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Keywords = wind shear

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16 pages, 5958 KiB  
Article
Numerical Simulation of Vertical Cyclic Responses of a Bucket in Over-Consolidated Clay
by Jun Jiang, Chengxi Luo and Dong Wang
J. Mar. Sci. Eng. 2024, 12(8), 1319; https://doi.org/10.3390/jmse12081319 - 4 Aug 2024
Viewed by 343
Abstract
Multi-bucket foundations have become an alternative for large offshore wind turbines, with the expansion of offshore wind energy into deeper waters. The vertical cyclic loading–displacement responses of the individual bucket of the tripod foundation are relevant to the deflection of multi-bucket foundations and [...] Read more.
Multi-bucket foundations have become an alternative for large offshore wind turbines, with the expansion of offshore wind energy into deeper waters. The vertical cyclic loading–displacement responses of the individual bucket of the tripod foundation are relevant to the deflection of multi-bucket foundations and crucial for serviceability design. Finite element analyses are used to investigate the responses of a bucket subjected to symmetric vertical cyclic loading in over-consolidated clay. The Undrained Cyclic Accumulation Model (UDCAM) is adopted to characterize the stress–strain properties of clay, the parameters of which are calibrated through monotonic and cyclic direct simple shear tests. The performance of the finite element (FE) model combined with UDCAM in simulating vertical displacement amplitudes is evaluated by comparison with existing centrifuge tests. Moreover, the impact of the bucket’s aspect ratio on vertical displacement amplitude is investigated through a parametric study. A predictive equation is proposed to estimate the vertical displacement amplitudes of bucket foundations with various aspect ratios, based on the cyclic displacement amplitude of a bucket with an aspect ratio of unity. Full article
(This article belongs to the Special Issue Advances in Marine Geological and Geotechnical Hazards)
Show Figures

Figure 1

Figure 1
<p>Response of multi-bucket foundation subjected to horizontal cyclic loading and definitions of vertical cyclic loading components.</p>
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<p>Process of the methodology.</p>
Full article ">Figure 3
<p>Mesh of the bucket and soil: (<b>a</b>) <span class="html-italic">L</span>/<span class="html-italic">D</span> = 0.5; (<b>b</b>) <span class="html-italic">L</span>/<span class="html-italic">D</span> = 2.</p>
Full article ">Figure 4
<p>Static shear strength from monotonic DSS tests.</p>
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<p>Contour diagrams at <span class="html-italic">τ</span><sub>a</sub> = 0 deduced from cyclic DSS tests: (<b>a</b>) OCR = 2; (<b>b</b>) OCR = 3.</p>
Full article ">Figure 6
<p>Typical <span class="html-italic">γ</span><sub>c</sub> values varying with <span class="html-italic">τ</span><sub>c</sub>/<span class="html-italic">s</span><sub>u</sub> at <span class="html-italic">N</span> = 1, 10, 100, and 1000 (OCR = 3).</p>
Full article ">Figure 7
<p>Monotonic vertical force–displacement curves from FE analyses and tests.</p>
Full article ">Figure 8
<p>Comparison of cyclic displacement amplitudes by centrifuge test and FE: (<b>a</b>) <span class="html-italic">V</span><sub>c</sub>/<span class="html-italic">V</span><sub>0</sub> = 0.37, 0.51, and 0.58; (<b>b</b>) <span class="html-italic">V</span><sub>c</sub>/<span class="html-italic">V</span><sub>0</sub> = 0.42, 0.53, and 0.64.</p>
Full article ">Figure 9
<p>Comparison of cyclic loading amplitudes with <span class="html-italic">N</span> at different <span class="html-italic">L</span>/<span class="html-italic">D</span> values.</p>
Full article ">Figure 10
<p>Displacement contours at <span class="html-italic">N</span> = 300 for Case 1: (<b>a</b>) <span class="html-italic">L</span>/<span class="html-italic">D</span> = 0.5; (<b>b</b>) <span class="html-italic">L</span>/<span class="html-italic">D</span> = 1; (<b>c</b>) <span class="html-italic">L</span>/<span class="html-italic">D</span> = 1.5; (<b>d</b>) <span class="html-italic">L</span>/<span class="html-italic">D</span> = 2.</p>
Full article ">Figure 11
<p>Comparison of <span class="html-italic">w</span><sub>c</sub>/<span class="html-italic">L</span> by FE and Equation (2) at <span class="html-italic">D</span> = 4 m and various <span class="html-italic">L</span>/<span class="html-italic">D</span> values.</p>
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<p>Performance of Equation (2) for cases in <a href="#jmse-12-01319-t002" class="html-table">Table 2</a>: (<b>a</b>) Cases A and B; (<b>b</b>) Cases C and D; (<b>c</b>) Cases E and F.</p>
Full article ">Figure 12 Cont.
<p>Performance of Equation (2) for cases in <a href="#jmse-12-01319-t002" class="html-table">Table 2</a>: (<b>a</b>) Cases A and B; (<b>b</b>) Cases C and D; (<b>c</b>) Cases E and F.</p>
Full article ">
18 pages, 7479 KiB  
Article
The Role of Tide and Wind in Modulating Density Stratification in the Pearl River Estuary during the Dry Season
by Lei Zhu, Jiangchuan Sheng and Liwen Pang
J. Mar. Sci. Eng. 2024, 12(8), 1241; https://doi.org/10.3390/jmse12081241 - 23 Jul 2024
Viewed by 445
Abstract
Density stratification plays a crucial role in estuarine hydrodynamics and material transport. In this study, we utilized a well-calibrated numerical model to investigate the stratification processes and underlying mechanisms in the dynamically wide Pearl River Estuary (PRE). In the upper estuary, longitudinal straining [...] Read more.
Density stratification plays a crucial role in estuarine hydrodynamics and material transport. In this study, we utilized a well-calibrated numerical model to investigate the stratification processes and underlying mechanisms in the dynamically wide Pearl River Estuary (PRE). In the upper estuary, longitudinal straining governs stratification, enhancing it during ebb tide and reducing it during flood tide. The Coriolis force becomes significant in the lower estuary due to the increased basin width, causing seaward freshwater to be confined to the West Shoal, where a pronounced transverse salinity gradient forms. Interacting with lateral current shear, density stratification is most pronounced in this region. The prevailing northeasterly wind creates a mixed layer near the surface, shifting stratification to the middle layer of the water column in the upper estuary. Wind stirring reduces stratification throughout the estuary. Under the wind’s influence, the seaward outflow is confined to a narrower region and shifts westward, resulting in the most apparent stratification occurring on the West Shoal of the PRE due to lateral straining. These findings on the evolution of freshwater pathways and their role in modulating density stratification have significant implications for other wide estuaries, such as Delaware Bay and the La Plata-Parana estuary. Full article
(This article belongs to the Section Physical Oceanography)
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Figure 1

Figure 1
<p>(<b>a</b>) Large-scale numerical model of the Pearl River Delta and location of the Pearl River Estuary (PRE) (1. Humen; 2. Jiaomen; 3. Hongqili; 4. Hengmen; 5. Modaomen; 6. Jitimen; 7. Hutiaomen; 8. Yamen). There are three estuaries in the Pearl River Delta, including the PRE, the Modaomen estuary (ME), and the Huangmaohai estuary (HE). (<b>b</b>) Bathymetry of the PRE (triangles denote the tidal gauge stations, and red stars, labeled from S1 to S6, are the measurement sites). Abbreviations: <b>LTI</b> Lantau Island, <b>QAI</b> Qi’ao Island.</p>
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<p>(<b>a</b>) Freshwater discharge from the headwater during January 2013, (<b>b</b>) Tidal elevation near the estuary mouth, and (<b>c</b>) Wind data during the simulation period. The data was obtained from the Climate Forecast System Reanalysis (National Centers for Environmental Prediction).</p>
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<p>(<b>a</b>) The comparisons of tidal and non-tidal water levels (dashed blue lines denote observation and solid red lines denote simulated results) for stations QUB (<b>a</b>,<b>b</b>) and SPW (<b>c</b>,<b>d</b>).</p>
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<p>(<b>a</b>–<b>l</b>) Comparisons of the observed (blue circles) and modeled (red line) current speed and direction at stations S1, S3, and S6. The first and second rows denote the current speed and direction at the surface layer, and the third and last rows are the results at the bottom layer.</p>
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<p>Comparisons of the observed (blue circles) and modeled (red line) current speed and direction at stations S1 (<b>a</b>,<b>b</b>), S3 (<b>c</b>,<b>d</b>) and S6 (<b>e</b>,<b>f</b>). The left and right columns denote the results for the surface and bottom layers, respectively.</p>
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<p>The depth-averaged subtidal flow and tidal-averaged salinity during the neap tide (<b>a</b>,<b>c</b>) and the spring tide (<b>b</b>,<b>d</b>) for simulations without wind (<b>a</b>,<b>b</b>) and with wind (<b>c</b>,<b>d</b>). The gray lines denote the 10, 20, and 30-psu isolines.</p>
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<p>Horizontal distribution of squared buoyancy frequency (shown in log10 scale) and depth-averaged velocity ((<b>a</b>–<b>d</b>) neap tide without wind; (<b>e</b>–<b>h</b>) neap tide with wind; (<b>i</b>–<b>l</b>) spring tide without wind; (<b>m</b>–<b>p</b>) spring tide with wind; from left to right: peak flood, flood slack, peak ebb, and ebb slack). The gray lines denote the 10, 20, and 30-psu isolines. Two transverse sections (green and black dashed liens shown in (<b>e</b>)) were selected to further investigate the stratification process.</p>
Full article ">Figure 8
<p>Transverse distribution of lateral flow (vectors), squared buoyancy frequency (contours, shown in log10 scale), and salinity (gray lines) in the upper transect ((<b>a</b>–<b>d</b>) neap tide without wind; (<b>e</b>–<b>h</b>) spring tide without wind; (<b>i</b>–<b>l</b>) neap tide with wind; (<b>m</b>–<b>p</b>) spring tide with wind; from left to right: peak flood, flood slack, peak ebb, and ebb slack).</p>
Full article ">Figure 9
<p>Same as <a href="#jmse-12-01241-f008" class="html-fig">Figure 8</a>, but in the lower transect.</p>
Full article ">Figure 10
<p>Subtidal water flux (averaged within 15 days, the first half period of the simulation) in salinity-distance space (solid lines denote the results with wind, while the dashed lines are the results without wind; the values of water flux in the figure are divided by 1000 m<sup>3</sup>/s for clarity; positive values denote net landward transport of water mass while negative values denote seaward transport; the flow direction for scenario without wind is denoted by dashed arrows; and the solid arrows denote the flow direction for scenario with wind forcing; the starting point in the figure is located at the estuary mouth between Hongkong and Macau and the endpoint is located at Humen Outlet).</p>
Full article ">Figure 11
<p>Time series of integrated terms (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>) and time series of the integrated longitudinal and lateral strainings (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>) in the upper estuary (<b>a</b>,<b>b</b>,<b>e</b>,<b>f</b>) and lower estuary (<b>c</b>,<b>d</b>,<b>g</b>,<b>h</b>). The first two rows are the results for cases without wind, and the last two rows denote the results for cases with wind. A positive value represents the tendency to increase stratification.</p>
Full article ">Figure 12
<p>A conceptual diagram to illustrate the effects of northeasterly wind on stratification in a convergent estuary. (<b>a</b>) Schematic topography of a convergent estuary, with an arrow showing the wind direction. The estuarine circulation exhibits a vertically sheared structure in the upper estuary ((<b>b</b>) without wind; (<b>c</b>) with wind), with landward flow at the bottom (denoted by cross) and seaward flow directing seaward (denoted by dot). The structure of estuarine circulation turns to a transverse one in the lower estuary ((<b>d</b>) without wind; (<b>e</b>) with wind), with landward flow in the right part of the estuary and seaward flow presenting in the left of the estuary. The isopycnals are shown by dashed lines, with density <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ρ</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> &lt; <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ρ</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> &lt; <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ρ</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math>. The arrows denote the lateral flow.</p>
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23 pages, 6958 KiB  
Article
Effect of Tropical Cyclone Wind Forcing on Improving Upper Ocean Simulation: An Idealized Study
by Xinxin Yue and Biao Zhang
Remote Sens. 2024, 16(14), 2574; https://doi.org/10.3390/rs16142574 - 13 Jul 2024
Viewed by 673
Abstract
We examined how wind forcing affects the upper ocean response under idealized tropical cyclone (TC) conditions. In this study, we constructed three parameterized wind fields with varying spatial and temporal resolutions for TCs of different intensities and translation speeds. The simulated surface and [...] Read more.
