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22 pages, 19530 KiB  
Article
Cascading Landslide: Kinematic and Finite Element Method Analysis through Remote Sensing Techniques
by Claudia Zito, Massimo Mangifesta, Mirko Francioni, Luigi Guerriero, Diego Di Martire, Domenico Calcaterra and Nicola Sciarra
Remote Sens. 2024, 16(18), 3423; https://doi.org/10.3390/rs16183423 - 14 Sep 2024
Viewed by 265
Abstract
Cascading landslides are specific multi-hazard events in which a primary movement triggers successive landslide processes. Areas with dynamic and quickly changing environments are more prone to this type of phenomena. Both the kind and the evolution velocity of a landslide depends on the [...] Read more.
Cascading landslides are specific multi-hazard events in which a primary movement triggers successive landslide processes. Areas with dynamic and quickly changing environments are more prone to this type of phenomena. Both the kind and the evolution velocity of a landslide depends on the materials involved. Indeed, rockfalls are generated when rocks fall from a very steep slope, while debris flow and/or mudslides are generated by fine materials like silt and clay after strong water imbibition. These events can amplify the damage caused by the initial trigger and propagate instability along a slope, often resulting in significant environmental and societal impacts. The Morino-Rendinara cascading landslide, situated in the Ernici Mountains along the border of the Abruzzo and Lazio regions (Italy), serves as a notable example of the complexities and devastating consequences associated with such events. In March 2021, a substantial debris flow event obstructed the Liri River, marking the latest step in a series of landslide events. Conventional techniques such as geomorphological observations and geological surveys may not provide exhaustive information to explain the landslide phenomena in progress. For this reason, UAV image acquisition, InSAR interferometry, and pixel offset analysis can be used to improve the knowledge of the mechanism and kinematics of landslide events. In this work, the interferometric data ranged from 3 January 2020 to 24 March 2023, while the pixel offset data covered the period from 2016 to 2022. The choice of such an extensive data window provided comprehensive insight into the investigated events, including the possibility of identifying other unrecorded events and aiding in the development of more effective mitigation strategies. Furthermore, to supplement the analysis, a specific finite element method for slope stability analysis was used to reconstruct the deep geometry of the system, emphasizing the effect of groundwater-level flow on slope stability. All of the findings indicate that major landslide activities were concentrated during the heavy rainfall season, with movements ranging from several centimeters per year. These results were consistent with numerical analyses, which showed that the potential slip surface became significantly more unstable when the water table was elevated. Full article
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Figure 1

Figure 1
<p>Aerial and field images of the Morino-Rendinara landslide that are representative of the impact of the landslide on the environment. (<b>a</b>) Overview of phenomenon taken from Google Earth [<a href="#B16-remotesensing-16-03423" class="html-bibr">16</a>] satellite images of 13 June 2022, from the upper sector near Morino Hamlet to the lower sector, Liri River, and deep-seated rotational slide; (<b>b</b>) Details of rockfall/avalanches sector; (<b>c</b>) Debris flow source area; (<b>d</b>) Debris flow transit zone; (<b>e</b>) Lowest debris flow transit zone; (<b>f</b>) Liri River dam; and (<b>g</b>) Effect on Liri River dam.</p>
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<p>Geographical location of Morino-Rendinara. Green lines indicate the regional boundaries; red lines indicate the municipality of Morino, Castronovo, and San Vincenzo Valle Roveto composing the involved municipality; the light blue square indicates the landslide and the study area.</p>
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<p>Geological map extract from the CARG (Geological CARtography Map n.220 Sora) Project [<a href="#B22-remotesensing-16-03423" class="html-bibr">22</a>], with indications of the geological formations and tectonic processes present in the area.</p>
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<p>Maps of the survey and debritic cover layer reconstruction using cross-sections to empathize the heterogeneity of deposits covering the substrate. (a) The section develops on maximum slope line. (b) The section develops on perpendicular direction.</p>
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<p>Conceptual flow chart of the work phases.</p>
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<p>Spatiotemporal baseline map of SBAS-InSAR interferometric data of ascending track (<b>a</b>) and descending track (<b>b</b>).</p>
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<p>The inventory map was drawn using the results of the study. The image identifies three main mechanisms: a rockfall in the upper part, a deep-seated rotational slide in the central part, and a debris flow in the lower part.</p>
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<p>Details of the landslide inventory map. The various fillings show the different landslide types identified in the study area: the rockfall in the upper part, the deep-seated rotational slide in the central part, and the debris flow in the lower part of the slope.</p>
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<p>PS velocity along the ascending (<b>a</b>) and descending (<b>b</b>) geometries from 2020 to 2023. Red dots indicate major velocity trends and instability, green and blue dots indicate minor velocity and stable sectors.</p>
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<p>Selected time series in ascending geometry (<b>a</b>) and descending geometry (<b>b</b>). The analyzed time series illustrates a very unstable sector represented by reflectors P106_60-61 and 102_57 in ascending geometry and P_70_141-141-136 in descending geometry. Additionally, some stable sectors are represented, such as P_83_135-143, P_85_68, and P_86_69.</p>
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<p>Time series vs. rainfall analyses. (<b>a</b>) Monthly cumulative rainfall for analysis period vs. one ascending and descending representative time series; (<b>b</b>) Daily cumulative rainfall for analysis period vs. one ascending and descending representative time series; (<b>c</b>) Cumulative rainfall for analysis period vs. one ascending and descending representative time series.</p>
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<p>Shear strain index results of the 2D numerical modelling with different pore pressure conditions. (<b>a</b>) Analysis without pore pressure; (<b>b</b>) Analysis with water table 0.5 m from ground level; (<b>c</b>) Analysis with water table 2.0 m from ground level; (<b>d</b>) Analysis with water table 6.0 m from ground level.</p>
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26 pages, 11662 KiB  
Article
Advanced Numerical Simulation of Scour around Bridge Piers: Effects of Pier Geometry and Debris on Scour Depth
by Muhanad Al-Jubouri, Richard P. Ray and Ethar H. Abbas
J. Mar. Sci. Eng. 2024, 12(9), 1637; https://doi.org/10.3390/jmse12091637 - 13 Sep 2024
Viewed by 259
Abstract
Investigating different pier shapes and debris Finteractions in scour patterns is vital for understanding the risks to bridge stability. This study investigates the impact of different shapes of pier and debris interactions on scour patterns using numerical simulations with flow-3D and controlled laboratory [...] Read more.
Investigating different pier shapes and debris Finteractions in scour patterns is vital for understanding the risks to bridge stability. This study investigates the impact of different shapes of pier and debris interactions on scour patterns using numerical simulations with flow-3D and controlled laboratory experiments. The model setup is rigorously calibrated against a physical flume experiment, incorporating a steady-state flow as the initial condition for sediment transport simulations. The Fractional Area/Volume Obstacle Representation (FAVOR) technique and the renormalized group (RNG) turbulence model enhance the simulation’s precision. The numerical results indicate that pier geometry is a critical factor influencing the scour depth. Among the tested shapes, square piers exhibit the most severe scour, with depths reaching 5.8 cm, while lenticular piers show the least scour, with a maximum depth of 2.5 cm. The study also highlights the role of horseshoe, wake, and shear layer vortices in determining scour locations, with varying impacts across different pier shapes. The Q-criterion study identified debris-induced vortex generation and intensification. The debris amount, thickness, and pier diameter (T/Y) significantly affect the scouring patterns. When dealing with high wedge (HW) debris, square piers have the largest scour depth at T/Y = 0.25, while lenticular piers exhibit a lower scour. When debris is present, the scour depth rises at T/Y = 0.5. Depending on the form of the debris, a significant fluctuation of up to 5 cm was reported. There are difficulties in precisely estimating the scour depth under complicated circumstances because of the disparity between numerical simulations and actual data, which varies from 6% for square piers with a debris relative thickness T/Y = 0.25 to 32% for cylindrical piers with T/Y = 0.5. The study demonstrates that while flow-3D simulations align reasonably well with the experimental data under a low debris impact, discrepancies increase with more complex debris interactions and higher submersion depths, particularly for cylindrical piers. The novelty of this work lies in its comprehensive approach to evaluating the effects of different pier shapes and debris interactions on scour patterns, offering new insights into the effectiveness of flow-3D simulations in predicting the scour patterns under varying conditions. Full article
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Figure 1
<p>Experimental flume for investigating turbulent characteristics and scour processes around varied pier geometries.</p>
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<p>Pier shapes are employed in experimental and numerical models including, (<b>A</b>) lenticular (LE), (<b>B</b>) ogival (OG), (<b>C</b>) rectangular (RE), (<b>D</b>) oblong (OB), (<b>E</b>) elliptical (EL), (<b>F</b>) octagonal (OC), (<b>G</b>) double-circular (DC), (<b>H</b>) Joukasaky (JO), (<b>I</b>) rectangular chamfered (REC), (<b>J</b>) diamond (DI) and square (S), and (<b>K</b>) polygonal (PO) shapes.</p>
