Chunling Zhu and Yufei Cai
Aerospace 2021, 8(12), 373; https://doi.org/10.3390/aerospace8120373 - 1 Dec 2021
Cited by 7 | Viewed by 2811
Abstract
An unsteady tightly-coupled icing model is established in this paper to solve the numerical simulation problem of unsteady aircraft icing. The multi-media fluid of air and droplets is regarded as a single medium fluid with variable material properties. Taking the droplet concentration as [...] Read more.
An unsteady tightly-coupled icing model is established in this paper to solve the numerical simulation problem of unsteady aircraft icing. The multi-media fluid of air and droplets is regarded as a single medium fluid with variable material properties. Taking the droplet concentration as the phase parameter and the droplet resistance coefficient as the interphase force, the mass concentration distribution of the droplet is obtained by solving the Cahn–Hilliard equation. Fick’s law is introduced to improve the Cahn–Hilliard equation to predict the droplet shadow zone. On this basis, the procedure of the unsteady numerical simulation method for aircraft icing is established, including grid generation, the dual-time-step method to realize the unsteady calculation of the air and droplet tightly-coupled mixed flow field, and the improved shallow water icing model. Finally, through the comparative analysis of numerical examples, the effectiveness of the new model in predicting the droplet impact characteristics and the droplet shadow zone are verified. Compared with other icing models, the ice shapes predicted by the unsteady tightly-coupled model were found to be the most consistent with the experiments. In the icing comparison conditions in this manuscript, the prediction accuracy of the ice thickness at the stagnation point of the leading edge was up to 35% higher than that of LEWICE. Full article
(This article belongs to the Section Aeronautics)
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Figure 1

Figure 1
<p>Framework for the quasi-steady icing simulation.</p> Full article ">Figure 2
<p>Framework for the unsteady icing simulation.</p> Full article ">Figure 3
<p>Region division of the computational grid.</p> Full article ">Figure 4
<p>The computational grid. The red grid is the sectorial domain.</p> Full article ">Figure 5
<p>The force diagram of droplets and air in the unit.</p> Full article ">Figure 6
<p>Diagram of water film.</p> Full article ">Figure 7
<p>Droplet volume fraction diagram.</p> Full article ">Figure 8
<p>Droplet collection coefficient of the SSD-case.</p> Full article ">Figure 9
<p>Droplet collection coefficient of the SLD-case. The normal model is the Eulerian method without droplet deformation, breaking, splashing, bouncing and spreading.</p> Full article ">Figure 10
<p>Comparison of calculation result and experimental result in droplet shadow zone. The area surrounded by the red dot is the droplet shadow zone obtained from the experiment [<a href="#B42-aerospace-08-00373" class="html-bibr">42</a>], and the area surrounded by the blue line is the droplet shadow zone predicted by the tightly-coupled model.</p> Full article ">Figure 11
<p>Liquid water content calculated by the Eulerian model and droplet trajectory by the Lagrangian method.</p> Full article ">Figure 12
<p>Liquid water content calculated by the tightly-coupled method.</p> Full article ">Figure 13
<p>Droplet shadow zone at the airfoil trailing edge. The blue area is the droplet shadow zone where the liquid water content is considered to be 0.</p> Full article ">Figure 14
<p>Ice shape characterization metrics.</p> Full article ">Figure 15
<p>Comparison of ice shapes.</p> Full article ">Figure 16
<p>Ice shapes of SSD cases.</p> Full article ">Figure 17
<p>Ice shapes of SLD cases.</p> Full article ">Figure 18
<p>Comparison of ice shapes under Case 1 (Temperature = −9.9 °C).</p> Full article ">Figure 19
<p>Comparison of ice shapes under Case 5 (Temperature = −19.6 °C).</p> Full article ">Figure 20
<p>Enlarged view of the comparison of ice shapes at the leading edge of an airfoil.</p> Full article ">Figure 21
<p>Distribution of the droplet collection coefficient at the initial time.</p> Full article ">Figure 22
<p>Distribution of the droplet collection coefficient at <span class="html-italic">t</span> = 804 s.</p> Full article ">
17 pages, 4697 KiB  
Article
Design and Simulation of a Flexible Bending Actuator for Solar Sail Attitude Control
by Meilin Liu, Zihao Wang, Daiki Ikeuchi, Junyu Fu and Xiaofeng Wu
Aerospace 2021, 8(12), 372; https://doi.org/10.3390/aerospace8120372 - 1 Dec 2021
Cited by 7 | Viewed by 3372
Abstract
This paper presents the design of a flexible bending actuator using shape memory alloy (SMA) and its integration in attitude control for solar sailing. The SMA actuator has advantages in its power-to-weight ratio and light weight. The bending mechanism and models of the [...] Read more.
This paper presents the design of a flexible bending actuator using shape memory alloy (SMA) and its integration in attitude control for solar sailing. The SMA actuator has advantages in its power-to-weight ratio and light weight. The bending mechanism and models of the actuator were designed and developed. A neural network based adaptive controller was implemented to control the non-linear nature of the SMA actuator. The actuator control modules were integrated into the solar sail attitude model with a quaternion PD controller that formed a cascade control. The feasibility and performance of the proposed actuator for attitude control were investigated and evaluated, showing that the actuator could generate 1.5 × 10−3 Nm torque which maneuvered a 1600 m2 CubeSat based solar sail by 45° in 14 h. The results demonstrate that the proposed SMA bending actuator can be effectively integrated in attitude control for solar sailing under moderate external disturbances using an appropriate controller design, indicating the potential of a lighter solar sail for future missions. Full article
(This article belongs to the Section Astronautics & Space Science)
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Figure 1
<p>SMA Wire Block Diagram.</p> Full article ">Figure 2
<p>(<b>a</b>) Bending SMA actuator; (<b>b</b>) actuator configuration [<a href="#B30-aerospace-08-00372" class="html-bibr">30</a>].</p> Full article ">Figure 3
<p>The integration of the actuators and the solar sail.</p> Full article ">Figure 4
<p>Actuator block diagram.</p> Full article ">Figure 5
<p>Deployed Solar Sail with Rotated Membranes.</p> Full article ">Figure 6
<p>Structure of BPNN for Adaptive PID Control.</p> Full article ">Figure 7
<p>Solar Sail Attitude Control Loop.</p> Full article ">Figure 8
<p>PIDNN control for SMA actuator.</p> Full article ">Figure 9
<p>SMA model validation.</p> Full article ">Figure 10
<p>SMA Control Response to Step input.</p> Full article ">Figure 11
<p>SMA control response with small offset to step input.</p> Full article ">Figure 11 Cont.
<p>SMA control response with small offset to step input.</p> Full article ">Figure 12
<p>Martensite fraction change to step input using PIDNN.</p> Full article ">Figure 13
<p>Temperature change to step input using PIDNN.</p> Full article ">Figure 14
<p>SMA actuator response to positive sinusoidal wave.</p> Full article ">Figure 15
<p>SMA control response to sin input (f = 1/600 rad/s).</p> Full article ">Figure 16
<p>SMA control response to sin input (f = 1/400 rad/s).</p> Full article ">Figure 17
<p>SMA control response to sin input (f = 1/300 rad/s).</p> Full article ">Figure 18
<p>Attitude control of a CubeSat solar sail with the sail size of 1600 m<sup>2</sup>.</p> Full article ">Figure 18 Cont.
<p>Attitude control of a CubeSat solar sail with the sail size of 1600 m<sup>2</sup>.</p> Full article ">
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