We examined how wind forcing affects the upper ocean response under idealized tropical cyclone (TC) conditions. In this study, we constructed three parameterized wind fields with varying spatial and temporal resolutions for TCs of different intensities and translation speeds. The simulated surface and subsurface temperatures were cooler and deeper when using the blended wind fields owing to their higher wind speeds compared to those from coarse–resolution wind fields. Additionally, TC–induced currents were significantly stronger on the right side of the TC track, with notable differences in current velocities. Similar to the increase in ocean currents, the simulated turbulent kinetic energy driven by the blended winds is significantly higher than that simulated by the coarse-resolution wind fields. These findings suggest that using high-quality wind fields to drive ocean models can enhance the accuracy of the upper ocean response to TCs. The sensitivity of the upper ocean responses to wind forcing depends on the TC’s intensity and translation speed. Stronger and slower-moving TCs induce greater vertical shear and enhanced mixing. Therefore, accurate wind stress as a surface boundary condition is crucial for numerical ocean models. Full article
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Domain configuration for all idealized experiments. The dashed line at 130°E represents the idealized tropical cyclone (TC) track. The red dot at the bottom represents the initial TC center, moving with a translation speed of 10 m/s. The blue arrow represents the propagation direction, while the red dot at the top represents the TC center at model time 72 h for the fast-moving TC. The light blue box represents the approximate area of impact for fast-moving TCs. Initial vertical profiles of (<b>b</b>) temperature, (<b>c</b>) salinity, and (<b>d</b>) density in the upper 200 m. The black dots represent the sigma levels used in the Regional Ocean Modeling System (ROMS).</p>
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<p>Spatial distribution of Category 3–5 Typhoons (referred to as STY) parameterized surface winds at model time 72 h for (<b>a</b>) the high-resolution TC parameterized asymmetric wind field (W1) with a spatial resolution of 1 km, (<b>b</b>) low-resolution TC parameterized asymmetric wind field (W2) with a spatial resolution of 25 km, and (<b>c</b>) blended winds (W3) between W1 and W2 with a spatial resolution of 8 km. RMW represents the radius of the maximum wind speed, TSP represents the translation speed, and MWS represents the maximum wind speed. (<b>d</b>) Parametric wind profiles showing the wind speed of W1. The black, blue, and red lines represent Tropical Storm (TS), Category 1–2 Typhoons (referred to as TY), and STY, respectively.</p>
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<p>Horizontal distributions of SST cooling (°C) at model time 72 h for (<b>a</b>) TS, (<b>b</b>) TY, and (<b>c</b>) STY driven by the W1 winds. Horizontal distributions of ocean currents (m/s) at model time 72 h for (<b>d</b>) TS, (<b>e</b>) TY, and (<b>f</b>) STY forced by the W1 winds. Horizontal distributions of turbulent kinetic energy (TKE) (<math display="inline"><semantics> <mrow> <mrow> <mrow> <msup> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </mrow> <mo>/</mo> <mrow> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </mrow> </mrow> </mrow> </semantics></math>) at model time 72 h for (<b>g</b>) TS, (<b>h</b>) TY, and (<b>i</b>) STY forced by the W1 winds. The TC has a northward translation speed of 5 m/s, with RMW values of 66 km for TS, 35 km for TY, and 25 km for STY. The concentric dashed-line cycles represent the RMW, twice the RMW (2RMW), and four times the RMW (4RMW). The blue dashed lines indicate the TC track, and the red dots denote the TC center at model time 72 h.</p>
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<p>Vertical distributions of the subsurface temperature differences (°C) at model time 72 h for (<b>a</b>) TS, (<b>b</b>) TY, and (<b>c</b>) STY driven by the W1 winds. Vertical distributions of ocean currents (m/s) at model time 72 h for (<b>d</b>) TS, (<b>e</b>) TY, and (<b>f</b>) STY forced by the W1 winds. Vertical distributions of turbulent kinetic energy (TKE) (<math display="inline"><semantics> <mrow> <mrow> <mrow> <msup> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </mrow> <mo>/</mo> <mrow> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </mrow> </mrow> </mrow> </semantics></math>) at model time 72 h for (<b>g</b>) TS, (<b>h</b>) TY, and (<b>i</b>) STY forced by the W1 winds. The TC has a northward translation speed of 5 m/s, with RMW values of 66 km for TS, 35 km for TY, and 25 km for STY. The black dotted lines mark the position of the TC center.</p>
Full article ">Figure 5
<p>Horizontal distributions of the simulated SST cooling difference (°C) with varying intensities of (<b>a</b>,<b>d</b>) TS, (<b>b</b>,<b>e</b>) TY, (<b>c</b>,<b>f</b>) STY. Differences: (<b>a</b>–<b>c</b>) W1 minus W2, and (<b>d</b>–<b>f</b>) W1 minus W3. The concentric dashed-line cycles indicate the RMW, 2RMW, and 4RMW. The blue dashed lines indicate the TC track, and the red dots indicate the TC center at model time 72 h.</p>
Full article ">Figure 6
<p>Vertical distributions of simulated temperature differences (°C) in the east–west direction for (<b>a</b>,<b>d</b>) TS, (<b>b</b>,<b>e</b>) TY, (<b>c</b>,<b>f</b>) STY. Differences: (<b>a</b>–<b>c</b>) W1 minus W2, and (<b>d</b>–<b>f</b>) W1 minus W3. The TC moves northward at 5 m/s. The black dotted lines mark the position of the TC center.</p>
Full article ">Figure 7
<p>Horizontal distributions of simulated near-surface current differences (m/s) with varying intensities of (<b>a</b>,<b>d</b>) TS, (<b>b</b>,<b>e</b>) TY, (<b>c</b>,<b>f</b>) STY. Differences: (<b>a</b>–<b>c</b>) W1 minus W2, and (<b>d</b>–<b>f</b>) W1 minus W3. The northward translation speed of the TC is 5 m/s. Concentric dashed-line circles indicate the RMW, 2RMW, and 4RMW. Blue dashed lines depict the TC track, and red dots indicate the TC center at model time 72 h.</p>
Full article ">Figure 8
<p>Vertical distributions of simulated ocean current differences (m/s) in the east–west direction with varying intensities of (<b>a</b>,<b>d</b>) TS, (<b>b</b>,<b>e</b>) TY, (<b>c</b>,<b>f</b>) STY. Differences: (<b>a</b>–<b>c</b>) W1 minus W2, and (<b>d</b>–<b>f</b>) W1 minus W3. The northward translation speed of the TC is 5 m/s. The black dotted lines mark the position of the TC center.</p>
Full article ">Figure 9
<p>Horizontal distributions of simulated SST cooling differences (°C) with varying translation speeds of (<b>a</b>,<b>d</b>) T3, (<b>b</b>,<b>e</b>) T5, (<b>c</b>,<b>f</b>) T10. Differences: (<b>a</b>–<b>c</b>) W1 minus W2, and (<b>d</b>–<b>f</b>) W1 minus W3. Concentric dashed-line circles indicate RMW, 2RMW, and 4RMW. Blue dashed lines depict the TC track, and red dots indicate the TC center at model time 72 h.</p>
Full article ">Figure 10
<p>Vertical distributions of simulated temperature differences (°C) in the east–west direction for (<b>a</b>,<b>d</b>) T3, (<b>b</b>,<b>e</b>) T5, (<b>c</b>,<b>f</b>) T10. Differences: (<b>a</b>–<b>c</b>) W1 minus W2, and (<b>d</b>–<b>f</b>) W1 minus W3. The black dotted lines mark the position of the TC center.</p>
Full article ">Figure 11
<p>Horizontal distributions of simulated surface current differences (m/s) for (<b>a</b>,<b>d</b>) T3, (<b>b</b>,<b>e</b>) T5, (<b>c</b>,<b>f</b>) T10. Differences: (<b>a</b>–<b>c</b>) W1 minus W2, and (<b>d</b>–<b>f</b>) W1 minus W3. Concentric dashed-line circles indicate RMW, 2RMW, and 4RMW. Blue dashed lines depict the TC track, and red dots indicate the TC center at model time 72 h.</p>
Full article ">Figure 12
<p>Vertical distributions of simulated eastward ocean current differences (m/s) in the east–west direction for (<b>a</b>,<b>d</b>) T3, (<b>b</b>,<b>e</b>) T5, (<b>c</b>,<b>f</b>) T10. Differences: (<b>a</b>–<b>c</b>) W1 minus W2, and (<b>d</b>–<b>f</b>) W1 minus W3. The black dotted lines mark the position of the TC center.</p>
Full article ">Figure 13
<p>The horizontal distributions of simulated TKE (<math display="inline"><semantics> <mrow> <mrow> <mrow> <msup> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </mrow> <mo>/</mo> <mrow> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </mrow> </mrow> </mrow> </semantics></math>) between W1 and W2 winds for (<b>a1</b>) TS with a translation speed of 5 m/s, (<b>b1</b>) TY with a translation speed of 5 m/s, (<b>c1</b>) STY with a translation speed of 5 m/s, (<b>d1</b>) STY with a translation speed of 3 m/s, and (<b>e1</b>) STY with a translation speed of 10 m/s. The horizontal distributions of simulated TKE (<math display="inline"><semantics> <mrow> <mrow> <mrow> <msup> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </mrow> <mo>/</mo> <mrow> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </mrow> </mrow> </mrow> </semantics></math>) between W1 and W3 winds for (<b>a2</b>) TS with a translation speed of 5 m/s, (<b>b2</b>) TY with a translation speed of 5 m/s, (<b>c2</b>) STY with a translation speed of 5 m/s, (<b>d2</b>) STY with a translation speed of 3 m/s, and (<b>e2</b>) STY with a translation speed of 10 m/s. The concentric dashed-line circles indicate the RMW, 2RMW, and 4RMW. The blue dashed lines represent the TC track, while the red dots mark the TC center at 72 h of model time.</p>
Full article ">
24 pages, 12476 KiB  
Article
Conceptual Design and Structural Performance Analysis of an Innovative Deep-Sea Aquaculture Platform
by Yangyang Li, Xingwei Zhen, Yesen Zhu, Yi Huang, Lixin Zhang and Hongxia Li
J. Mar. Sci. Eng. 2024, 12(7), 1058; https://doi.org/10.3390/jmse12071058 - 24 Jun 2024
Viewed by 395
Abstract
This paper proposes a conceptual design of an innovative deep-sea aquaculture platform that integrates a steel structural framework and high-density polyethylene (HDPE) floats. It aims to overcome the limitations of prevailing aquaculture equipment, including inadequate resistance to strong wind and waves, complex technologies, [...] Read more.
This paper proposes a conceptual design of an innovative deep-sea aquaculture platform that integrates a steel structural framework and high-density polyethylene (HDPE) floats. It aims to overcome the limitations of prevailing aquaculture equipment, including inadequate resistance to strong wind and waves, complex technologies, and prohibitively high costs. The design scheme and key parameters of the main platform, the netting system, and the mooring systems are presented. Based on the stochastic design wave method, the characteristic load response scenarios and design wave parameters are determined and analyzed. Strength analysis is conducted to assess the structural performance, vulnerabilities, and overall safety of the platform under various characteristic load conditions. The results indicate that the Von Mises stress levels across different sections of the platform conform to the allowable stress thresholds under various characteristic load conditions. However, the stress levels of the platform are notably higher when subjected to characteristic loads associated with vertical shear, vertical bending moments, and torsion about the horizontal axis, which requires further efforts in the design process to enhance the structural safety of the platform. The proposed design methodology and the presented research results can provide a wide range of references for the design and analysis of deep-sea fisheries aquaculture equipment. Full article
(This article belongs to the Section Ocean Engineering)
Show Figures

Figure 1

Figure 1
<p>Sketch of the innovative deep-sea aquaculture platform.</p>
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<p>Framework and process of the methodology of conceptual design and performance analysis.</p>
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<p>Optimization method for the principal scales of the platform.</p>
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<p>Flowchart of the stochastic design wave method.</p>
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<p>Position of platform section division.</p>
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<p>Structural strength analysis flowchart.</p>
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<p>Schematic of the platform layout scheme.</p>
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<p>Schematic of the platform internal structure of the platform.</p>
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<p>Finite element model and boundary constraint.</p>
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<p>Variation curve of each performance indicator with test factors.</p>
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<p>Distribution of characteristic loads along the y-axis.</p>
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<p>Distribution of characteristic loads along the x-axis.</p>
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<p>Distribution of characteristic loads along the x-axis.</p>
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<p>Characteristic load response transfer function for each condition.</p>
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<p>Stress values and stress distribution under different mesh sizes.</p>
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<p>Stress distribution of platform under LC6 condition (vertical bending moment at x = 3.75 m).</p>
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<p>Results of yield strength calibration.</p>
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24 pages, 14077 KiB  
Article
Spatio-Temporal Variation in Suspended Sediment during Typhoon Ampil under Wave–Current Interactions in the Yangtze River Estuary
by Jie Wang, Cuiping Kuang, Daidu Fan, Wei Xing, Rufu Qin and Qingping Zou
Water 2024, 16(13), 1783; https://doi.org/10.3390/w16131783 - 24 Jun 2024
Viewed by 689
Abstract
Suspended sediment plays a major role in estuary morphological change and shoal erosion and deposition. The impact of storm waves on sediment transport and resuspension in the Yangtze River Estuary (YRE) was investigated using a 3D coupling hydrodynamic-wave model with a sediment transport [...] Read more.