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<p>Six uniform debris configurations were employed during the experiments, (<b>A</b>) rectangle, (<b>B</b>) tringle bow, (<b>C</b>) high wedge, (<b>D</b>) low wedge, (<b>E</b>) triangle yield, and (<b>F</b>) half circle.</p>
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<p>Detailed view of the computational domain with mesh representation: (<b>a</b>) top view, (<b>b</b>) side view, and (<b>c</b>) front view.</p>
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<p>The FAVOR technique in flow-3D meshing includes (<b>a</b>) square and cylindrical piers with six debris shapes and (<b>b</b>) various pier shapes without debris accumulations.</p>
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<p>The maximum relative scour depth (Z<sub>s</sub>/D) following encounters with debris for (<span class="html-italic">T</span>/<span class="html-italic">Y</span> = 0.25).</p>
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<p>The maximum relative scour depth (Z<sub>s</sub>/D) following encounters with debris for (<span class="html-italic">T</span>/<span class="html-italic">Y</span> = 0.5).</p>
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<p>The maximum relative scour depth (Z<sub>s</sub>/D) following encounters with debris for (<span class="html-italic">T</span>/<span class="html-italic">Y</span> = 1).</p>
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<p>Analysis of the shape factor for thirteen distinct pier geometries developed from [<a href="#B18-jmse-12-01637" class="html-bibr">18</a>].</p>
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<p>Standard deviation analysis of average scour depth for different pier shapes.</p>
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<p>Standard deviation analysis of average scour depth for different debris shapes.</p>
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<p>The local scour distribution around the different piers shapes includes (<b>a</b>) square, (<b>b</b>) rectangular, (<b>c</b>) rectangular chamfered, (<b>d</b>) diamond, (<b>e</b>) polygonal, (<b>f</b>) cylindrical, (<b>g</b>) octagonal, (<b>h</b>) double–circular, (<b>i</b>) Joukasaky, (<b>j</b>) elliptical, (<b>k</b>) ogival, (<b>l</b>) oblong, and (<b>m</b>) lenticular.</p>
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<p>The local scour distribution around the different piers shapes includes (<b>a</b>) square, (<b>b</b>) rectangular, (<b>c</b>) rectangular chamfered, (<b>d</b>) diamond, (<b>e</b>) polygonal, (<b>f</b>) cylindrical, (<b>g</b>) octagonal, (<b>h</b>) double–circular, (<b>i</b>) Joukasaky, (<b>j</b>) elliptical, (<b>k</b>) ogival, (<b>l</b>) oblong, and (<b>m</b>) lenticular.</p>
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<p>The flow velocity patterns around different pier shapes.</p>
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<p>Comparative scour depth value for square and cylindrical piers across various debris shapes when debris relative thickness (<span class="html-italic">T</span>/<span class="html-italic">Y</span>) cases equal to 0.25.</p>
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<p>Comparative scour depth value for square and cylindrical piers across various debris shapes when debris relative thickness (<span class="html-italic">T</span>/<span class="html-italic">Y</span>) cases equal to 0.5.</p>
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<p>Q–criterion flow visualization around a cylindrical pier includes (<b>a</b>) without debris, (<b>b</b>) 3 cm thick rectangular debris, and (<b>c</b>) 6 cm thick rectangular debris.</p>
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<p>Assessing flow-3D’s accuracy in predicting scour depths for cylindrical piers: a detailed examination at different (<span class="html-italic">T</span>/<span class="html-italic">Y</span>) ratios.</p>
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19 pages, 19608 KiB  
Article
Identifying the Characteristics and Implications of Post-Earthquake Debris Flow Events Based on Multi-Source Remote Sensing Images
by Wen Jin, Guotao Zhang, Yi Ding, Nanjiang Liu and Xiaowei Huo
Remote Sens. 2024, 16(17), 3336; https://doi.org/10.3390/rs16173336 - 8 Sep 2024
Viewed by 450
Abstract
Strong earthquakes often bring amounts of loose material, disrupting the balance of material transportation within a watershed and severely impacting the restoration of the ecological environment and human safety downstream. Therefore, it is crucial to identify the frequency and scale of these debris [...] Read more.
Strong earthquakes often bring amounts of loose material, disrupting the balance of material transportation within a watershed and severely impacting the restoration of the ecological environment and human safety downstream. Therefore, it is crucial to identify the frequency and scale of these debris flow events, as well as to explore their long-term development and impact on internal and external channels. Using multi-source remote sensing images from four perspectives, hillslope, channel, accumulation fan, and their relationship with the mainstream, we reconstructed a debris flow event dataset from 2008 to 2020, explored a method for identifying these events, and analyzed their impacts on channels and accumulation fans in Mozi Gully affected by the Wenchuan earthquake. Loose matter was predominantly found in areas proximate to the channel and at lower elevations during debris flow events. Alterations in channel width, accumulation fans downstream, and their potential to obstruct rivers proved to be vital for identifying the large scale of debris flow event. Finally, we encapsulated the evolution patterns and constraints of post-earthquake debris flows. Determination in frequency and scale could offer valuable supplementary data for scenario hypothesis parameters in post-earthquake disaster engineering prevention and control. Full article
(This article belongs to the Special Issue Study on Hydrological Hazards Based on Multi-source Remote Sensing)
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Figure 1
<p>Location of Mozi Gully, where (<b>a</b>) shows the location of Mozi Gully in Wenchuan, Sichuan, China as the yellow rectangle; (<b>b</b>) shows the detailed information of Mozi Gully and the relationship between Mozi Gully and Min River; (<b>c</b>) shows the topographic relief, channel profile, and the detailed display of local characteristics in Mozi Gully.</p>
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<p>Flowchart for building a debris flow event database using multi-source remote sensing data.</p>
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<p>Distribution of the landslide of different post-earthquake periods in Mozi Gully. (<b>a</b>–<b>n</b>) shows the distribution of landslides from 2007 to 2020 in Mozi Gully.</p>
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<p>Distribution and development of the loose materials located in hillslopes of different post-earthquake periods in Mozi Gully. There is the relationship between the CPLM and the distribution of loose materials, including the distance from channel (DC, (<b>a</b>)), slope (S, (<b>b</b>)), and elevation (E, (<b>c</b>)). Some typical cumulative ratios were selected to analyze the annual changes in landslide locations, which are shown in (<b>d</b>–<b>f</b>).</p>
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<p>Change in the channel width downstream of Mozi Gully. (<b>a</b>) Changes in channel width in different periods; (<b>b</b>) change in channel width in different periods; (<b>c</b>) maximum width of accumulation fan in different periods.</p>
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<p>Characteristics of the channel before and after debris flow occurred on 20 August 2019. (<b>a</b>) This image was taken on 7 June 2019, by UAV; (<b>b</b>) this image was taken on 24 August 2019; (<b>c</b>) and (<b>d</b>) were the profile of A-A’ and B-B’ in a, respectively.</p>
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<p>Characteristics of the channel before and after debris flow occurred on 2020. The images used in (<b>a</b>,<b>d</b>) were taken by UAV on 9 May 2020 and the images used in (<b>b</b>,<b>e</b>) were taken on 6 September 2020. (<b>c</b>,<b>f</b>) are the profile of A-A’ and B-B’, respectively.</p>
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<p>Sketch for reconstruction of debris flow events using multi-source remote sensing data.</p>
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<p>Evolution patterns of post-earthquake debris flow in Mozi Gully. (<b>a</b>) Schematic diagram of the Mozi Gully before the Wenchuan earthquake. (<b>b</b>) Development of debris flows and distribution of loose materials after earthquakes in Mozi Gully from 2008 to 2013. (<b>c</b>) Development of debris flows and distribution of loose materials after earthquakes in Mozi Gully from 2014 to 2018. (<b>d</b>) Development of debris flows and distribution of loose materials after earthquakes in Mozi Gully after 2019.</p>
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<p>Changes in critical rainfall for triggering debris flows and regional annual precipitation: (<b>a</b>) shows the increased critical rainfall for triggering debris flows in Er Gully, which is only separated by a mountain with Mozi Gully. The data used in (<b>a</b>) were obtained from Guo, et al. [<a href="#B19-remotesensing-16-03336" class="html-bibr">19</a>]; (<b>b</b>) shows the change in annual precipitation in affected area of Wenchuan earthquake. The precipitation was obtained from Intensification Innovation Lab (<a href="https://www.worldclim.org/data/worldclim21.html" target="_blank">https://www.worldclim.org/data/worldclim21.html</a>, accessed on 24 August 2024) [<a href="#B55-remotesensing-16-03336" class="html-bibr">55</a>].</p>
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22 pages, 9330 KiB  
Article
Monitoring and Evaluation of Debris Flow Disaster in the Loess Plateau Area of China: A Case Study
by Baofeng Wan, Ning An and Gexue Bai
Water 2024, 16(17), 2539; https://doi.org/10.3390/w16172539 - 8 Sep 2024
Viewed by 444
Abstract
The Loess Plateau area, with complex geomorphological features and geological structure, is highly prone to geologic disasters such as landslides and debris flow, which cause great losses. To investigate the initiation mechanism of landslide and debris flow disasters and their spreading patterns, historical [...] Read more.
The Loess Plateau area, with complex geomorphological features and geological structure, is highly prone to geologic disasters such as landslides and debris flow, which cause great losses. To investigate the initiation mechanism of landslide and debris flow disasters and their spreading patterns, historical satellite images in the Laolang gully were collected and digitized to generate three-dimensional topographic and geomorphological maps. Typical landslides were selected for landslide thickness measurement using a standard penetrometer and high-density electrical method. Numerical models were established to simulate the occurrence and development of landslides under different working conditions and to evaluate the spreading range based on the propagation algorithm and friction law. The results show that the 10 m resolution DEM data are well matched with the potential hazard events observed in the field site. The smaller the critical slope threshold, the greater the extent and distance of landslide spreading. The larger the angle of arrival, the greater the energy loss, and therefore the smaller the landslide movement distance. The results can provide scientific theoretical guidance for the prevention and control of rainfall-induced landslide and debris flow disasters in the Loess Plateau area. Full article
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Figure 1
<p>(<b>a</b>) The distribution of the landslides; (<b>b</b>) Thickness measurements using a standard penetrometer and high-density electrical method; The uppercase letters A to D are the boundaries of the electrical test line; The lowercase letters a to s are the standard penetrometer test point, respectively.</p>
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<p>The measuring results of the hit numbers and penetration depth for the 19 selected landslides. (<b>a</b>–<b>s</b>) are the serial number of the penetrometer test point, respectively.</p>
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<p>The measuring results of the hit numbers and penetration depth for the 19 selected landslides. (<b>a</b>–<b>s</b>) are the serial number of the penetrometer test point, respectively.</p>
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<p>The measuring results of the high-density electrical method: (<b>a</b>) Section A–B; (<b>b</b>) Section C–D.</p>
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<p>Disaster area distributions under flood conditions identified in DEM data at different resolutions.</p>
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<p>Spreading results under flood conditions identified in DEM data at different resolutions.</p>
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<p>Disaster area distributions under collapse-affected debris flow conditions identified in DEM data at different resolutions.</p>
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<p>Spreading results under collapse-induced debris flow conditions identified in DEM data at different resolutions.</p>
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<p>Disaster area distributions under landslide-induced debris flow conditions identified in DEM data at different resolutions.</p>
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<p>Spreading results under landslide-induced debris flow conditions identified in DEM data at different resolutions.</p>
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<p>Disaster area distributions under the simultaneous influence of collapse and landslide conditions identified in DEM data at different resolutions.</p>
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<p>Spreading results under the simultaneous influence of collapse and landslide conditions identified in DEM data at different resolutions.</p>
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<p>Disaster area distributions under flood conditions at different critical slope thresholds.</p>
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<p>Spreading results under flood conditions at different critical slope thresholds.</p>
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<p>Disaster area distributions under the simultaneous influence of collapse and landslide conditions at different critical slope thresholds.</p>
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<p>Spreading results under the simultaneous influence of collapse and landslide conditions at different critical slope thresholds.</p>
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<p>Spreading results under the simultaneous influence of collapse and landslide conditions at different index x values.</p>
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<p>Spreading results under flood conditions at different index x values.</p>
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<p>Spreading results of flooding under different arrival angles.</p>
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<p>Spreading results of debris flow under different arrival angles.</p>
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20 pages, 2618 KiB  
Article
Enhanced Tailings Dam Beach Line Indicator Observation and Stability Numerical Analysis: An Approach Integrating UAV Photogrammetry and CNNs
by Kun Wang, Zheng Zhang, Xiuzhi Yang, Di Wang, Liyi Zhu and Shuai Yuan
Remote Sens. 2024, 16(17), 3264; https://doi.org/10.3390/rs16173264 - 3 Sep 2024
Viewed by 341
Abstract
Tailings ponds are recognized as significant sources of potential man-made debris flow and major environmental disasters. Recent frequent tailings dam failures and growing trends in fine tailings outputs underscore the critical need for innovative monitoring and safety management techniques. Here, we propose an [...] Read more.
Tailings ponds are recognized as significant sources of potential man-made debris flow and major environmental disasters. Recent frequent tailings dam failures and growing trends in fine tailings outputs underscore the critical need for innovative monitoring and safety management techniques. Here, we propose an approach that integrates UAV photogrammetry with convolutional neural networks (CNNs) to extract beach line indicators (BLIs) and conduct enhanced dam safety evaluations. The significance of real 3D geometry construction in numerical analysis is investigated. The results demonstrate that the optimized You Only Look At CoefficienTs (YOLACT) model outperforms in recognizing the beach boundary line, achieving a mean Intersection over Union (mIoU) of 72.63% and a mean Pixel Accuracy (mPA) of 76.2%. This approach shows promise for future integration with autonomously charging UAVs, enabling comprehensive coverage and automated monitoring of BLIs. Additionally, the anti-slide and seepage stability evaluations are impacted by the geometry shape and water condition configuration. The proposed approach provides more conservative seepage calculations, suggesting that simplified 2D modeling may underestimate tailings dam stability, potentially affecting dam designs and regulatory decisions. Multiple numerical methods are suggested for cross-validation. This approach is crucial for balancing safety regulations with economic feasibility, helping to prevent excessive and unsustainable burdens on enterprises and advancing towards the goal of zero harm to people and the environment in tailings management. Full article
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Graphical abstract

Graphical abstract
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<p>Diagram illustrating common BLIs monitoring methods. (<b>a</b>) Equidistant beach width sign placed along the beach section used for the visual identification method. (<b>b</b>) The radar level gauge method, illustrating its setup and data collection technique. (<b>c</b>) The laser ranging method, illustrating the device setup at the dam crest, the measurement of the irradiated laser beam angle <math display="inline"><semantics> <mi>α</mi> </semantics></math>, and the distance <span class="html-italic">l</span> to the decant pond boundary. (<b>d</b>) The seepage backcalculation method, illustrating the placement of piezometers and the principle for calculating the BLIs. (<b>e</b>) Arrangement layout of the tailings pond BLIs monitoring devices.</p>
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<p>Optimized YOLACT model workflow. Here, “+” denotes elementwise addition, “2×” indicates twice linear upsampling,“DWconv” stands for depthwise convolution, and “BN” refers to batch normalization.</p>
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<p>Optimized DeepLabV3+ model workflow.</p>
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<p>Information and UAV aerial survey results of the tailings pond in the case study. (<b>a</b>) Geographical location. (<b>b</b>) Structural composition. (<b>c</b>,<b>d</b>) UAV aerial survey Digital Orthophoto Map (DOM) texture and Digital Surface Model (DSM) topographic results.</p>
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<p>Computational models of tailings dam stability analyses. (<b>a</b>) Two-dimensional (2D) model. (<b>b</b>) Pseudo-three-dimensional (P-3D) model that extends the 2D profiles fitting with water level conditions. (<b>c</b>) Real three-dimensional (R-3D) model that incorporates actual BLIs conditions identified from the proposed approach.</p>
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<p>Comparison of automated recognition results. (<b>a1</b>∼<b>a4</b>) Original images. (<b>b1</b>∼<b>b4</b>) Manually annotated results. (<b>c1</b>∼<b>c4</b>) Recognition results of optimized DeepLabV3+. (<b>d1</b>∼<b>d4</b>) Recognition results of optimized YOLACT.</p>
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<p>The extracted contours delineating the beach interface and the boundary edge of the decant pond using the optimized YOLACT model.</p>
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<p>Beach slope observation results. (<b>a</b>) Equidistant beach slope calculation sections. (<b>b</b>–<b>g</b>): Beach slope values extracted from DSM from section I to section VI plotted against the distance from the embankment crest.</p>
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<p>Results of dam stability evaluation. (<b>a</b>) 2D model, dam stability coefficient 2.096 (simplified Bishop method). (<b>b</b>) 2D model, dam stability coefficient 3.10 (SRM method). (<b>c</b>) P-3D model that extended the 2D profiles fitting with the minimum beach width condition, dam stability coefficient 3.86 (SRM method). (<b>d</b>) R-3D model based on actual BLIs condition extracted from the proposed approach, dam stability coefficient 4.00 (SRM method).</p>
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<p>Results of the tailings pond seepage analyses. (<b>a</b>) Model A, with water levels set at the minimum beach width condition. (<b>b</b>) Model B, with water levels set at the actual beach boundary line condition and extracted using the optimized YOLACT. (<b>c</b>) Comparison of the simulated seepage phreatic lines for Models A and B under various rainfall scenarios.</p>
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20 pages, 2732 KiB  
Article
Sources Analysis and Health Risk Assessment of Heavy Metals in Street Dust from Urban Core of Zhengzhou, China
by Minghao Ren, Yali Deng, Wenshan Ni, Jingjing Su, Yao Tong, Xiao Han, Fange Li, Hongjian Wang, Fei Zhao, Xiaoxiao Huang and Zhiquan Huang
Sustainability 2024, 16(17), 7604; https://doi.org/10.3390/su16177604 - 2 Sep 2024
Viewed by 972
Abstract
Fifty-one street dust samples were systematically collected from the urban core of Zhengzhou, China, and analyzed for potentially toxic metals. The concentrations of vanadium (V), manganese (Mn), copper (Cu), zinc (Zn), arsenic (As), lead (Pb), and nickel (Ni) in the samples surpassed the [...] Read more.