Suspended sediment plays a major role in estuary morphological change and shoal erosion and deposition. The impact of storm waves on sediment transport and resuspension in the Yangtze River Estuary (YRE) was investigated using a 3D coupling hydrodynamic-wave model with a sediment transport model during Typhoon Ampil. This model has been validated in field observations of water level, current, wave, and sediment concentration. The model was run for tide only, tide + wind, tide + wind and wave forcing conditions. It was found that: (1) typhoons can increase the suspended sediment concentration (SSC) by enhancing bed shear stress (BSS), especially in the offshore area of the YRE, and there is hysteresis between SSC and BSS variation; (2) exponential and vertical-line types are the main vertical profile of the SSC in the YRE and typhoons can strengthen vertical mixing and reconstruct the vertical distribution; and (3) waves are the dominating forcing factor for the SSC in the majority of the YRE through wave-induced BSS which releases sediment from the seabed. This study comprehensively investigates the spatio-temporal variation in SSC induced by Typhoon Ampil in the main branch of the YRE, which provides insights into sediment transport and resuspension during severe storms for estuaries around the world. Full article
(This article belongs to the Special Issue Hydrodynamics and Sediment Transport in the Coastal Zone)
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<p>The study area and the storm track of Typhoon Ampil (2018). (<b>a</b>) Overview of the simulation area with the field stations of water level indicated by pink triangles, currents denoted by black solid circles, and the ship measurement point of sediment denoted by the four groups of hollow circles. (<b>b</b>) The focus study area with its main branches; the nine pink solid circles are the main study point in the following analysis and the typhoon path of Ampil. (<b>c</b>) The storm track of Typhoon Ampil moving from the East China Sea in the northwest direction.</p>
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<p>The unstructured grid system used in this study.</p>
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<p>Comparison of simulated and measured water levels during Typhoon Ampil at the Luchaogang, Sheshan, and Dajishan field stations; the <span class="html-italic">skill</span> number and RMSE are shown in the figure.</p>
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<p>Comparison of simulated and measured water levels during Typhoon Ampil at the Luchaogang, Sheshan, and Dajishan field stations; the <span class="html-italic">skill</span> number and RMSE are shown in the figure.</p>
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<p>Validation of wave predictions during Typhoon Ampil at Yangkougang Station.</p>
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<p>Validation of predicted suspended sediment concentration (SSC) using field observations collected by ships at the four groups of measured points indicated in <a href="#water-16-01783-f001" class="html-fig">Figure 1</a>.</p>
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<p>The measured wind velocity, water level, significant wave height, and period collected at Yangkougang Station as marked in <a href="#water-16-01783-f001" class="html-fig">Figure 1</a> during Typhoon Ampil (the time begins at 00:00, 17 July 2018).</p>
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<p>The time series from 20 to 25 July of surface suspended sediment concentration (SSC) at the nine selected study sites (as indicated in the top right corner).</p>
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<p>Bottom shear stress (BSS) variation and four 24 h time-averaged suspended sediment concentration (SSC) histograms before and after typhoon landing at the six selected study sites (the red dotted line represents the maximum critical shear stress).</p>
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<p>Time evolution of horizontal variation in suspended sediment concentration (SSC) during the typhoon with the current vectors indicated by the arrows.</p>
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<p>Time evolution of horizontal variation in bed shear stress (BSS) during Typhoon Ampil.</p>
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<p>Four sections of NBL, NCL, CPL, and SPL along the main branches of the YRE for the analysis of the vertical distribution of suspended sediment during Typhoon Ampil.</p>
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<p>The spatial variation in suspended sediment concentration (SSC) across the section NBL indicated in <a href="#water-16-01783-f012" class="html-fig">Figure 12</a> from 21 to 24 July during Typhoon Ampil with a time interval of 12 h.</p>
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<p>The spatial variation in suspended sediment concentration (SSC) across the section NCL indicated in <a href="#water-16-01783-f012" class="html-fig">Figure 12</a> from 21 to 24 July during Typhoon Ampil with a time interval of 612 h.</p>
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<p>The spatial variation in suspended sediment concentration (SSC) across the section NPL indicated in <a href="#water-16-01783-f012" class="html-fig">Figure 12</a> from 21 to 24 July during Typhoon Ampil with a time interval of 12 h.</p>
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<p>The spatial variation in suspended sediment concentration (SSC) across the section SPL indicated in <a href="#water-16-01783-f012" class="html-fig">Figure 12</a> from 21 to 24 July during Typhoon Ampil with a time interval of 12 h.</p>
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<p>Comparison of predicted suspended sediment concentration (SSC) isoline for WCI (tide + wind + wave) and NW (tide + wind) forcing conditions as described in <a href="#water-16-01783-t003" class="html-table">Table 3</a>.</p>
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<p>The vertical profile of suspended sediment concentration (SSC) form at the nine study sites indicated in <a href="#water-16-01783-f001" class="html-fig">Figure 1</a> at 12:00 on 22 July 2018.</p>
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<p>The difference between the predicted SSC difference (DSSC) distribution for WCI (tide + wind + wave) and AT (tidal only) forcing conditions as described in <a href="#water-16-01783-t003" class="html-table">Table 3</a>.</p>
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<p>Comparison of bed shear stress (BSS) between the WCI (tide + wind + wave) and AT (tide only) conditions at the six study sites in the three main branches indicated in <a href="#water-16-01783-f001" class="html-fig">Figure 1</a>.</p>
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15 pages, 19532 KiB  
Article
Comprehensive Analysis of Factors Underpinning the Superior Performance of Ducted Horizontal-Axis Helical Wind Turbines
by Shaikh Zishan Suheel, Ahmad Fazlizan, Halim Razali, Kok Hoe Wong, Altaf Hossain Molla, Rajkumar Singh Rathore, M. S. Hossain Lipu and Mahidur R. Sarker
Energies 2024, 17(12), 3029; https://doi.org/10.3390/en17123029 - 19 Jun 2024
Viewed by 541
Abstract
The societal and economic reliance on non-renewable energy sources, primarily fossil fuels, has raised concerns about an imminent energy crisis and climate change. The transition towards renewable energy sources faces challenges, notably in understanding turbine shear forces within wind technology. To address this [...] Read more.
The societal and economic reliance on non-renewable energy sources, primarily fossil fuels, has raised concerns about an imminent energy crisis and climate change. The transition towards renewable energy sources faces challenges, notably in understanding turbine shear forces within wind technology. To address this gap, a novel solution emerges in the form of the ducted horizontal-axis helical wind turbine. This innovative design aims to improve airflow dynamics and mitigate adverse forces. Computational fluid dynamics and experimental assessments were employed to evaluate its performance. The results indicate a promising technology, showcasing the turbine’s potential to harness energy from diverse wind sources. The venturi duct aided in the augmentation of the velocity, thereby increasing the maximum energy content of the wind by 179.16%. In addition, 12.16% of the augmented energy was recovered by the turbine. Notably, the integration of a honeycomb structure demonstrated increased revolutions per minute (RPM) by rectifying the flow and reducing the circular wind, suggesting the impact of circular wind components on turbine performance. The absence of the honeycomb structure allows the turbine to encounter more turbulent wind (circular wind), which is the result of the movement of the fan. Strikingly, the downwash velocity of the turbine was observed to be equal to the incoming velocity, suggesting the absence of an axial induction factor and, consequently, no back force on the system. However, limitations persist in the transient modelling and in determining optimal performance across varying wind speeds due to experimental constraints. Despite these challenges, this turbine marks a significant stride in wind technology, highlighting its adaptability and potential for heightened efficiency, particularly at higher speeds. Further refinement and exploration are imperative for broadening the turbine’s application in renewable energy generation. This research emphasizes the turbine’s capacity to adapt to different wind velocities, signaling a promising avenue for more efficient and sustainable energy production. Full article
(This article belongs to the Section A3: Wind, Wave and Tidal Energy)
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<p>Technical specifications of the convergent and divergent duct.</p>
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<p>Technical specifications of the honeycomb structure.</p>
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<p>(<b>a</b>) The meshing and geometry of the helical turbine; (<b>b</b>) meshing and twist angle of the turbine in COMSOL; (<b>c</b>) the geometry of the complete system.</p>
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<p>Grid study.</p>
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<p>(<b>a</b>) Velocity measurement points throughout the test rig. (<b>b</b>) Points of measurement of velocity across various cross-sections.</p>
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<p>Pressure and velocity relationship in an empty diffuser.</p>
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<p>Velocity and pressure profiles generated from the CFD studies (<b>a</b>) Velocity Streamlines throughout the system and around the turbine; (<b>b</b>) velocity slice at point 0 on the grid and the pressure contour on the turbine surface.</p>
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<p>(<b>a</b>) Average velocity profile along the test rig. (<b>b</b>) Average velocities across the test rig.</p>
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<p>Cp vs. TSR graph at different velocities.</p>
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<p>(<b>a</b>) Power vs. resistance graph at various speeds; (<b>b</b>) power vs. RPM graph at different velocities.</p>
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<p>Torque vs. RPM graph at different velocities.</p>
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<p>Torque coefficient vs. TSR graph at different velocities.</p>
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21 pages, 12213 KiB  
Article
A 3D Numerical Model to Estimate Lightning Types for PyroCb Thundercloud
by Surajit Das Barman, Rakibuzzaman Shah, Syed Islam and Apurv Kumar
Appl. Sci. 2024, 14(12), 5305; https://doi.org/10.3390/app14125305 - 19 Jun 2024
Viewed by 447
Abstract
Pyrocumulonimbus (pyroCb) thunderclouds, produced from extreme bushfires, can initiate frequent cloud-to-ground (CG) lightning strikes containing extended continuing currents. This, in turn, can ignite new spot fires and inflict massive harm on the environment and infrastructures. This study presents a 3D numerical thundercloud model [...] Read more.
Pyrocumulonimbus (pyroCb) thunderclouds, produced from extreme bushfires, can initiate frequent cloud-to-ground (CG) lightning strikes containing extended continuing currents. This, in turn, can ignite new spot fires and inflict massive harm on the environment and infrastructures. This study presents a 3D numerical thundercloud model for estimating the lightning of different types and its striking zone for the conceptual tripole thundercloud structure which is theorized to produce the lightning phenomenon in pyroCb storms. More emphasis is given to the lower positive charge layer, and the impacts of strong wind shear are also explored to thoroughly examine various electrical parameters including the longitudinal electric field, electric potential, and surface charge density. The simulation outcomes on pyroCb thunderclouds with a tripole structure confirm the presence of negative longitudinal electric field initiation at the cloud’s lower region. This initiation is accompanied by enhancing the lower positive charge region, resulting in an overall positive electric potential increase. Consequently, negative surface charge density appears underneath the pyroCb thundercloud which has the potential to induce positive (+CG) lightning flashes. With wind shear extension of upper charge layers in pyroCb, the lightning initiation potential becomes negative to reduce the absolute field value and would generate negative (−CG) lightning flashes. A subsequent parametric study is carried out considering a positive correlation between aerosol concentration and charge density to investigate the sensitivity of pyroCb electrification under the influence of high aerosol conditions. The suggested model would establish the basis for identifying the potential area impacted by lightning and could also be expanded to analyze the dangerous conditions that may arise in wind energy farms or power substations in times of severe pyroCb events. Full article
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<p>(<b>a</b>) Active fire and (<b>b</b>) lightning strike observations on Black Saturday, 7 February 2009 [<a href="#B11-applsci-14-05305" class="html-bibr">11</a>].</p>
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<p>PyroCb thunderclouds exhibit the following: (<b>a</b>) a tripole structure characterized by a prevailing upper positive (UP) charge layer, a prominent middle negative (MN) and a minor lower positive (LP) charge layers, accompanied by an extra negative screening layer (SC) positioned at the top, and (<b>b</b>) the effect of wind shear to create the titled structure.</p>
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<p>The vertical profile of the pyroCb thundercloud model (in xz plane) exhibits a tripole charge structure under two different conditions: (<b>a</b>) without LP charge enhancement (configuration 1) and (<b>b</b>) with increased LP charge region (configuration 2).</p>
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<p>Plots of the distributions of electric potential (<b>a1</b>,<b>a2</b>) and the changes in the electric field (<b>b1</b>,<b>b2</b>) for configurations 1 and 2 of the tripole structure-based pyroCb thundercloud, respectively.</p>
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<p>Graphs depicting the electric potential <span class="html-italic">V</span> (MV) are shown at the point of maximum field where the initiation of flash is marked with an “x” (<b>a1</b>,<b>a2</b>). The longitudinal electric field <math display="inline"><semantics> <msub> <mi>E</mi> <mi>z</mi> </msub> </semantics></math> (kV/m) for two different thundercloud configurations is also illustrated in (<b>b1</b>,<b>b2</b>).</p>
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<p>Plots of surface charge density <math display="inline"><semantics> <mi>σ</mi> </semantics></math> for: (<b>a</b>) configuration 1 and (<b>b</b>) configuration 2 to identify probable CG lightning types in pyroCb thunderclouds. Detailed views of the temporal changes in <math display="inline"><semantics> <mi>σ</mi> </semantics></math> on Earth’s surface (<math display="inline"><semantics> <mrow> <mi>x</mi> <mi>y</mi> </mrow> </semantics></math> plane) are presented for two tripole charge configurations.</p>
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<p>Vertical cross-sectional representation of pyroCb thunderclouds incorporating the wind shear extension of SC and UP charge layers when (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>, and (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>8</mn> </mrow> </semantics></math> km.</p>
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<p>With the wind shear extension of SC and UP charge layers, projections of potential (<b>a1</b>–<b>a3</b>) and electric field (<b>b1</b>–<b>b3</b>) distributions in the conceptual pyroCb thundercloud.</p>
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<p>Graphs showing the electric potential <span class="html-italic">V</span> (MV) at maximum field point of pyroCb thundercloud (<b>a1</b>–<b>a3</b>) and its corresponding longitudinal electric field <math display="inline"><semantics> <msub> <mi>E</mi> <mi>z</mi> </msub> </semantics></math> (kV/m) (<b>b1</b>–<b>b3</b>) under the effect of wind shear. The symbol “x” in (<b>a1</b>–<b>a3</b>) represents the flash initiation point, and the red-dashed dotted line in (<b>b1</b>–<b>b3</b>) indicates the initiation threshold field.</p>
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<p>Plots of surface charge density <math display="inline"><semantics> <mi>σ</mi> </semantics></math> (<b>a</b>–<b>c</b>) for a pyroCb thundercloud under the effect of wind shear.</p>
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<p>Figures illustrating (<b>a1</b>–<b>c1</b>) the electric potential <span class="html-italic">V</span> (MV) at maximum field point, and (<b>a2</b>–<b>c2</b>) the longitudinal electric field <math display="inline"><semantics> <msub> <mi>E</mi> <mi>z</mi> </msub> </semantics></math> (kV/m) for pyroCb thundercloud with wind shear extension values of (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math> and (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>8</mn> </mrow> </semantics></math> km. The threshold field value of initiation is shown by red dash-dotted lines.</p>
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<p>Variations in surface charge density <math display="inline"><semantics> <mi>σ</mi> </semantics></math> in pyroCb for aerosol concentrations: <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> </mrow> </semantics></math> 1000 cm<sup>−3</sup> (<b>a1</b>–<b>c1</b>) and 5000 cm<sup>−3</sup> (<b>a2</b>–<b>c2</b>) in the presence of wind shear.</p>
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<p>Variations in surface charge density <math display="inline"><semantics> <mi>σ</mi> </semantics></math> in pyroCb for aerosol concentrations: <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> </mrow> </semantics></math> 10,000 cm<sup>−3</sup> (<b>a1</b>–<b>c1</b>) and 20,000 cm<sup>−3</sup> (<b>a2</b>–<b>c2</b>).</p>
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18 pages, 11359 KiB  
Article
Study of Quality Control Methods Utilizing IRMCD for HY-2B Data Assimilation Application
by Jiazheng Hu, Yu Zhang, Jianjun Xu, Jiajing Li, Duanzhou Shao, Qichang Tan and Junjie Feng
Atmosphere 2024, 15(6), 728; https://doi.org/10.3390/atmos15060728 - 18 Jun 2024
Viewed by 421
Abstract
Quality control (QC) of HaiYang-2B (HY-2B) satellite data is mainly based on the observation process, which remains uncertain for data assimilation (DA). The data in operation have not been widely used in numerical weather prediction. To ensure HY-2B data meet the theoretical assumptions [...] Read more.
Quality control (QC) of HaiYang-2B (HY-2B) satellite data is mainly based on the observation process, which remains uncertain for data assimilation (DA). The data in operation have not been widely used in numerical weather prediction. To ensure HY-2B data meet the theoretical assumptions for DA applications, the iterated reweighted minimum covariance determinant (IRMCD) QC method was studied in HY-2B data based on the typhoon “Chanba”. The statistical results showed that most of the outliers were eliminated, and the observation increment distribution of the HY-2B data after QC (QCed) was closer to a Gaussian distribution than the raw data. The kurtosis and skewness of the QCed data were much closer to zero. The QCed track demonstrated the lowest accumulated error and the best intensity in typhoon assimilation, and the QCed intensity was closest to the observation during the nearshore enhancement, exhibiting the strongest intensity among the experiment. Further analysis revealed that the improvement was accompanied by a significant reduction in vertical wind shear during the nearshore enhancement of the typhoon. The QCed moisture flux divergence and vertical velocity in the upper layer increased significantly, which promoted the upward transport of momentum in the lower layers and contributed to the maintenance of the typhoon’s barotropic structure. Compared with the assimilation of raw data, the effective removal of outliers using the IRMCD algorithm significantly improved the simulation results for typhoons. Full article
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<p>Terrain height; red rectangular box is forecasting experiment area (South China Sea).</p>
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<p>Data assimilation experiment framework.</p>
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<p>Probability density distribution of the observation increment (OMB), u-wind (left), and v-wind (right) during 30 June, 00 UTC–3 July, 00 UTC 2022; before QC (<b>a</b>,<b>b</b>); after QC (<b>c</b>,<b>d</b>).</p>
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<p>Distribution of observation (<span class="html-italic">x</span>-axis) and background filed (<span class="html-italic">y</span>-axis); u-wind (left), v-wind (right) during 30 June, 00 UTC−3 July, 00 UTC 2022; before QC (<b>a</b>,<b>b</b>); after QC (<b>c</b>,<b>d</b>).</p>
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<p>Quantile–quantile (Q–Q) scatterplots; u-wind (left), and v-wind (right) during 30 June, 00 UTC−3 July, 00 UTC 2022; before QC (<b>a</b>,<b>b</b>); after QC (<b>c</b>,<b>d</b>).</p>
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<p>Wind field of observation on 30 June, 12 UTC 2022; the red barbs are the outliers based on the IRMCD.</p>
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<p>Vertical profile of wind speed increment (m/s) on 30 June, 12 UTC 2022; before QC (<b>a</b>); after QC (<b>b</b>).</p>
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<p>Analysis of increment in wind speed (m/s) at the 850 hPa levels and 850 hPa horizontal wind vector on 30 June, 12 UTC 2022; before QC (<b>a</b>); after QC (<b>b</b>).</p>
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<p>Analysis of increment in divergence (s<sup>−1</sup>) in 850 hPa levels on 30 June, 12 UTC 2022; before QC (<b>a</b>); after QC (<b>b</b>).</p>
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<p>Simulation of the track (<b>a</b>) and the accumulated error (<b>b</b>) of track from 30 June, 12 UTC to 2 July, 12 UTC 2022. The best track from IBTrACS (black line) and experiments for EXP-CTRL (green line), EXP-HY2B (yellow line), EXP-IRMCD (red line).</p>
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<p>Simulation of intensity, variation in minimum sea level pressure (MSLP, (<b>a</b>)), and maximum wind speed (<b>b</b>) from 30 June, 12 UTC to 2 July, 12 UTC 2022. The best track from IBTrACS (black line), the experiments for EXP-CTRL (green line), EXP-HY2B (yellow line), and EXP-IRMCD (red line).</p>
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<p>The 6 h pressure change (<b>a</b>) and the vertical wind shear (VWS, (<b>b</b>)) in a square area of 200 km from 30 June, 12 UTC to 2 July, 12 UTC 2022. The pressure below the magenta dotted line is equal to or less than −4.14 hPa; the best track from IBTrACS (black line), the experiments for EXP-CTRL (green line), EXP-HY2B (yellow line), and EXP-IRMCD (red line).</p>
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<p>Time series of moisture flux divergence (the first column, g/(kg*s)) and vertical velocity (the second column, m/s). The experiments for EXP-CTRL (<b>a</b>,<b>b</b>), EXP-HY2B (<b>c</b>,<b>d</b>), and EXP-IRMCD (<b>e</b>,<b>f</b>) on 1 July.</p>
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<p>Horizontal wind vector (500 hPa) and horizontal distribution of VWS (m/s) between 200 and 850 hPa. The experiments for EXP-CTRL (<b>a</b>–<b>c</b>), EXP-HY2B (<b>d</b>–<b>f</b>), and EXP-IRMCD (<b>g</b>–<b>i</b>) on 1 July, 21 UTC (the first column), 2 July, 00 UTC (the second column), and 03 UTC (the third column).</p>
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<p>Latitudinal vertical profile along the typhoon center. The experiments for EXP-CTRL (<b>a</b>–<b>c</b>), EXP-HY2B (<b>d</b>–<b>f</b>), and EXP-IRMCD (<b>g</b>–<b>i</b>) on 2 July, 00 UTC (the first column), 03 UTC (the second column), and 06 UTC (the third column). Shaded colors are horizontal wind speed (m/s) at different levels. The black triangle is the center of the typhoon at the surface.</p>
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<p>Meridional vertical profile along the typhoon center. The experiments for EXP-CTRL (<b>a</b>–<b>c</b>), EXP-HY2B (<b>d</b>–<b>f</b>), and EXP-IRMCD (<b>g</b>–<b>i</b>) on 2 July, 00 UTC (the first column), 03 UTC (the second column), and 06 UTC (the third column). Shaded colors are the horizontal wind speed (m/s) at different levels. The black triangle is the center of the typhoon at the surface.</p>
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15 pages, 4614 KiB  
Article
Wind Shear Response of Aircraft with C* and C*U Controller during Approach
by Yufei Yan and Lei Song
Aerospace 2024, 11(6), 476; https://doi.org/10.3390/aerospace11060476 - 17 Jun 2024
Viewed by 599
Abstract
This study investigates the impact of wind shear on the flight dynamics of commercial aircraft where C* and C*U control laws are employed during the approach phase. Given the high incidence of flight accidents during takeoff and landing attributed to wind shear, this [...] Read more.
This study investigates the impact of wind shear on the flight dynamics of commercial aircraft where C* and C*U control laws are employed during the approach phase. Given the high incidence of flight accidents during takeoff and landing attributed to wind shear, this research aims to enhance aviation safety by analyzing control law behavior under varying wind shear conditions. A nonlinear flight simulation model was developed, utilizing aerodynamic and engine data from a B737, to explore the aircraft’s response to different wind shear intensities. The simulation analysis was used to compare the response of the aircraft with C* and C*U controllers, respectively, under different wind shear, and to evaluate the effectiveness of its stability enhancement in wind shear. It was found that in most cases, the controller can achieve a good stabilization effect, but in some cases of wind fields, the aircraft suffered more significant oscillation. Full article
(This article belongs to the Special Issue Advanced Aircraft Technology)
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<p>Simulator architecture.</p>
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<p>Static lift coefficient varies with angle of attack.</p>
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<p>Static pitch moment coefficient varies with angle of attack.</p>
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<p>The wind shear model.</p>
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<p>Simplified C* controller structure.</p>
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<p>Simplified C*U controller structure.</p>
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<p>CAP flight quality level of short-period modal.</p>
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<p>The response of the aircraft with different controllers in a 10-knot tailwind shear: (<b>a</b>) the angle of attack response; (<b>b</b>) the angle of pitch; (<b>c</b>) the climb angle response; (<b>d</b>) the airspeed response.</p>
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<p>The response of the aircraft with different controllers in a 10-knot headwind shear: (<b>a</b>) the angle of attack response; (<b>b</b>) the angle of pitch; (<b>c</b>) the climb angle response; (<b>d</b>) the airspeed response.</p>
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<p>The response of the aircraft with different controllers in a 15-knot tailwind shear: (<b>a</b>) the angle of attack response; (<b>b</b>) the angle of pitch; (<b>c</b>) the climb angle response; (<b>d</b>) the airspeed response.</p>
Full article ">Figure A1 Cont.