Fifty-one street dust samples were systematically collected from the urban core of Zhengzhou, China, and analyzed for potentially toxic metals. The concentrations of vanadium (V), manganese (Mn), copper (Cu), zinc (Zn), arsenic (As), lead (Pb), and nickel (Ni) in the samples surpassed the background values of the local soil, indicating a notable potential for contamination. Spatially, the traffic area was the most polluted with a total heavy metal concentration of Cu, Zn, As, Pb, and Ni, while the pollution levels were lower in the culture and education area and commercial area with total concentrations of V and Mn. Seasonal variations were discerned in the concentrations of heavy metals, with V, Cu, Zn, and As exhibiting heightened levels during the fall and winter, while Mn, Ni, and Pb reached peaks in the spring season. Zn exhibited the highest mean geo-accumulation index (Igeo) value at 2.247, followed by Cu at 2.019, Pb at 0.961, As at 0.590, Ni at 0.126, Mn at −0.178, and V at −0.359. The potential ecological risk index (RI) in the traffic-intensive area markedly exceeded other functional areas. Health risk assessments showed that children were more vulnerable to heavy metal exposure than adults, particularly through the ingestion pathway. Correlation analysis, principal component analysis (PCA), and cluster analysis (CA) were applied in conjunction with the spatial–temporal concentration patterns across various functional areas to ascertain the plausible sources of heavy metal pollutants. The results indicated that heavy metals in the urban street dust of Zhengzhou were multifaceted, stemming from natural processes and diverse anthropogenic activities such as coal burning, industrial emissions, traffic, and construction operations. Full article
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<p>Sampling sites in the study area. (<b>a</b>) Location of Zhengzhou city in Henan. (<b>b</b>) Central urban region of Zhengzhou city and its county-level cities. (<b>c</b>) Study area and sampling sites in Zhengzhou, China. Note: S1, culture and education area; S2, traffic area; S3, commercial area.</p>
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<p>The distribution of mean concentrations for heavy metals in the urban street dust of Zhengzhou (mg·kg<sup>−1</sup>, dry weight) from three functional areas. Note: S1, culture and education area; S2, traffic area; S3, commercial area.</p>
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<p>Seasonal variations in heavy metal concentrations. (<b>a</b>) The mean concentrations of heavy metals. (<b>b</b>) Seasonal variations in heavy metal concentrations from three functional areas. Note: (<b>a</b>) The ends of the half box represent the 25th and 75th percentile values, respectively. The horizontal line at the bottom and top denotes the minimum and maximum concentration. The data distribution is depicted by colorful dots. (<b>b</b>) Data are represented as the mean ± 95% CI (confidence interval). The solid circle represents the mean heavy metal concentrations.</p>
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<p>A box plot of the geo-accumulation index (<span class="html-italic">I<sub>geo</sub></span>) for selected heavy metals in urban road dust. Note: The dashed line denotes the threshold of contamination assessment and their classification values are listed in <a href="#app1-sustainability-16-07604" class="html-app">Table S1</a>. The black horizontal line in the middle of the box represents the median values, while the solid black square in the box represents the mean values. The ends of the box represent the 25th and 75th percentile values, respectively. The horizontal line in the bottom and top of the box plots denotes a multiplication of the interquartile range (IQR) by 1.5.</p>
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<p>A source analysis of heavy metal contamination in urban street dust. (<b>a</b>) A heatmap illustrating the Pearson correlation coefficients of heavy metals. (<b>b</b>) Diagrams of three-dimensional space: three principal components for seven heavy metals in Zhengzhou. (<b>c</b>) The results of the cluster analysis of heavy metals obtained by Ward’s hierarchical clustering method (the distances reflect the degree of correlation between different elements). Note: (<b>a</b>) one (*) and two (**) asterisks indicate that the correlation is significant at the 0.05 level (two-tailed) and the 0.01 level (two-tailed), respectively. The values of correlation coefficients have been labeled in the figure.</p>
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12 pages, 8555 KiB  
Article
An Experimental Study of the Retention Effect of Urban Drainage Systems in Response to Grate Inlet Clogging
by Seongil Yeom and Jungkyu Ahn
Sustainability 2024, 16(17), 7596; https://doi.org/10.3390/su16177596 - 2 Sep 2024
Viewed by 468
Abstract
The rainfall drainage characteristics of urban areas result in more surface runoff compared to soil surfaces. Conventional Urban Drainage Systems, CUDs, have disadvantages when managing this surface runoff, leading to urban water circulation issues such as flooding and depletion of groundwater. The performance [...] Read more.
The rainfall drainage characteristics of urban areas result in more surface runoff compared to soil surfaces. Conventional Urban Drainage Systems, CUDs, have disadvantages when managing this surface runoff, leading to urban water circulation issues such as flooding and depletion of groundwater. The performance of CUDs varies significantly depending on the clogging of grate inlets with various debris and shapes. To address these disadvantages, Sustainable Urban Drainage Systems, SUDs, have been proposed. This study compares the drainage efficiency of the two systems; using a physical model with an artificial rainfall simulator, an experimental study was conducted with respect to clogging type, clogging ratio, and rainfall intensity. Comparative analysis of peak flow rates and the peak time demonstrates the advantages of IRDs. As a result, IRDs are applicable to the mitigation of urban water circulation problems such as inundation. Full article
(This article belongs to the Section Sustainable Water Management)
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<p>Example of clogging on a grate inlet. (<b>a</b>) Complete clogging on road; (<b>b</b>) partial clogging on O-shaped channel.</p>
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<p>Schematic diagram of IRDs [<a href="#B19-sustainability-16-07596" class="html-bibr">19</a>].</p>
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<p>Diagram of physical model: (<b>a</b>) components; (<b>b</b>) specification of surface area.</p>
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<p>Detail of grate inlet: (<b>a</b>) OCs; (<b>b</b>) IRDs.</p>
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<p>Detail of surface area: (<b>a</b>) specification of pavement; (<b>b</b>) result of sieve test.</p>
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<p>Diagram and example of clogging types: (<b>a</b>) Expansion-type clogging; (<b>b</b>) Contraction-type clogging; (<b>c</b>) example of Contraction-type clogging; (<b>d</b>) example of Expansion-type clogging.</p>
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<p>Flowchart of experimental study.</p>
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<p>Measurement of runoff. (<b>a</b>) OCs; (<b>b</b>) IRDs.</p>
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<p>Measurement of surface water depth. (<b>a</b>) IN30; (<b>b</b>) IN60; (<b>c</b>) IN90.</p>
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<p>Accumulative discharge of physical model: (<b>a</b>) IRDs-E; (<b>b</b>) IRDs-C; (<b>c</b>) OCs-E; (<b>d</b>) OCs-C.</p>
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<p>Comparison of peak time, <span class="html-italic">T<sub>p</sub></span>.</p>
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<p>Dimensionless comparison of <span class="html-italic">T</span>*.</p>
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17 pages, 911 KiB  
Article
Turbulent Micropolar Open-Channel Flow
by George Sofiadis, Antonios Liakopoulos, Apostolos Palasis and Filippos Sofos
Fluids 2024, 9(9), 202; https://doi.org/10.3390/fluids9090202 - 31 Aug 2024
Viewed by 253
Abstract
The present paper focuses on the investigation of the turbulent characteristics of an open-channel flow by employing the micropolar model. The underlying model has already been proven to correctly describe the secondary phase of turbulent wall-bounded flows. The open-channel case comprises an ideal [...] Read more.