<p>The response of the aircraft with different controllers in a 15-knot tailwind shear: (<b>a</b>) the angle of attack response; (<b>b</b>) the angle of pitch; (<b>c</b>) the climb angle response; (<b>d</b>) the airspeed response.</p>
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<p>The response of the aircraft with different controllers in a 15-knot headwind shear: (<b>a</b>) the angle of attack response; (<b>b</b>) the angle of pitch; (<b>c</b>) the climb angle response; (<b>d</b>) the airspeed response.</p>
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<p>The response of the aircraft with different controllers in a 25-knot headwind shear: (<b>a</b>) the angle of attack response; (<b>b</b>) the angle of pitch; (<b>c</b>) the climb angle response; (<b>d</b>) the airspeed response.</p>
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29 pages, 11029 KiB  
Article
The Use of Externally Bonded Fibre Reinforced Polymer Composites to Enhance the Seismic Resilience of Single Shear Walls: A Nonlinear Time History Assessment
by Ali Abbaszadeh and Omar Chaallal
J. Compos. Sci. 2024, 8(6), 229; https://doi.org/10.3390/jcs8060229 - 17 Jun 2024
Viewed by 480
Abstract
In medium- to high-rise buildings, single shear walls (SSWs) are often used to resist lateral force due to wind and earthquakes. They are designed to dissipate seismic energy mainly through plastic hinge zones at the base. However, they often display large post-earthquake deformations [...] Read more.
In medium- to high-rise buildings, single shear walls (SSWs) are often used to resist lateral force due to wind and earthquakes. They are designed to dissipate seismic energy mainly through plastic hinge zones at the base. However, they often display large post-earthquake deformations that can give rise to many economic and safety concerns within buildings. Hence, the primary objective of this research study is to minimize residual deformations in existing SSWs located in the Western and Eastern seismic zones of Canada, thereby enhancing their resilience and self-centering capacity. To that end, four SSWs of 20 and 15 stories, located in Vancouver and Montreal, were meticulously designed and detailed per the latest Canadian standards and codes. The study assessed the impact of three innovative strengthening schemes on the seismic response of these SSWs through 2D nonlinear time history (NLTH) analysis. All three strengthening schemes involved the application of Externally Bonded Fiber Reinforced Polymer (EB-FRP) to the shear walls. Accordingly, a total of 208 NLTH analyses were conducted to assess the effectiveness of all strengthening configurations. The findings unveiled that the most efficient technique for reducing residual drift in SSWs involved applying three layers of vertical FRP sheets to the extreme edges of the wall, full FRP wrapping the walls, and full FRP wrapping of the plastic hinge zone. Nevertheless, it is noteworthy that implementing these strengthening schemes may lead to an increase in bending moment and base shear force demands within the walls. Full article
(This article belongs to the Special Issue Polymer Composites and Fibers, Volume II)
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<p>Self-centering mechanisms that are frequently discussed in seismic literature.</p>
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<p>Plan view of the studied building.</p>
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<p>Reinforcement configuration of shear walls.</p>
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<p>Different strengthening schemes used in this study: (<b>a</b>) R3-SSW; (<b>b</b>) R2-SSW; (<b>c</b>) R1-SSW; (<b>d</b>) FRP wrapping of shear walls.</p>
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<p>Member and node modeling of (<b>a</b>) 20-story shear wall; (<b>b</b>) 15-story shear wall.</p>
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<p>Growth in the hazard level of site category C.</p>
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<p>Response spectra for scaled ground motions.</p>
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<p>Variation in earthquake record content for different ground motion scenarios.</p>
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<p>Mean peak IDR values for different scenarios and strengthening schemes in 20-story SSW located in Vancouver.</p>
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<p>Mean peak IDR values for different scenarios and strengthening schemes in 15-story SSW located in Vancouver.</p>
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<p>Mean peak IDR values for different scenarios and strengthening schemes in 20-story SSW located in Montreal.</p>
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<p>Mean peak IDR values for different scenarios and strengthening schemes in 15-story SSW located in Montreal.</p>
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<p>Maximum RIDR in 20-story C-SSW for different scenarios located in Vancouver.</p>
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<p>Maximum RIDR in 15-story C-SSW for different scenarios located in Vancouver.</p>
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<p>Reduction of RIDR in 20-story SSW located in Vancouver, due to strengthening schemes, in all scenarios.</p>
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<p>Reduction of RIDR in 15-story SSW located in Vancouver, due to strengthening schemes, in all scenarios.</p>
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<p>Maximum RIDR in 20-story C-SSW for different scenarios located in Montreal.</p>
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<p>Maximum RIDR in 15-story C-SSW for different scenarios located in Montreal.</p>
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<p>Reduction of RIDR in 20-story SSW located in Montreal, due to strengthening schemes, in all scenarios.</p>
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<p>Reduction of RIDR in 15-story SSW located in Montreal, due to strengthening schemes, in all scenarios.</p>
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<p>Shear demand in the 20-story and 15-story SSWs located in Vancouver due to different scenarios.</p>
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<p>Shear demand in the 20-story SSWs located in Montreal due to different scenarios.</p>
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<p>Bending demand in the 15-story SSWs located in Vancouver due to different scenarios.</p>
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<p>Bending demand in the 20-story SSWs located in Montreal due to different scenarios.</p>
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17 pages, 3121 KiB  
Article
Near-Surface Thermodynamic Influences on Evaporation Duct Shape
by Sarah E. Wessinger, Daniel P. Greenway, Tracy Haack and Erin E. Hackett
Atmosphere 2024, 15(6), 718; https://doi.org/10.3390/atmos15060718 - 15 Jun 2024
Viewed by 473
Abstract
This study utilizes in situ measurements and numerical weather prediction forecasts curated during the Coupled Air–Sea Processes Electromagnetic Ducting Research (CASPER) east field campaign to assess how thermodynamic properties in the marine atmospheric surface layer influence evaporation duct shape independent of duct height. [...] Read more.
This study utilizes in situ measurements and numerical weather prediction forecasts curated during the Coupled Air–Sea Processes Electromagnetic Ducting Research (CASPER) east field campaign to assess how thermodynamic properties in the marine atmospheric surface layer influence evaporation duct shape independent of duct height. More specifically, we investigate evaporation duct shape through a duct shape parameter, a parameter known to affect the propagation of X-band radar signals and is directly related to the curvature of the duct. Relationships between this duct shape parameter and air sea temperature difference (ASTD) reveal that during unstable periods (ASTD < 0), the duct shape parameter is generally larger than in near-neutral or stable atmospheric conditions, indicating tighter curvature of the M-profile. Furthermore, for any specific duct height, a strong linear relationship between the near-surface-specific humidity gradient and the duct shape parameter is found, suggesting that it is primarily driven by near-surface humidity gradients. The results demonstrate that an a priori estimate of duct shape, for a given duct height, is possible if the near-surface humidity gradient is known. Full article
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<p>Example of a modified refractivity profile for the two-layer model described by Equation (1) [<a href="#B8-atmosphere-15-00718" class="html-bibr">8</a>] and associated parameters.</p>
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<p><span class="html-italic">In situ</span> bulk measurements from the R/V Sharp used by the COARE algorithm to compute vertical profiles of temperature, wind speed, and specific humidity: (<b>A</b>) <span class="html-italic">in situ</span> bulk temperature measurements: skin-corrected sea surface temperature (SST) and air temperature 12 m above the sea surface measured from the bow mast of the R/V Sharp. The ASTD (right axis) between these two measurements is used as a rough estimate for observed stability regime. (<b>B</b>) <span class="html-italic">In situ</span> bulk measurements of wind speed and atmospheric pressure (right axis) at 12 m above the sea surface measured on the bow mast of the R/V Sharp. (<b>C</b>) <span class="html-italic">In situ</span> bulk measurements of specific humidity 12 m above the sea surface measured on the R/V Sharp bow mast (note the measurement gap in October is due to a mid-cruise port call).</p>
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<p>Modified refractivity profiles from a COAMPS<sup>®</sup> forecast corresponding to October 10th 0600Z; color denotes their respective ranges from Duck pier (see color bar).</p>
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<p>(<b>A</b>) Example nonlinear-least-squares regression (NLSR) fit of Equation (1) to a COAMPS<sup>®</sup> modified refractivity profile. The forecast in this example is for November 3rd 0700Z, which was initialized at 0000 UTC. (<b>B</b>) Histograms of <span class="html-italic">E<sub>r</sub></span> for COAMPS<sup>®</sup> (blue; left axis) and COARE (grey; right axis) (see legend), where bin-overlap is in the alternate shade of blue.</p>
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<p>(<b>A</b>) NLSR-fits of the parametric refractivity model (Equation (1)) to two COAMPS<sup>®</sup> <span class="html-italic">M</span>-profiles both with <span class="html-italic">z<sub>d</sub></span> = 13 m and <span class="html-italic">m</span><sub>1</sub> = 0.1 M-units m<sup>−1</sup>, different <span class="html-italic">c</span><sub>0</sub> (see legend), and M<sub>0</sub> of 369 and 375 M-units for the red and blue profiles, respectively. (<b>B</b>) Scatter plot of NLSR-based <span class="html-italic">c</span><sub>0</sub> of COAMPS<sup>®</sup> <span class="html-italic">M</span>-profiles and the corresponding evaporation layer-averaged second order vertical derivative of modified refractivity. The colors denote duct height (see color bar).</p>
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<p>Relationships between stability metrics and c<sub>0</sub>. (<b>A</b>) shows the relationship between c<sub>0</sub> and <span class="html-italic">Ri</span> for COAMPS profiles, while (<b>B</b>) shows the relationship between c<sub>0</sub> and ASTD (for all numerical data—see legend).</p>
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<p>The relationship between <span class="html-italic">c</span><sub>0</sub> and wind speed shear (<math display="inline"><semantics> <mrow> <mrow> <mrow> <mo>∂</mo> <mi>U</mi> </mrow> <mo>/</mo> <mrow> <mo>∂</mo> <mi>z</mi> </mrow> </mrow> </mrow> </semantics></math>) for both COARE and COAMPS<sup>®</sup>. The color of each marker corresponds to the respective ASTD (see color bar).</p>
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<p>The relationship between the NSSHG (<math display="inline"><semantics> <mrow> <mrow> <mrow> <mo>∂</mo> <mi>q</mi> </mrow> <mo>/</mo> <mrow> <mo>∂</mo> <mi>z</mi> </mrow> </mrow> </mrow> </semantics></math>) and <span class="html-italic">c</span><sub>0</sub>, where the color of each marker is the respective ASTD for both COARE and COAMPS<sup>®</sup> (see color bar).</p>
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<p>Relationship between c<sub>0</sub> and <math display="inline"><semantics> <mrow> <mrow> <mrow> <mo>∂</mo> <mi>q</mi> </mrow> <mo>/</mo> <mrow> <mo>∂</mo> <mi>z</mi> </mrow> </mrow> </mrow> </semantics></math>, when <math display="inline"><semantics> <mrow> <mrow> <mrow> <mo>∂</mo> <mi>q</mi> </mrow> <mo>/</mo> <mrow> <mo>∂</mo> <mi>z</mi> </mrow> </mrow> </mrow> </semantics></math> is estimated over various altitude ranges. (<b>A</b>) shows the relationship between <math display="inline"><semantics> <mrow> <mrow> <mrow> <mo>∂</mo> <mi>q</mi> </mrow> <mo>/</mo> <mrow> <mo>∂</mo> <mi>z</mi> </mrow> </mrow> </mrow> </semantics></math> and c<sub>0</sub> when the lower reference altitude is varied (see legend). (<b>B</b>) shows the relationship between <math display="inline"><semantics> <mrow> <mrow> <mrow> <mo>∂</mo> <mi>q</mi> </mrow> <mo>/</mo> <mrow> <mo>∂</mo> <mi>z</mi> </mrow> </mrow> </mrow> </semantics></math> and c<sub>0</sub> when <math display="inline"><semantics> <mrow> <mrow> <mrow> <mo>∂</mo> <mi>q</mi> </mrow> <mo>/</mo> <mrow> <mo>∂</mo> <mi>z</mi> </mrow> </mrow> </mrow> </semantics></math> is computed using the altitude range of 0.01z<sub>d</sub>–2z<sub>d</sub> and where the color of each marker is the respective <span class="html-italic">z<sub>d</sub></span> (see color bar). (<b>C</b>) shows the relationship between c<sub>0</sub> and <math display="inline"><semantics> <mrow> <mrow> <mrow> <mo>∂</mo> <mi>q</mi> </mrow> <mo>/</mo> <mrow> <mo>∂</mo> <mi>z</mi> </mrow> </mrow> </mrow> </semantics></math> when the upper reference altitude is varied (see legend). (<b>D</b>) shows the relationship between <math display="inline"><semantics> <mrow> <mrow> <mrow> <mo>∂</mo> <mi>q</mi> </mrow> <mo>/</mo> <mrow> <mo>∂</mo> <mi>z</mi> </mrow> </mrow> </mrow> </semantics></math> and c<sub>0</sub> when <math display="inline"><semantics> <mrow> <mrow> <mrow> <mo>∂</mo> <mi>q</mi> </mrow> <mo>/</mo> <mrow> <mo>∂</mo> <mi>z</mi> </mrow> </mrow> </mrow> </semantics></math> is computed using the altitude range of 0 m–4 m and where the color of each marker is the respective <span class="html-italic">z<sub>d</sub></span> (see color bar). (<b>E</b>) Example COAMPS<sup>®</sup> vertical <span class="html-italic">q</span> profile from 30 October 2015, at 1300Z, where the corresponding <span class="html-italic">M</span>-profile <span class="html-italic">z<sub>d</sub></span> is 13 m (red dashed line) and the height of various reference points on the profile are illustrated (see legend).</p>
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32 pages, 16801 KiB  
Article
A Critical Review of Cone Penetration Test-Based Correlations for Estimating Small-Strain Shear Modulus in North Sea Soils
by Bruno Stuyts, Wout Weijtjens, Carlos Sastre Jurado, Christof Devriendt and Anis Kheffache
Geotechnics 2024, 4(2), 604-635; https://doi.org/10.3390/geotechnics4020033 - 14 Jun 2024
Viewed by 1256
Abstract
The geotechnical characterisation of offshore wind farm sites requires measurement or estimation of the small-strain shear stiffness Gmax of the subsoil. This parameter can be derived from shear wave velocity Vs measurements if the bulk density of the soil is known. [...] Read more.