The present paper focuses on the investigation of the turbulent characteristics of an open-channel flow by employing the micropolar model. The underlying model has already been proven to correctly describe the secondary phase of turbulent wall-bounded flows. The open-channel case comprises an ideal candidate to further test the micropolar model as many environmental flows carry a secondary phase, the behavior of which is of great interest for applications such as sedimentation transport and debris flow. Direct Numerical Simulations (DNSs) have been carried out on an open channel for Reb = 11,200 based on mean crossectional velocity, channel height, and the fluid kinematic viscosity. The simulated results are compared against previous experimental as well as Langrangian DNS data of similar flows, with excellent agreement. The micropolar model is capable of describing the same problem but in an Eulerian frame, thus significantly simplifying the computational cost and complexity. Full article
(This article belongs to the Special Issue Modelling Flows in Pipes and Channels)
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<p>Schematic of the geometry that has been used in the present study, where <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>2</mn> <mi>δ</mi> </mrow> </semantics></math>.</p>
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<p>Normalized mean velocity profile of the present hydrodynamic case compared against experimental results of Komori et al. [<a href="#B18-fluids-09-00202" class="html-bibr">18</a>] and DNS results of Lam and Banerjee [<a href="#B19-fluids-09-00202" class="html-bibr">19</a>] and of Bauer et al. [<a href="#B20-fluids-09-00202" class="html-bibr">20</a>].</p>
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<p>Normalized mean velocity profile of the present hydrodynamic case, compared against micropolar cases at the same <math display="inline"><semantics> <mrow> <mi>R</mi> <msub> <mi>e</mi> <mi>b</mi> </msub> </mrow> </semantics></math>.</p>
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<p>Mean velocity profile of the present hydrodynamic case, compared against the micropolar case of <math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>0.9</mn> </mrow> </semantics></math> at the same <math display="inline"><semantics> <mrow> <mi>R</mi> <msub> <mi>e</mi> <mi>b</mi> </msub> </mrow> </semantics></math>, in outer units.</p>
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<p>Mean spanwise micropolar velocity profile of the present open-channel case, compared against wall-bounded channel case at the same <math display="inline"><semantics> <mrow> <mi>R</mi> <msub> <mi>e</mi> <mi>b</mi> </msub> </mrow> </semantics></math> = 11,200 and <math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>0.4</mn> </mrow> </semantics></math>. The open-channel case is normalized with the total channel height (h), while the closed channel case with (<math display="inline"><semantics> <mrow> <mi>δ</mi> <mo>=</mo> <mi>h</mi> <mo>/</mo> <mn>2</mn> </mrow> </semantics></math>).</p>
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<p>Diagnostic function (<math display="inline"><semantics> <mo>Ξ</mo> </semantics></math>) plots of the present DNS data, for <math display="inline"><semantics> <mrow> <mi>R</mi> <msub> <mi>e</mi> <mi>b</mi> </msub> </mrow> </semantics></math> = 11,200 and cases of <math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>0.0</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>0.9</mn> </mrow> </semantics></math>, plotted in inner-law units.</p>
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<p>Power-law diagnostic function (<math display="inline"><semantics> <mo>Γ</mo> </semantics></math>) based on the present DNS data, for <math display="inline"><semantics> <mrow> <mi>R</mi> <msub> <mi>e</mi> <mi>b</mi> </msub> </mrow> </semantics></math> = 11,200 and cases of <math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>0.0</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>0.9</mn> </mrow> </semantics></math>, plotted in inner-law units.</p>
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<p>Normalized (<b>a</b>) <math display="inline"><semantics> <msub> <mi>u</mi> <mrow> <mi>r</mi> <mi>m</mi> <mi>s</mi> </mrow> </msub> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <msub> <mi>v</mi> <mrow> <mi>r</mi> <mi>m</mi> <mi>s</mi> </mrow> </msub> </semantics></math>, and (<b>c</b>) <math display="inline"><semantics> <msub> <mi>w</mi> <mrow> <mi>r</mi> <mi>m</mi> <mi>s</mi> </mrow> </msub> </semantics></math> velocity profiles of the hydrodynamic case (<math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>) plotted against global coordinates and compared with experimental results of Komori et al. [<a href="#B18-fluids-09-00202" class="html-bibr">18</a>] and DNS results of Lam and Banerjee [<a href="#B19-fluids-09-00202" class="html-bibr">19</a>].</p>
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<p>Normalized (<b>a</b>) <math display="inline"><semantics> <msub> <mi>u</mi> <mrow> <mi>r</mi> <mi>m</mi> <mi>s</mi> </mrow> </msub> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <msub> <mi>v</mi> <mrow> <mi>r</mi> <mi>m</mi> <mi>s</mi> </mrow> </msub> </semantics></math>, and (<b>c</b>) <math display="inline"><semantics> <msub> <mi>w</mi> <mrow> <mi>r</mi> <mi>m</mi> <mi>s</mi> </mrow> </msub> </semantics></math> velocity profiles of the hydrodynamic case (<math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>) compared with the micropolar case (<math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>0.9</mn> </mrow> </semantics></math>), plotted against global coordinates. The final difference values (shaded area) have been normalized with the shear velocity so that, if the difference has a value of, for example, <math display="inline"><semantics> <mrow> <mn>0.5</mn> </mrow> </semantics></math>, it will be equal to <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mi>τ</mi> </msub> <mo>/</mo> <mn>2</mn> </mrow> </semantics></math>.</p>
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<p>Normalized shear stress profiles of the hydrodynamic case (<math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>) <math display="inline"><semantics> <mrow> <mi>R</mi> <msub> <mi>e</mi> <mi>b</mi> </msub> </mrow> </semantics></math> = 11,200, compared with experimental results of Komori et al. [<a href="#B18-fluids-09-00202" class="html-bibr">18</a>] and DNS results of Lam and Banerjee [<a href="#B19-fluids-09-00202" class="html-bibr">19</a>], plotted against normalized wall distance.</p>
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<p>Normalized shear stress profiles of the hydrodynamic case (<math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>), compared with the micropolar case of (<math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>0.9</mn> </mrow> </semantics></math>), for <math display="inline"><semantics> <mrow> <mi>R</mi> <msub> <mi>e</mi> <mi>b</mi> </msub> </mrow> </semantics></math> = 11,200, plotted against normalized wall distance. The shaded areas represent the difference between the two normalized stress profiles: (red shade) <math display="inline"><semantics> <mrow> <msub> <mover> <mrow> <mo>−</mo> <mo>(</mo> <msup> <mi>u</mi> <mo>′</mo> </msup> <msup> <mi>v</mi> <mo>′</mo> </msup> <mo>)</mo> </mrow> <mo>¯</mo> </mover> <mrow> <mi>m</mi> <mo>=</mo> <mn>0</mn> </mrow> </msub> <mo>&lt;</mo> <msub> <mover> <mrow> <mo>−</mo> <mo>(</mo> <msup> <mi>u</mi> <mo>′</mo> </msup> <msup> <mi>v</mi> <mo>′</mo> </msup> <mo>)</mo> </mrow> <mo>¯</mo> </mover> <mrow> <mi>m</mi> <mo>=</mo> <mn>0.9</mn> </mrow> </msub> </mrow> </semantics></math>, (blue shade) <math display="inline"><semantics> <mrow> <msub> <mover> <mrow> <mo>−</mo> <mo>(</mo> <msup> <mi>u</mi> <mo>′</mo> </msup> <msup> <mi>v</mi> <mo>′</mo> </msup> <mo>)</mo> </mrow> <mo>¯</mo> </mover> <mrow> <mi>m</mi> <mo>=</mo> <mn>0</mn> </mrow> </msub> <mo>&gt;</mo> <msub> <mover> <mrow> <mo>−</mo> <mo>(</mo> <msup> <mi>u</mi> <mo>′</mo> </msup> <msup> <mi>v</mi> <mo>′</mo> </msup> <mo>)</mo> </mrow> <mo>¯</mo> </mover> <mrow> <mi>m</mi> <mo>=</mo> <mn>0.9</mn> </mrow> </msub> </mrow> </semantics></math>.</p>
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28 pages, 37291 KiB  
Article
Probabilistic Cascade Modeling for Enhanced Flood and Landslide Hazard Assessment: Integrating Multi-Model Approaches in the La Liboriana River Basin
by Johnny Vega, Laura Ortiz-Giraldo, Blanca A. Botero, César Hidalgo and Juan Camilo Parra
Water 2024, 16(17), 2404; https://doi.org/10.3390/w16172404 - 27 Aug 2024
Viewed by 534
Abstract
Extreme rainfall events in Andean basins frequently trigger landslides, obstructing river channels and causing flash flows, loss of lives, and economic damage. This study focused on improving the modeling of these events to enhance risk management, specifically in the La Liboriana basin in [...] Read more.
Extreme rainfall events in Andean basins frequently trigger landslides, obstructing river channels and causing flash flows, loss of lives, and economic damage. This study focused on improving the modeling of these events to enhance risk management, specifically in the La Liboriana basin in Salgar (Colombia). A cascading modeling methodology was developed, integrating the spatially distributed rainfall intensities, hazard zoning with the SLIDE model, propagation modeling with RAMMS using calibrated soil rheological parameters, the distributed hydrological model TETIS, and flood mapping with IBER. Return periods of 2.33, 5, 10, 25, 50, and 100 years were defined and applied throughout the methodology. A specific extreme event (18 May 2015) was modeled for calibration and comparison. The spatial rainfall intensities indicated maximum concentrations in the northwestern upper basin and southeastern lower basin. Six landslide hazard maps were generated, predicting landslide-prone areas with a slightly above random prediction rate for the 2015 event. The RAMMS debris flow modeling involved 30 simulations, indicating significant deposition within the river channel and modifying the terrain. Hydraulic modeling with the IBER model revealed water heights ranging from 0.23 to 7 m and velocities from 0.34 m/s to 6.98 m/s, with urban areas showing higher values, indicating increased erosion and infrastructure damage potential. Full article
(This article belongs to the Section Hydrogeology)
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<p>Location map of the study area: (<b>a</b>) continental scale; (<b>b</b>) country scale; and (<b>c</b>) basin scale.</p>
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<p>Characterization of the study area: (<b>a</b>) elevation; (<b>b</b>) slope; (<b>c</b>) landforms; (<b>d</b>) soil depth (thickness); (<b>e</b>) geology; and (<b>f</b>) landcover.</p>
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<p>Schematic workflow for the multi-hazard assessment.</p>
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<p>Rainfall intensities (m/h) for different return periods.</p>
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<p>Landslide hazard assessment (in terms of the FoS values) considering different return periods.</p>
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<p>Maximum deposition height (m) according for the considered return period.</p>
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<p>Analysis of the deposition heights (m) in the river section k18+910 according to the considered return periods.</p>
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<p>Location of the simulation points (flow hydrographs) for the hydraulic model and rainfall depths (mm) for the considered return periods.</p>
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<p>Torrential flood event hydrograph simulated with the TETIS model.</p>
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<p>Landcover of the flood zone along the La Liboriana river, modified based on the analysis of aerial photographs.</p>
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<p>Flow hydrographs at the reach outlet. Results comparison of the IBER and TETIS models.</p>
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<p>Hydraulic modeling results for the torrential flood event of 18 May 2015. (<b>a</b>,<b>b</b>) Maximum water height (m); (<b>c</b>) sites with a water level reported in Velasquez et al. [<a href="#B38-water-16-02404" class="html-bibr">38</a>] used for calibrating the hydraulic model; and (<b>d</b>,<b>e</b>) maximum velocity (m/s).</p>
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<p>Hydraulic modeling results for a 100-year return period torrential flood for La Margarita rural zone and the urban area of Salgar: (<b>top</b>) maximum water height (m); and (<b>bottom</b>) maximum velocity (m/s).</p>
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11 pages, 4354 KiB  
Brief Report
Factors Affecting Cell Viability during the Enzymatic Dissociation of Human Endocrine Tumor Tissues
by Anastasia Shcherbakova, Marina Utkina, Anna Valyaeva, Nano Pachuashvili, Ekaterina Bondarenko, Liliya Urusova, Sergey Popov and Natalya Mokrysheva
Biology 2024, 13(9), 665; https://doi.org/10.3390/biology13090665 - 27 Aug 2024
Viewed by 416
Abstract
The enzymatic dissociation of human solid tissues is a critical process for disaggregating extracellular matrix and the isolation of individual cells for various applications, including the immortalizing primary cells, creating novel cell lines, and performing flow cytometry and its specialized type, FACS, as [...] Read more.
The enzymatic dissociation of human solid tissues is a critical process for disaggregating extracellular matrix and the isolation of individual cells for various applications, including the immortalizing primary cells, creating novel cell lines, and performing flow cytometry and its specialized type, FACS, as well as conducting scRNA-seq studies. Tissue dissociation procedures should yield intact, highly viable single cells that preserve morphology and cell surface markers. However, endocrine tissues, such as adrenal gland tumors, thyroid carcinomas, and pituitary neuroendocrine tumors, present unique challenges due to their complex tissue organization and morphological features. Our study conducted a morphological examination of these tissues, highlighting the intricate structures and secondary degenerative changes that complicate the dissociation process. We investigated the effects of various dissociation parameters, including the types of enzymes, incubation duration, and post-dissociation purification procedures, such as debris removal and nontarget blood cell lysis, on the viability of cells derived from different tumor types. The findings emphasize the importance of optimizing tissue digestion protocols to preserve cell viability and integrity, ensuring reliable outcomes for downstream analyses. Full article
(This article belongs to the Section Cell Biology)
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<p>Images of endocrine tumor samples. (<b>A</b>) Gross image of the adrenal medullary tumor. 1. A microscopic image of the adrenal medullary tumor with hyalinized stroma; H&amp;E; ×180. 2. A microscopic image of the adrenal medullary tumor with hemorrhage (blue dashed line); H&amp;E; ×150. 3. A microscopic image of the adrenal medullary tumor hyalinized stroma (red dashed line) and necrosis (blue dashed line). In the areas of tumor necrosis, there is a large number of segmented neutrophils; H&amp;E; ×150. (<b>B</b>) Gross image of the adrenocortical tumor. 1. A microscopic image of the adrenocortical tumor with hyalinized stroma (blue dashed line); H&amp;E; ×150. 2. A microscopic image of the adrenocortical tumor with a large area of tumor necrosis. There is pronounced inflammatory infiltration around the necrotic area; H&amp;E; ×150. 3. A microscopic image of the adrenocortical tumor with high fibrosis; H&amp;E; ×180. (<b>C</b>) Gross image of the thyroid carcinoma. 1. A microscopic image of the nodular thyroid hyperplasia. Proliferation of thyrocytes with the formation of papillary structures and accumulation of colloid in follicles; H&amp;E; ×80. 2. A microscopic image of the thyroid tissue with hemorrhage; H&amp;E; ×150. 3. A microscopic image of the papillary thyroid carcinoma with hyalinized stroma (red dashed line) and calcification (blue dashed line); H&amp;E; ×150. 4. A microscopic image of the medullary thyroid carcinoma with the deposition of homogeneous eosinophilic masses (amyloid); H&amp;E; ×180. (<b>D</b>) Gross image of the PitNET. 1. A microscopic image of the PitNET with hemorrhage (blue dashed line) and bone fragments (red dashed line); H&amp;E; ×150.</p>
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<p>Representation of cell suspensions (n = 93) from four studied tumor tissue types, including adrenal medullary and adrenocortical tumors, thyroid carcinomas, and PitNETs (pituitary neuroendocrine tumors). The figure sequentially indicates the type of enzyme used, including Coll I (Collagenase I), Coll IV (Collagenase IV), MTDK (Multi Tissue Dissociation Kit), and NTDK (Neural Tissue Dissociation Kit), along with the dissociation time (min), cell viability (%), and post-dissociation procedures. The post-dissociation procedures include RBCS +/− (Red Blood Cell Lysis Solution) and DRS +/− (Dead Cell Removal Solution).</p>
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<p>Comparison of the effects of various enzyme types and dissociation time intervals on the viability of cell suspensions (n = 93) from four studied tumor tissue types. (<b>A</b>) Box plots compare the influence of enzyme types, including Coll I (Collagenase I), Coll IV (Collagenase IV), MTDK (Multi Tissue Dissociation Kit), and NTDK (Neural Tissue Dissociation Kit), on cell viability across different tissue types: adrenal medullary tumor (n = 26), adrenocortical tumor (n = 25), PitNET (n = 33) samples, and all tumor samples combined (n = 84). (<b>B</b>) Box plots compare the influence of dissociation time intervals (min) on cell viability across tissue types: adrenal medullary tumor (n = 26), adrenocortical tumor (n = 25), PitNET (n = 33), and thyroid carcinoma (n = 9) samples. (<b>C</b>) The line graphs compare cell viability over dissociation time intervals (min) based on enzyme types for the adrenal medullary tumor (n = 26), adrenocortical tumor (n = 25), PitNET (n = 33), and thyroid carcinoma (n = 9) samples. Comparisons of cell viability between dissociation time intervals were conducted using Welch’s one-way ANOVA (oneway.test function in R), followed by pairwise Welch’s <span class="html-italic">t</span>-tests (t.test function in R). Pairwise Wilcoxon rank-sum tests (wilcox.test function in R) were used to evaluate differences in cell viability between samples grouped by enzyme type, RBCS, or DRS. The data in the line graphs are presented as mean ± SD. The statistical significance is indicated as follows: ****: <span class="html-italic">p</span> ≤ 0.0001; **: <span class="html-italic">p</span> ≤ 0.01; *: <span class="html-italic">p</span> ≤ 0.05; ns (not significant): <span class="html-italic">p</span> &gt; 0.05.</p>
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<p>Comparison of the effects of the post-dissociation procedures: DRS (Dead Cell Removal Solution) and RBCS (Red Blood Cell Lysis Solution) on the viability of cell suspensions from studied tumor tissue types. (<b>A</b>) Box plots compare the influence of DRS +/− on cell viability across adrenal medullary tumor (n = 26), adrenocortical tumor (n = 25), PitNET (n = 33) samples, and all tumor samples combined (n = 84). (<b>B</b>) Box plots compare the influence of RBCS +/− on cell viability across adrenal medullary tumor (n = 26), adrenocortical tumor (n = 25), PitNET (n = 33) samples, thyroid carcinoma (n = 9) samples, and all tumor samples combined (n = 93). Comparisons of cell viability between dissociation time intervals were conducted using Welch’s one-way ANOVA (oneway.test function in R), followed by pairwise Welch’s <span class="html-italic">t</span>-tests (t.test function in R). Pairwise Wilcoxon rank-sum tests (wilcox.test function in R) were used to evaluate the differences in cell viability between samples grouped by RBCS or DRS. Statistical significance is indicated as follows: *: <span class="html-italic">p</span> ≤ 0.05, ns (not significant): <span class="html-italic">p</span> &gt; 0.05.</p>
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28 pages, 90455 KiB  
Article
Lessons Learnt from the Simulations of Aero-Engine Ground Vortex
by Wenqiang Zhang, Tao Yang, Jun Shen and Qiangqiang Sun
Aerospace 2024, 11(9), 699; https://doi.org/10.3390/aerospace11090699 - 26 Aug 2024
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Abstract
With the startup of the aero-engine, the ground vortex is formed between the ground and the engine intake. The ground vortex leads to total pressure and swirl distortion, which reduces the performance of the engine. The inhalation of the dust and debris through [...] Read more.