The geotechnical characterisation of offshore wind farm sites requires measurement or estimation of the small-strain shear stiffness Gmax of the subsoil. This parameter can be derived from shear wave velocity Vs measurements if the bulk density of the soil is known. Since direct measurements of Vs are generally not available at all foundation locations in a wind farm, correlations with cone penetration test (CPT) results are often used to determine location-specific stiffness parameters for foundation design. Existing correlations have mostly been calibrated to onshore datasets which may not contain the same soil types and stress conditions found in the North Sea. The distinct geological history of the North Sea necessitates a critical review of these existing CPT-based correlations. They are evaluated against an extensive database of in situ Vs measurements in the southern North Sea. The importance of modelling the stress-dependent nature of Vs is highlighted, and a novel stress-dependent model for Vs from CPT data, which leads to an improved fit, is presented. As the small-strain stiffness is used as an input to foundation response calculations, the model uncertainty of the correlation can introduce significant uncertainty into the resulting foundation response. This transformation uncertainty is quantified for each of the correlations evaluated in this study and shows important variations. Full article
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<p>Depositional environments in the North Sea during the early Eocene [<a href="#B24-geotechnics-04-00033" class="html-bibr">24</a>] (?-symbols indicate uncertain boundaries of the depositional environments or sediment supply).</p>
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<p>Depositional environments in the North Sea during the Mid-Miocene [<a href="#B24-geotechnics-04-00033" class="html-bibr">24</a>].</p>
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<p>Depositional environments in the North Sea during the Early Pleistocene [<a href="#B24-geotechnics-04-00033" class="html-bibr">24</a>].</p>
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<p>Schematic depositional history for the IJmuiden Ver offshore wind farm zone from the Mid-Pleistocene to the Mid-Eemian [<a href="#B25-geotechnics-04-00033" class="html-bibr">25</a>].</p>
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<p>Schematic depositional history for the IJmuiden Ver offshore wind farm zone from the Mid-Eemian to present day [<a href="#B25-geotechnics-04-00033" class="html-bibr">25</a>].</p>
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<p>Geographical location of the S-PCPT tests in the Belgian, Dutch, German and Danish sectors of the North Sea.</p>
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<p>Effect of the CPT testing method on the relation between vertical effective stress and shear wave velocity.</p>
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<p>Combined box and violin plots of the shear wave velocity dataset.</p>
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<p>Relation between vertical effective stress, total cone resistance, soil behaviour type index and shear wave velocity.</p>
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<p>Comparison of calculated and measured <math display="inline"><semantics> <msub> <mi>G</mi> <mi>max</mi> </msub> </semantics></math> using the correlation by [<a href="#B8-geotechnics-04-00033" class="html-bibr">8</a>]. The measurements are colour coded according to soil behaviour type index <math display="inline"><semantics> <msub> <mi>I</mi> <mi>c</mi> </msub> </semantics></math>.</p>
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<p>Comparison of calculated and measured <math display="inline"><semantics> <msub> <mi>G</mi> <mi>max</mi> </msub> </semantics></math> using the correlation by Rix and Stokoe [<a href="#B9-geotechnics-04-00033" class="html-bibr">9</a>]. The measurements are colour coded according to soil behaviour type index <math display="inline"><semantics> <msub> <mi>I</mi> <mi>c</mi> </msub> </semantics></math>.</p>
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<p>Comparison of calculated and measured <math display="inline"><semantics> <msub> <mi>G</mi> <mi>max</mi> </msub> </semantics></math> using the correlation by Mayne and Rix [<a href="#B10-geotechnics-04-00033" class="html-bibr">10</a>]. The measurements are colour coded according to soil behaviour type index <math display="inline"><semantics> <msub> <mi>I</mi> <mi>c</mi> </msub> </semantics></math>.</p>
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<p>Comparison of calculated and measured <math display="inline"><semantics> <msub> <mi>G</mi> <mi>max</mi> </msub> </semantics></math> using the correlation by Peuchen et al. [<a href="#B18-geotechnics-04-00033" class="html-bibr">18</a>]. The measurements are colour coded according to soil behaviour type index <math display="inline"><semantics> <msub> <mi>I</mi> <mi>c</mi> </msub> </semantics></math>.</p>
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<p>Comparison of calculated and measured <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> using the correlation by Wride et al. [<a href="#B11-geotechnics-04-00033" class="html-bibr">11</a>]. The measurements are colour coded according to soil behaviour type index <math display="inline"><semantics> <msub> <mi>I</mi> <mi>c</mi> </msub> </semantics></math>.</p>
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<p>Verification of the linear trend between <math display="inline"><semantics> <msub> <mi>I</mi> <mi>c</mi> </msub> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mrow> <mi>s</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> <mo>/</mo> <msub> <mi>q</mi> <mrow> <mi>c</mi> <mn>1</mn> <mi>N</mi> </mrow> </msub> </mrow> </semantics></math> identified by Hegazy and Mayne for the North Sea data.</p>
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<p>Comparison of calculated and measured <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> using the correlation by Hegazy and Mayne [<a href="#B12-geotechnics-04-00033" class="html-bibr">12</a>]. The measurements are colour coded according to soil behaviour type index <math display="inline"><semantics> <msub> <mi>I</mi> <mi>c</mi> </msub> </semantics></math>.</p>
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<p>Comparison of calculated and measured <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> using the correlation by Andrus et al. [<a href="#B13-geotechnics-04-00033" class="html-bibr">13</a>]. The measurements are colour coded according to soil behaviour type index <math display="inline"><semantics> <msub> <mi>I</mi> <mi>c</mi> </msub> </semantics></math>.</p>
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<p>Comparison of calculated and measured <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> using the correlation by Tonni and Simonini [<a href="#B14-geotechnics-04-00033" class="html-bibr">14</a>]. The measurements are colour coded according to soil behaviour type index <math display="inline"><semantics> <msub> <mi>I</mi> <mi>c</mi> </msub> </semantics></math>.</p>
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<p>Predicted variation in <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> with depth for the 6 reference sediments provided by Lyu et al. [<a href="#B41-geotechnics-04-00033" class="html-bibr">41</a>].</p>
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<p>Comparison of calculated and measured <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> using the correlation by Robertson and Cabal [<a href="#B16-geotechnics-04-00033" class="html-bibr">16</a>]. The measurements are colour coded according to soil behaviour type index <math display="inline"><semantics> <msub> <mi>I</mi> <mi>c</mi> </msub> </semantics></math>.</p>
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<p>Comparison of calculated and measured <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> using the correlation by McGann et al. [<a href="#B17-geotechnics-04-00033" class="html-bibr">17</a>]. The measurements are colour coded according to soil behaviour type index <math display="inline"><semantics> <msub> <mi>I</mi> <mi>c</mi> </msub> </semantics></math>.</p>
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<p>Combined box and violin plots of the ratio <math display="inline"><semantics> <mfrac> <msub> <mi>G</mi> <mrow> <mi>max</mi> <mo>,</mo> <mi>c</mi> <mi>a</mi> <mi>l</mi> <mi>c</mi> <mi>u</mi> <mi>l</mi> <mi>a</mi> <mi>t</mi> <mi>e</mi> <mi>d</mi> </mrow> </msub> <msub> <mi>G</mi> <mrow> <mi>max</mi> <mo>,</mo> <mi>m</mi> <mi>e</mi> <mi>a</mi> <mi>s</mi> <mi>u</mi> <mi>r</mi> <mi>e</mi> <mi>d</mi> </mrow> </msub> </mfrac> </semantics></math> [<a href="#B8-geotechnics-04-00033" class="html-bibr">8</a>,<a href="#B9-geotechnics-04-00033" class="html-bibr">9</a>,<a href="#B10-geotechnics-04-00033" class="html-bibr">10</a>,<a href="#B18-geotechnics-04-00033" class="html-bibr">18</a>].</p>
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<p>Combined box and violin plots of the ratio <math display="inline"><semantics> <mfrac> <msub> <mi>V</mi> <mrow> <mi>s</mi> <mo>,</mo> <mi>c</mi> <mi>a</mi> <mi>l</mi> <mi>c</mi> <mi>u</mi> <mi>l</mi> <mi>a</mi> <mi>t</mi> <mi>e</mi> <mi>d</mi> </mrow> </msub> <msub> <mi>V</mi> <mrow> <mi>s</mi> <mo>,</mo> <mi>m</mi> <mi>e</mi> <mi>a</mi> <mi>s</mi> <mi>u</mi> <mi>r</mi> <mi>e</mi> <mi>d</mi> </mrow> </msub> </mfrac> </semantics></math> [<a href="#B11-geotechnics-04-00033" class="html-bibr">11</a>,<a href="#B12-geotechnics-04-00033" class="html-bibr">12</a>,<a href="#B13-geotechnics-04-00033" class="html-bibr">13</a>,<a href="#B16-geotechnics-04-00033" class="html-bibr">16</a>,<a href="#B17-geotechnics-04-00033" class="html-bibr">17</a>].</p>
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<p>Comparison of calculated and measured <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> using the new stress-dependent correlation. The measurements are colour coded according to the soil behaviour type index <math display="inline"><semantics> <msub> <mi>I</mi> <mi>c</mi> </msub> </semantics></math>.</p>
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<p>Dependence of the ratio of calculated to measured <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> on the values of <math display="inline"><semantics> <msub> <mi>q</mi> <mi>c</mi> </msub> </semantics></math>. A marginal histogram for <math display="inline"><semantics> <msub> <mi>q</mi> <mi>c</mi> </msub> </semantics></math> is shown in the uppermost panel and a marginal histogram for the ratio of calculated to measured <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> is shown in the rightmost panel.</p>
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<p>Dependence of the ratio of calculated to measured <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> on the values of <math display="inline"><semantics> <msub> <mi>I</mi> <mi>c</mi> </msub> </semantics></math>. A marginal histogram for <math display="inline"><semantics> <msub> <mi>I</mi> <mi>c</mi> </msub> </semantics></math> is shown in the uppermost panel and a marginal histogram for the ratio of calculated to measured <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> is shown in the rightmost panel.</p>
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<p>Dependence of the ratio of calculated to measured <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> on the values of <math display="inline"><semantics> <msubsup> <mi>σ</mi> <mrow> <mi>v</mi> <mn>0</mn> </mrow> <mo>′</mo> </msubsup> </semantics></math>. A marginal histogram for <math display="inline"><semantics> <msubsup> <mi>σ</mi> <mrow> <mi>v</mi> <mn>0</mn> </mrow> <mo>′</mo> </msubsup> </semantics></math> is shown in the uppermost panel and a marginal histogram for the ratio of calculated to measured <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> is shown in the rightmost panel.</p>
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<p>Comparison of <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> profiles obtained with different correlations with direct <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> measurements for location IJV162-SCPT with uniform sandy soil [<a href="#B11-geotechnics-04-00033" class="html-bibr">11</a>,<a href="#B12-geotechnics-04-00033" class="html-bibr">12</a>,<a href="#B13-geotechnics-04-00033" class="html-bibr">13</a>,<a href="#B16-geotechnics-04-00033" class="html-bibr">16</a>,<a href="#B17-geotechnics-04-00033" class="html-bibr">17</a>].</p>
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<p>Comparison of <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> profiles obtained with different correlations with direct <math display="inline"><semantics> <msub> <mi>V</mi> <mi>s</mi> </msub> </semantics></math> measurements for location IJV038-SCPT with layered soil [<a href="#B11-geotechnics-04-00033" class="html-bibr">11</a>,<a href="#B12-geotechnics-04-00033" class="html-bibr">12</a>,<a href="#B13-geotechnics-04-00033" class="html-bibr">13</a>,<a href="#B16-geotechnics-04-00033" class="html-bibr">16</a>,<a href="#B17-geotechnics-04-00033" class="html-bibr">17</a>].</p>
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11 pages, 3803 KiB  
Article
Wave-Particle Interactions in Astrophysical Plasmas
by Héctor Pérez-De-Tejada
Galaxies 2024, 12(3), 28; https://doi.org/10.3390/galaxies12030028 - 6 Jun 2024
Viewed by 407
Abstract
Dissipation processes derived from the kinetic theory of gases (shear viscosity and heat conduction) are employed to examine the solar wind that interacts with planetary ionospheres. The purpose of this study is to estimate the mean free path of wave-particle interactions that produce [...] Read more.