With the startup of the aero-engine, the ground vortex is formed between the ground and the engine intake. The ground vortex leads to total pressure and swirl distortion, which reduces the performance of the engine. The inhalation of the dust and debris through a ground vortex can erode the fan blade, block the seals and degrade turbine cooling performance. As the diameter of the modern fan blade becomes larger, the clearance between the intake lip and the ground surface is smaller, which enhances the strength of the ground vortex. Though considerable numerical studies have been conducted with the predictions of the ground vortex, it is noted that the accurate simulation of the ground vortex is still a tough task. This paper presents authors’ simulation work of the ground vortex into an intake model with different crosswind speeds. This paper tackles the challenge with a parametric study to provide useful guidelines on how to obtain a good match with the experimental data. The influence of the mesh density, performance of different turbulence models and how the boundary layer thickness affects the prediction results are conducted and analysed. The detailed structure of the flow field with ground vortex is presented, which can shed light on the experimental observations. A number of suggestions are presented that can pave the road to the accurate flow field simulations with strong vorticities. Full article
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Figure 1

Figure 1
<p>Computational domain for the ground vortex simulation.</p>
Full article ">Figure 2
<p>Definition of (<b>a</b>) DC60 and (<b>b</b>) <math display="inline"><semantics> <mrow> <mo>Γ</mo> </mrow> </semantics></math>.</p>
Full article ">Figure 3
<p>Vortex visualisation with Q-criterion.</p>
Full article ">Figure 4
<p>Comparisons of the total pressure between the CFD (<b>left</b>) and experimental data (experimental plot courtesy of Cranfield University library) (<b>right</b>) at the AIP [<a href="#B10-aerospace-11-00699" class="html-bibr">10</a>], <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>−</mo> <mi>ω</mi> </mrow> </semantics></math> SST turbulence model. (<b>a</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>18.3</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>9.1</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>6.1</mn> </mrow> </semantics></math>; and (<b>d</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>5.2</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 4 Cont.
<p>Comparisons of the total pressure between the CFD (<b>left</b>) and experimental data (experimental plot courtesy of Cranfield University library) (<b>right</b>) at the AIP [<a href="#B10-aerospace-11-00699" class="html-bibr">10</a>], <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>−</mo> <mi>ω</mi> </mrow> </semantics></math> SST turbulence model. (<b>a</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>18.3</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>9.1</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>6.1</mn> </mrow> </semantics></math>; and (<b>d</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>5.2</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 5
<p>Comparisons of the average velocity vector and vorticity vector field at the PIV measurement surface for different velocity ratios [<a href="#B10-aerospace-11-00699" class="html-bibr">10</a>] from CFD (<b>left</b>) and experimental data (<b>right</b>), <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>−</mo> <mi>ω</mi> </mrow> </semantics></math> SST turbulence model. (<b>a</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>18.3</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>9.1</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>6.1</mn> </mrow> </semantics></math>; and (<b>d</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>5.2</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 5 Cont.
<p>Comparisons of the average velocity vector and vorticity vector field at the PIV measurement surface for different velocity ratios [<a href="#B10-aerospace-11-00699" class="html-bibr">10</a>] from CFD (<b>left</b>) and experimental data (<b>right</b>), <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>−</mo> <mi>ω</mi> </mrow> </semantics></math> SST turbulence model. (<b>a</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>18.3</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>9.1</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>6.1</mn> </mrow> </semantics></math>; and (<b>d</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>5.2</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 6
<p>The grid topology of (<b>a</b>) Mesh A and (<b>b</b>) Mesh B.</p>
Full article ">Figure 7
<p>Total pressure at the AIP and velocity vector field of the PIV measurement plane for different crosswind speeds, <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>−</mo> <mi>ω</mi> </mrow> </semantics></math> SST turbulence model. (<b>a</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>18.3</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>9.1</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>6.1</mn> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>5.2</mn> </mrow> </semantics></math>; and (<b>e</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>4.6</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 7 Cont.
<p>Total pressure at the AIP and velocity vector field of the PIV measurement plane for different crosswind speeds, <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>−</mo> <mi>ω</mi> </mrow> </semantics></math> SST turbulence model. (<b>a</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>18.3</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>9.1</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>6.1</mn> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>5.2</mn> </mrow> </semantics></math>; and (<b>e</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>4.6</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 8
<p>Q-criterion of the ground vortex for the case <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>4.6</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>−</mo> <mi>ω</mi> </mrow> </semantics></math> SST turbulence model.</p>
Full article ">Figure 9
<p>Total pressure at the AIP and velocity vector field of the PIV measurement plane for different crosswind speeds, SA turbulence model. (<b>a</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>18.3</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>9.1</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>6.1</mn> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>5.2</mn> </mrow> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>4.6</mn> </mrow> </semantics></math>; and (<b>f</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>4.0</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 9 Cont.
<p>Total pressure at the AIP and velocity vector field of the PIV measurement plane for different crosswind speeds, SA turbulence model. (<b>a</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>18.3</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>9.1</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>6.1</mn> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>5.2</mn> </mrow> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>4.6</mn> </mrow> </semantics></math>; and (<b>f</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>4.0</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 9 Cont.
<p>Total pressure at the AIP and velocity vector field of the PIV measurement plane for different crosswind speeds, SA turbulence model. (<b>a</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>18.3</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>9.1</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>6.1</mn> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>5.2</mn> </mrow> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>4.6</mn> </mrow> </semantics></math>; and (<b>f</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>4.0</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 10
<p>Total pressure at the AIP and velocity vector field of the PIV measurement plane for different crosswind speeds, <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>−</mo> <mi>ε</mi> </mrow> </semantics></math> turbulence model. (<b>a</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>18.3</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>9.1</mn> </mrow> </semantics></math>; and (<b>c</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>6.1</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 10 Cont.
<p>Total pressure at the AIP and velocity vector field of the PIV measurement plane for different crosswind speeds, <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>−</mo> <mi>ε</mi> </mrow> </semantics></math> turbulence model. (<b>a</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>18.3</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>9.1</mn> </mrow> </semantics></math>; and (<b>c</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>6.1</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 11
<p>Time-averaged total pressure at the AIP and velocity vector field of the PIV measurement plane for different crosswind speeds, DDES computation. (<b>a</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>18.3</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>9.1</mn> </mrow> </semantics></math>; and (<b>c</b>) <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> <mo>/</mo> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </mrow> </mrow> <mo>=</mo> <mn>6.1</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 12
<p>Simulated and experimental total pressure at a fixed radial position crossing the vortex core.</p>
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<p>Simulated and measured circulation at different crosswind speeds.</p>
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<p>Simulated and measured distortion indexes at different crosswind speeds.</p>
Full article ">Figure 15
<p>Vortex configuration presented by different turbulence models: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>−</mo> <mi>ω</mi> </mrow> </semantics></math> SST; (<b>b</b>) SA; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>−</mo> <mi>ε</mi> </mrow> </semantics></math>; and (<b>d</b>) DDES.</p>
Full article ">Figure 16
<p>Slices of the vortex to perform vorticity integration.</p>
Full article ">Figure 17
<p>Velocity profile of the approaching boundary layer.</p>
Full article ">Figure 18
<p>Thickness of the approaching boundary layer, DDES: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>δ</mi> <mo>/</mo> <msub> <mrow> <mi>D</mi> </mrow> <mrow> <mi>l</mi> </mrow> </msub> <mo>=</mo> <mn>0.12</mn> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>δ</mi> <mo>/</mo> <msub> <mrow> <mi>D</mi> </mrow> <mrow> <mi>l</mi> </mrow> </msub> <mo>=</mo> <mn>0.45</mn> </mrow> </semantics></math>, and (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>δ</mi> <mo>/</mo> <msub> <mrow> <mi>D</mi> </mrow> <mrow> <mi>l</mi> </mrow> </msub> <mo>=</mo> <mn>1.03</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 19
<p>Flow structure of the vortex system with crosswind: (<b>a</b>) helical structure of the flow around the ground vortex and surface streamline of the potential vortex beneath the intake; (<b>b</b>) stream traces of the ground vortex at the PIV measurement plane; and (<b>c</b>) near-ground shear vortices.</p>
Full article ">Figure 20
<p>Normalised vertical velocity measured at the PIV plane: (<b>a</b>) CFD and (<b>b</b>) measurement.</p>
Full article ">Figure 21
<p>Axial slice through the ground vortex: (<b>a</b>) vertical velocity; (<b>b</b>) zoomed view of the vortex near ground surface; (<b>c</b>) Y velocity; and (<b>d</b>) static temperature.</p>
Full article ">Figure 21 Cont.
<p>Axial slice through the ground vortex: (<b>a</b>) vertical velocity; (<b>b</b>) zoomed view of the vortex near ground surface; (<b>c</b>) Y velocity; and (<b>d</b>) static temperature.</p>
Full article ">
33 pages, 11936 KiB  
Article
Evaluating the Performance of Protective Barriers against Debris Flows Using Coupled Eulerian Lagrangian and Finite Element Analyses
by Shiyin Sha, Ashley P. Dyson, Gholamreza Kefayati and Ali Tolooiyan
Sustainability 2024, 16(17), 7332; https://doi.org/10.3390/su16177332 - 26 Aug 2024
Viewed by 511
Abstract
Protective structures are critical in mitigating the dangers posed by debris flows. However, evaluating their performance remains a challenge, especially considering boulder transport in complex 3D terrains. This study introduces a comprehensive methodology to appraise the effectiveness of protective structures under the impact [...] Read more.