Dissipation processes derived from the kinetic theory of gases (shear viscosity and heat conduction) are employed to examine the solar wind that interacts with planetary ionospheres. The purpose of this study is to estimate the mean free path of wave-particle interactions that produce a continuum response in the plasma behavior. Wave-particle interactions are necessary to support the fluid dynamic interpretation that accounts for the interpretation of various features measured in a solar wind–planet ionosphere region; namely, (i) the transport of solar wind momentum to an upper ionosphere in the presence of a velocity shear, and (ii) plasma heating produced by momentum transport. From measurements conducted in the solar wind interaction with the Venus ionosphere, it is possible to estimate that in general terms, the mean free path of wave-particle interactions reaches λH ≥ 1000 km values that are comparable to the gyration radius of the solar wind particles in their Larmor motion within the local solar wind magnetic field. Similar values are also applicable to conditions measured by the Mars ionosphere and in cometary plasma wakes. Considerations are made in regard to the stochastic trajectories of the plasma particles that have been implied from the measurements made in planetary environments. At the same time, it is as possible that the same phenomenon is applicable to the interaction of stellar winds with the ionosphere of exoplanets, and also in regions where streaming ionized gases reach objects that are subject to rotational motion in other astrophysical problems (galactic flow–plasma interactions, black holes, etc.). Full article
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Figure 1

Figure 1
<p>(<b>Lower panel</b>) Trajectory of the Mariner 5 spacecraft projected in cylindrical coordinates in its flyby past Venus. Labels 1 to 5 along the trajectory mark important events in the plasma properties (a bow shock is identified at features 1 and 5), and the intermediate plasma transition occurs at features 2 and 4). (<b>Upper panel</b>) Magnetic field intensity and its latitudinal and azimuthal orientation, together with the plasma properties (thermal speed, density, and bulk speed) measured around Venus [<a href="#B1-galaxies-12-00028" class="html-bibr">1</a>].</p>
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<p>Ion speed and temperature measured along the orbit of Venera 10 on 19 April 1976. The Venera orbit in cylindrical coordinates is shown at the top. The temperature burst at position 1 was recorded during a flank crossing of a bow shock. A boundary layer is apparent by the increase in temperature and decrease in speed, and is initiated by the intermediate transition at the position labeled 2. A latter discontinuity in the boundary layer temperature profile corresponds to the boundary of the magneto-tail (from [<a href="#B6-galaxies-12-00028" class="html-bibr">6</a>]).</p>
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<p>Vector velocity speeds of the trans-terminator flow in the Venus upper ionosphere measured with instruments onboard the Pioneer Venus Orbiter spacecraft [<a href="#B9-galaxies-12-00028" class="html-bibr">9</a>].</p>
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<p>Measured flow velocities versus VEX altitude for solar wind H<sup>+</sup> ions, and ionospheric H<sup>+</sup> and O<sup>+</sup> ions. The curve marked v<sub>esc</sub> illustrates escape velocity versus altitude above Venus. The data points represent average values in 50 km altitude intervals sampled within Y = +0.5 of the dawn-dusk Meridian (<b>left panel</b>) and of the noon–midnight Meridian (<b>right panel</b>). Regions and boundaries are marked on the right-hand side as the I-sphere (the ionopause (IP), and the ionosheath (IMB) (from Lundin et al., (2011) [<a href="#B17-galaxies-12-00028" class="html-bibr">17</a>]).</p>
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<p>Mean free path values λ<sub>H</sub> of the solar wind obtained in wave-particle interactions and also in magnetic field fluctuations using the solar wind thermal speed V<sub>T</sub> and its kinematic viscosity coefficient ﬠ during the Mariner 5 trajectory in <a href="#galaxies-12-00028-f001" class="html-fig">Figure 1</a>. The connecting line labeled “W” refers to a value in the Venus inner and in the outer ionosheath that is implied by wave-particle interactions. The connecting line labeled “F” is implied by the magnetic field fluctuations.</p>
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<p>(<b>Upper panel</b>) Trajectory of the PVO in orbit 87 projected on one quadrant in cylindrical coordinates. The bow shock, the intermediate transition, and the ionopause are indicated. (<b>Lower panel</b>) Ion flux values measured as a function of energy in cycles I, II, III, and IV state their start time (their position is noted in the upper panel along the PVO trajectory). Positions A, B, and C in spectrum III mark the time when the ion fluxes were obtained ([<a href="#B24-galaxies-12-00028" class="html-bibr">24</a>]).</p>
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<p>View of a corkscrew vortex flow in fluid dynamics. Its geometry is equivalent to that of a vortex flow in the Venus wake, with its width and position varying during the solar cycle. Near the solar cycle minimum, the vortex is located closer to Venus (located by the right side) and there are also indications that its width becomes smaller with increasing distance downstream from the planet [<a href="#B13-galaxies-12-00028" class="html-bibr">13</a>].</p>
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24 pages, 14593 KiB  
Article
The Effects of Upper-Ocean Sea Temperatures and Salinity on the Intensity Change of Tropical Cyclones over the Western North Pacific and the South China Sea: An Observational Study
by Pak-Wai Chan, Ching-Chi Lam, Tai-Wai Hui, Zhigang Gao, Hongli Fu, Chunjian Sun and Hui Su
Atmosphere 2024, 15(6), 674; https://doi.org/10.3390/atmos15060674 - 31 May 2024
Viewed by 531
Abstract
With increasing air and sea temperatures, the thermodynamic environments over the oceans are becoming more favourable for the development of intense tropical cyclones (TCs) with rapid intensification (RI). The South China coastal region consists of highly densely populated cities, especially over the Pearl [...] Read more.
With increasing air and sea temperatures, the thermodynamic environments over the oceans are becoming more favourable for the development of intense tropical cyclones (TCs) with rapid intensification (RI). The South China coastal region consists of highly densely populated cities, especially over the Pearl River Delta (PRD) region. Intense TCs maintaining their strength or the RI of TCs close to the coastal region can present substantial forecasting challenges and have significant potential impacts on the coastal population. This study investigates the effect of sea-surface and sub-surface temperatures and salinity on the intensification of five TCs, namely Super Typhoon Hato in 2017, Super Typhoon Mangkhut in 2018, and Typhoon Talim, Super Typhoon Saola, and Severe Typhoon Koinu in 2023, which have significantly affected the South China coastal region and triggered high TC warning signals in Hong Kong in the past few years. This analysis utilised the Hong Kong Observatory’s TC best-track and intensity data, along with sea temperature and salinity profiles generated using the China Ocean ReAnalysis version 2 (CORA2) product from the National Marine Data and Information Service of China. It was found that high sea-surface temperatures (SST) of 30 °C or above for a depth of about 20 m, low sea-surface salinity (SSS) levels of 33.8 psu or below for a depth of at least 20 m, and strong salinity stratification of at least 0.6 psu per 100 m depth might offer useful hints for predicting the RI of TCs over the western North Pacific and the South China Sea (SCS) in operational forecasting, while noting other contributing environmental factors and synoptic flow patterns conducive to RI. This study represents the first documentation of sub-surface salinity’s impact on some intense TCs traversing the SCS during 2017–2023 based on an observational study. Our aim is to supplement operational techniques for forecasting RI with some quantitative guidance based on upper-level ocean observations of temperatures and salinity, on top of well-known but more rapidly changing dynamical factors like low-level convergence, weak vertical wind shear, and upper-level divergent outflow, as forecasted with numerical weather prediction models. This study will also encourage further research to refine the analysis of quantitative contributions from different RI factors and the identification of essential features for developing AI models as one way to improve the forecasting of TC RI before the TC makes landfall near the PRD, with due consideration given to the effect of freshwater river discharge from the Pearl River. Full article
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Figure 1
<p>A flowchart showing the data and methodology used in this study.</p>
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<p>Five TCs that traversed the WNP and the SCS, viz. Super Typhoon Hato (20–24 August 2017), Super Typhoon Mangkhut (7–17 September 2018), Typhoon Talim (14–18 July 2023), Super Typhoon Saola (24 August–3 September 2023), and Severe Typhoon Koinu (29 September–9 October 2023), in the area of study. Blue lines in the inset show the three main tributaries of the Pearl River.</p>
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<p>Data and methodology used in CORA2.</p>
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<p>(<b>a</b>) The HKO-analysed track and intensity of TC Hato overlaid on the SST distribution on 20 August 2017 before its passage over the WNP; (<b>b</b>) a snapshot of the vertical profile taken on the same day showing sea temperatures over a sea depth of about 150 m along the TC track with location points as shown in (<b>a</b>). A change in the TC category [<a href="#B50-atmosphere-15-00674" class="html-bibr">50</a>] with the central maximum wind in brackets is also marked on the time line. TC symbol in different colours show different TC categories (TD in black; TS in green, STS in blue, T in red, ST in pink, SuperT in purple) at the time.</p>
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<p>The same as <a href="#atmosphere-15-00674-f004" class="html-fig">Figure 4</a>, but replacing SST with SSS.</p>
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<p>(<b>a</b>) Vertical profile of sea temperatures over a depth of about 150 m below the sea surface shown at locations traversed by TC Mangkhut over the WNP and the SCS (<b>a</b>) before its passage on 7 September 2018, and (<b>b</b>) after its passage on 17 September 2018. The meanings of the annotations are the same as those described for <a href="#atmosphere-15-00674-f004" class="html-fig">Figure 4</a>b. (<b>c</b>) HKO-analysed track and intensity of TC Mangkhut overlaid on SST data for 17 September 2018 after its passage. The dotted blue box marks the region over the WNP where the cooling of the SST was significant.</p>
Full article ">Figure 6 Cont.
<p>(<b>a</b>) Vertical profile of sea temperatures over a depth of about 150 m below the sea surface shown at locations traversed by TC Mangkhut over the WNP and the SCS (<b>a</b>) before its passage on 7 September 2018, and (<b>b</b>) after its passage on 17 September 2018. The meanings of the annotations are the same as those described for <a href="#atmosphere-15-00674-f004" class="html-fig">Figure 4</a>b. (<b>c</b>) HKO-analysed track and intensity of TC Mangkhut overlaid on SST data for 17 September 2018 after its passage. The dotted blue box marks the region over the WNP where the cooling of the SST was significant.</p>
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<p>The same as <a href="#atmosphere-15-00674-f006" class="html-fig">Figure 6</a> (<b>a</b>) and (<b>b</b>), respectively, but replacing SST with SSS. The meanings of the annotations are the same as those described for <a href="#atmosphere-15-00674-f004" class="html-fig">Figure 4</a>b.</p>
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<p>The same as <a href="#atmosphere-15-00674-f004" class="html-fig">Figure 4</a>a, but with TC Talim overlaid on the SST distribution (<b>a</b>) on 14 July 2023 before its passage over the SCS; (<b>b</b>) on 16 July 2023 in the middle of its life history; and (<b>c</b>) on 18 July 2023 after its passage.</p>
Full article ">Figure 8 Cont.
<p>The same as <a href="#atmosphere-15-00674-f004" class="html-fig">Figure 4</a>a, but with TC Talim overlaid on the SST distribution (<b>a</b>) on 14 July 2023 before its passage over the SCS; (<b>b</b>) on 16 July 2023 in the middle of its life history; and (<b>c</b>) on 18 July 2023 after its passage.</p>
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<p>Vertical profile of (<b>a</b>) sea temperatures and (<b>b</b>) salinity over a depth of about 150 m below the sea surface on 14 July 2023 before the passage of TC Talim over the SCS at the locations it traversed. The meanings of the annotations are the same as those described for <a href="#atmosphere-15-00674-f004" class="html-fig">Figure 4</a>b.</p>
Full article ">Figure 9 Cont.
<p>Vertical profile of (<b>a</b>) sea temperatures and (<b>b</b>) salinity over a depth of about 150 m below the sea surface on 14 July 2023 before the passage of TC Talim over the SCS at the locations it traversed. The meanings of the annotations are the same as those described for <a href="#atmosphere-15-00674-f004" class="html-fig">Figure 4</a>b.</p>
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<p>(<b>a</b>) The same as <a href="#atmosphere-15-00674-f004" class="html-fig">Figure 4</a>a, but with TC Saola overlaid on the SST distribution (<b>a</b>) on 24 August 2023 before its passage over the WNP; and vertical profiles of (<b>b</b>) the sea temperatures and (<b>c</b>) the salinity over a depth of about 150 m below the sea surface on 24 August 2023 before its passage over the WNP and the SCS at the locations it traversed, as shown in (<b>a</b>). The meanings of the annotations are the same as those described for <a href="#atmosphere-15-00674-f004" class="html-fig">Figure 4</a>b.</p>
Full article ">Figure 10 Cont.
<p>(<b>a</b>) The same as <a href="#atmosphere-15-00674-f004" class="html-fig">Figure 4</a>a, but with TC Saola overlaid on the SST distribution (<b>a</b>) on 24 August 2023 before its passage over the WNP; and vertical profiles of (<b>b</b>) the sea temperatures and (<b>c</b>) the salinity over a depth of about 150 m below the sea surface on 24 August 2023 before its passage over the WNP and the SCS at the locations it traversed, as shown in (<b>a</b>). The meanings of the annotations are the same as those described for <a href="#atmosphere-15-00674-f004" class="html-fig">Figure 4</a>b.</p>
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<p>(<b>a</b>) The same as <a href="#atmosphere-15-00674-f004" class="html-fig">Figure 4</a>a, except with TC Koinu overlaid on the SST distribution (<b>a</b>) on 30 September 2023 before its passage over the WNP; and vertical profiles of (<b>b</b>) the sea temperatures and (<b>c</b>) the salinity over a depth of about 150 m below the sea surface on 30 September 2023 before its passage over the WNP and the SCS at the locations it traversed, as shown in (<b>a</b>). The meanings of the annotations are the same as those described for <a href="#atmosphere-15-00674-f004" class="html-fig">Figure 4</a>b.</p>
Full article ">Figure 11 Cont.
<p>(<b>a</b>) The same as <a href="#atmosphere-15-00674-f004" class="html-fig">Figure 4</a>a, except with TC Koinu overlaid on the SST distribution (<b>a</b>) on 30 September 2023 before its passage over the WNP; and vertical profiles of (<b>b</b>) the sea temperatures and (<b>c</b>) the salinity over a depth of about 150 m below the sea surface on 30 September 2023 before its passage over the WNP and the SCS at the locations it traversed, as shown in (<b>a</b>). The meanings of the annotations are the same as those described for <a href="#atmosphere-15-00674-f004" class="html-fig">Figure 4</a>b.</p>
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17 pages, 7771 KiB  
Article
Near-Surface Dispersion and Current Observations Using Dye, Drifters, and HF Radar in Coastal Waters
by Keunyong Kim, Hong Thi My Tran, Kyu-Min Song, Young Baek Son, Young-Gyu Park, Joo-Hyung Ryu, Geun-Ho Kwak and Jun Myoung Choi
Remote Sens. 2024, 16(11), 1985; https://doi.org/10.3390/rs16111985 - 31 May 2024
Viewed by 512
Abstract
This study explores the near-surface dispersion mechanisms of contaminants in coastal waters, leveraging a comprehensive method that includes using dye and drifters as tracers, coupled with diverse observational platforms like drones, satellites, in situ sampling, and HF radar. The aim is to deepen [...] Read more.
This study explores the near-surface dispersion mechanisms of contaminants in coastal waters, leveraging a comprehensive method that includes using dye and drifters as tracers, coupled with diverse observational platforms like drones, satellites, in situ sampling, and HF radar. The aim is to deepen our understanding of surface currents’ impact on contaminant dispersion, thereby improving predictive models for managing environmental incidents such as pollutant releases. Rhodamine WT dye, chosen for its significant fluorescent properties and detectability, along with drifter data, allowed us to investigate the dynamics of near-surface physical phenomena such as the Ekman current, Stokes drift, and wind-driven currents. Our research emphasizes the importance of integrating scalar tracers and Lagrangian markers in experimental designs, revealing differential dispersion behaviors due to near-surface vertical shear caused by the Ekman current and Stokes drift. During slow-current conditions, the elongation direction of the dye patch aligned well with the direction of a depth-averaged Ekman spiral, or Ekman transport. Analytical calculations of vertical shear, based on the Ekman current and Stokes drift, closely matched those derived from tracer observations. Over a 7 h experiment, the vertical diffusivity near the surface was first observed at the early stages of scalar mixing, with a value of 1.9×104 m2/s, and the horizontal eddy diffusivity of the dye patch and drifters reached the order of 1 m2/s at a 1000 m length scale. Particle tracking models demonstrate that while HF radar currents can effectively predict the trajectories of tracers near the surface, incorporating near-surface currents, including the Ekman current, Stokes drift, and windage, is essential for a more accurate prediction of the fate of surface floats. Full article
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Figure 1

Figure 1
<p>Study area. The domain of (<b>b</b>) indicates the area within a rectangle in (<b>a</b>). The gray-shaded area represents the coverage of HF radar in Yeosu Bay. The ‘x’ marks the initial dye release location. The red dot denotes the nearby buoy location where wind and wave data were collected. The black dots denote the locations of two HF radars.</p>
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<p>(<b>a</b>) Wind speed measured from R/V Research 1 and (<b>b</b>) corresponding wind stress. (<b>c</b>) Significant wave height as measured by the nearby buoy indicated in <a href="#remotesensing-16-01985-f001" class="html-fig">Figure 1</a>. The dye monitoring experiment was conducted during the period highlighted by two red lines. Variables <span class="html-italic">u</span> and <span class="html-italic">v</span> represent wind velocities in the east–west (EW) and north–south (NS) directions, respectively, while <span class="html-italic">U</span> denotes the magnitude of wind speed. <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mi>x</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mi>y</mi> </mrow> </msub> </mrow> </semantics></math> indicate wind stresses in the EW and NS directions, with <math display="inline"><semantics> <mrow> <mi>τ</mi> </mrow> </semantics></math> representing the overall magnitude of wind stress.</p>
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<p>(<b>a</b>–<b>d</b>) Dye and drifter trajectories overlaid on the background of HF radar current (HFR). Comparison of Eulerian HFR and tidal velocities (modeled from the TMD MATLAB toolbox version 2.5) at a fixed location near the dye release point in the east–west direction (<b>e</b>) and north–south direction (<b>f</b>). Panels (<b>g</b>,<b>h</b>) compare the Eulerian velocities in (<b>e</b>,<b>f</b>) with Lagrangian observational velocities during the experiment duration, marked by two gray lines in (<b>e</b>,<b>f</b>). Wind speeds in (<b>g</b>,<b>h</b>) are scaled down by a factor of 20.</p>
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<p>(<b>a</b>) Displacement of the center of the dye patch (‘○’), float (‘<math display="inline"><semantics> <mrow> <mo>×</mo> </mrow> </semantics></math>’), and drifters (‘∙’). The square indicates the release location, and the three concurrent locations are connected by lines. Displacements in the east–west direction (<b>b</b>) and north–south direction (<b>c</b>).</p>
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<p>Temporal evolution of the dye patch distribution at different times, with size and direction of the ellipse calculated from the principal axis analysis. The ellipse comprises two axes: the length of the longer axis is 3<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>m</mi> <mi>a</mi> </mrow> </msub> </mrow> </semantics></math> and that of the shorter axis is 3<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>m</mi> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math>. The angle, measured from the southward direction rotating clockwise, is presented in <a href="#remotesensing-16-01985-t001" class="html-table">Table 1</a>. The actual size of all distributions can be estimated along the x and y axes, with insets providing magnified views of the distributions.</p>
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<p>Vertical dye concentration distributions at 10 min (<b>a</b>) and 4 h (<b>b</b>) after the release, as measured by a fluorometer installed on the SCAMP.</p>
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<p>(<b>a</b>) Raw RGB images taken by a drone 2.3 h post-dye release (around 11:30 a.m.); (<b>b</b>) a magnified segment of (<b>a</b>), converted to grayscale to emphasize the wave propagation direction. The angle between the two vectors is approximately 45 degrees.</p>
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<p>Estimations of near-surface currents driven by the Ekman current and Stokes drift by analytical calculations (<b>a</b>) and the corresponding vertical shear (<b>b</b>) in the direction of the transect oriented 45 degrees from the southward direction. ‘Total’ represents the sum of the Ekman and Stokes components. Two dots represent the estimations of mean vertical shear derived from the mean velocity difference between the float and drifters (S1) and between the drifters and dye (S2).</p>
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<p>Shear estimation using two tracers (float and drifter). S1 and S2 represent the shear calculated from the velocity difference between the float and drifters (S1) and between the drifters and dye (S2). The subscripts ‘<span class="html-italic">y</span>’ and ‘<span class="html-italic">x</span> + <span class="html-italic">y</span>’ indicate the <span class="html-italic">y</span> direction (north–south or NS) component and the total sum of the NS and east–west (EW) components, respectively. <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>w</mi> <mi>i</mi> <mi>n</mi> <mi>d</mi> <mo> </mo> </mrow> </msub> </mrow> </semantics></math> denotes the total wind speed, and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>v</mi> </mrow> <mrow> <mi>w</mi> <mi>i</mi> <mi>n</mi> <mi>d</mi> </mrow> </msub> </mrow> </semantics></math> represents the NS component of the wind speed. The axis for wind speed is on the right side.</p>
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<p>(<b>a</b>) Second moments of distribution and (<b>b</b>) dispersion coefficient (<span class="html-italic">K</span>) over time for three tracers: a dye patch, a combination of float and drifters (‘f + d’), and drifters only (‘d’). The total variance <math display="inline"><semantics> <mrow> <mo>(</mo> <msup> <mrow> <mi>σ</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> <mo>)</mo> </mrow> </semantics></math> of dye concentration distribution is calculated as <math display="inline"><semantics> <mrow> <mo>(</mo> <msup> <mrow> <mi>σ</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> <mo>=</mo> <mn>2</mn> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>m</mi> <mi>a</mi> </mrow> </msub> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>m</mi> <mi>i</mi> </mrow> </msub> <mo>)</mo> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msup> <mrow> <mi>R</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </mrow> </semantics></math> represents the relative dispersion of Lagrangian tracers. (<b>c</b>) Variation of the dispersion coefficient (<span class="html-italic">K</span>) with the horizontal length scale (<math display="inline"><semantics> <mrow> <mi>l</mi> <mo>=</mo> <mn>3</mn> <mi mathvariant="sans-serif">σ</mi> </mrow> </semantics></math>) of dye patch.</p>
Full article ">Figure 11
<p>Estimated trajectories from the particle tracking model. HFR, EK, ST, and W indicate current components from HF radar, Ekman current, Stokes drift, and 1% of wind speed, respectively. The gray shaded area indicates the dye patch.</p>
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