Protective structures are critical in mitigating the dangers posed by debris flows. However, evaluating their performance remains a challenge, especially considering boulder transport in complex 3D terrains. This study introduces a comprehensive methodology to appraise the effectiveness of protective structures under the impact of debris flows for real-world conditions along the Hobart Rivulet in Tasmania, Australia. The validation of the Coupled Eulerian-Lagrangian (CEL) model against experimental data demonstrates its high accuracy in predicting flow dynamics and impact forces, whereby flow velocities are estimated for subsequent Finite Element (FE) analyses. By simulating boulder-barrier interactions, weak points in I-beam post barriers are identified, with a broad investigation of the effects on the barrier performance under various conditions. The establishment of a 3D CEL model to assess the interactions between debris flow, boulders, and I-beam post barriers in a complex rivulet terrain is of particular significance. Through CEL and FE analyses, various aspects of debris flow-structure interactions are presented, including structural failure, impact force, and boulder velocity. The findings provide insights into the suitability of various numerical methods to assess the performance of protective measures in real-world scenarios. Full article
(This article belongs to the Special Issue Innovative Technologies and Strategies in Disaster Management)
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Figure 1

Figure 1
<p>The overview of the proposed procedure for the performance assessment.</p>
Full article ">Figure 2
<p>(<b>a</b>) Model configuration for simulating the boulder-enriched debris flow tests [<a href="#B22-sustainability-16-07332" class="html-bibr">22</a>,<a href="#B27-sustainability-16-07332" class="html-bibr">27</a>]; (<b>b</b>) Comparison of experimental and CEL simulation results in the time history of impact force for Test D; (<b>c</b>) Comparison of experimental and CEL simulation results in the velocities of debris front and first arrival boulder.</p>
Full article ">Figure 3
<p>Topographic map highlighting the study area (red area) along the Hobart Rivulet (blue lines) (<b>Left</b>); Isometric view of the study area (<b>Right</b>).</p>
Full article ">Figure 4
<p>The CEL model of Hobart rivulet for flow velocity estimation. (<b>a</b>) Eulerian domain and initial debris flow; (<b>b</b>) the model configuration and flow velocity test locations (red points).</p>
Full article ">Figure 5
<p>The time history of debris flow velocity at the specific test locations along the flow path.</p>
Full article ">Figure 6
<p>(<b>a</b>) The selected existing protective barrier along the Hobart Rivulet (Left: front view); Two rows of vertical posts, with a cross flange installed on the top of the front row only (Right: side view); (<b>b</b>) The model of the selected I-beam post barrier for the performance analysis.</p>
Full article ">Figure 7
<p>The model of boulder–structure interaction for evaluating the weakest post within the barrier (used Test FN1 as an example): the geometry of the model (<b>top</b>) and side view of the model for one I-beam post (<b>bottom</b>).</p>
Full article ">Figure 8
<p>Comparison of the failure ratio and the maximum impact force between I-beam posts.</p>
Full article ">Figure 9
<p>The model of boulder–structure interaction for investigating the effect of boulder velocity: the geometry of the model (<b>top</b>) and side view of the model for FN9 I-beam post (<b>bottom</b>).</p>
Full article ">Figure 10
<p>Comparison of the failure ratio and the maximum impact force at different boulder velocities.</p>
Full article ">Figure 11
<p>The model of boulder–structure interaction for investigating the effect of boulder impact height (using the case of the height of 0.5 m as an example).</p>
Full article ">Figure 12
<p>Comparison of the failure ratio and the maximum impact force at various boulder impact heights.</p>
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<p>The model of boulder–structure interaction for investigating the effect of boulder size (using the case S3 as an example).</p>
Full article ">Figure 14
<p>Comparison of the failure ratio and the maximum impact force between I-beam posts at different boulder sizes.</p>
Full article ">Figure 15
<p>The model of boulder–structure interaction for investigating the effect of boulder impact orientation: (<b>a</b>) 45-degree inclination; (<b>b</b>) circular surface at 90 degrees to the Y-axis; (<b>c</b>) circular surface at 90 degrees to the X-axis; (<b>d</b>) circular surface at 90 degrees to the Z-axis.</p>
Full article ">Figure 16
<p>Comparison of the failure ratio and the maximum impact force between I-beam posts at different boulder impact orientations.</p>
Full article ">Figure 17
<p>The CEL model of flow–boulder–barrier interaction on the Hobart rivulet terrain (Using Case C2 as an example).</p>
Full article ">Figure 18
<p>Comparison of the failure ratio between cases at different boulder sizes at 50 s.</p>
Full article ">Figure 19
<p>Visualisation of failed elements of the barrier after boulder impacts at 50 s (<b>Left</b>) and comparison of the barrier performance at 0 s and 50 s (<b>Right</b>): dark blue–non-failed elements, grey–failed elements, at t = 50 s; light blue–non failed elements, at t = 0 s.</p>
Full article ">Figure 20
<p>Visualisation of the collision between the first boulder and I-beam post barrier for Case C1, capturing the moments before, during, and after the initial impact for 0.2 s: isometric view (<b>Left</b>); side view (<b>Right</b>).</p>
Full article ">Figure 21
<p>Visualisation of the collision between the first boulder and I-beam post barrier for Case C2, capturing the moments before, during, and after the initial impact for 0.2 s: isometric view (<b>Left</b>); side view (<b>Right</b>).</p>
Full article ">Figure 22
<p>Visualisation of the collision between the first boulder and I-beam post barrier for Case C3, capturing the moments before, during, and after the initial impact for 0.2 s: isometric view (<b>Left</b>); side view (<b>Right</b>).</p>
Full article ">Figure 23
<p>Comparative analysis for three cases (C1, C2, and C3) before, during, and after the boulder’s collision with the barrier: (<b>a</b>) failure ratio; (<b>b</b>) impact force; and (<b>c</b>) boulder velocity.</p>
Full article ">Figure 24
<p>The configurations of the FE boulder–barrier interaction models, based on CEL model observations: (<b>a</b>) a boulder with a diameter of 0.5 m placed at the height of 0.6 m with 45-degree inclination moves in the x direction at an initial speed of 10.5 m/s; (<b>b</b>) a boulder with a diameter of 1.5 m placed at the height of 0.75 m with circular surface at 90 degrees to the Z-axis moves in the x direction at an initial speed of 12.9 m/s; (<b>c</b>) a boulder with a diameter of 2.5 m placed at the height of 1.32 m with a circular surface at 90 degrees to the Z-axis moves in the x direction at an initial speed of 13 m/s.</p>
Full article ">Figure 25
<p>Comparative analysis for three scenarios: before (D1), during (D2), and after (D3) the boulder’s collision with the barrier: (<b>a</b>) failure ratio; (<b>b</b>) impact force; and (<b>c</b>) boulder velocity.</p>
Full article ">Figure 26
<p>Comparison of percent difference between the CEL and FE models for failure ratio, impact force, and boulder velocity across three scenarios (D1, D2, D3).</p>
Full article ">Figure 27
<p>Visualisation and comparison of failed elements of the barrier at 0.2 s after the first boulder impacts between two approaches with non-failed elements in blue and failed elements in grey: (<b>a</b>) CEL model; (<b>b</b>) FE model.</p>
Full article ">
22 pages, 38794 KiB  
Article
Inception of Constructional Submarine Conduit by Asymmetry Generated by Turbidity Current
by Daniel Bayer da Silva, Eduardo Puhl, Rafael Manica, Ana Luiza de Oliveira Borges and Adriano Roessler Viana
J. Mar. Sci. Eng. 2024, 12(9), 1476; https://doi.org/10.3390/jmse12091476 - 24 Aug 2024
Viewed by 478
Abstract
Submarine conduits are features responsible for transporting clastic debris from continents to the deep ocean. While the architecture of conduits has been extensively studied, the process of their inception remains unclear. This study highlights the possibility that some conduits are initiated by depositional [...] Read more.
Submarine conduits are features responsible for transporting clastic debris from continents to the deep ocean. While the architecture of conduits has been extensively studied, the process of their inception remains unclear. This study highlights the possibility that some conduits are initiated by depositional processes involving turbidity currents. Here, we present the results of eight experiments where gravity currents were allowed to develop their own pathways. The simulation tank represented natural scales of continental shelves, slopes, and basins. The initial experiments involved sediment-laden flows with low density (1–10% in volume). In first experiment runs (Series I), sediment deposition occurred primarily on the shelf and slope, resulting in an asymmetric transverse profile. This asymmetry facilitated subsequent conservative currents (1034 to 1070 kg/m3 due to salt dissolution) flowing alongside during the second series, resulting in the formation of a constructive submarine conduit. This feature is analogous to gully formations observed in various locations. This study correlates these findings with gully-like features and proposes a model where non-confined density flows can evolve into confined flows through the construction of asymmetric topography. An evolutionary model is proposed to explain the mechanism, which potentially elucidates the formation of many submarine conduits. Full article
(This article belongs to the Section Geological Oceanography)
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Figure 1

Figure 1
<p>(<b>a</b>) A 3D view of the experimental setup, scaled according to the dimensions and names of the environmental analogs; (<b>b</b>) plan view; (<b>c</b>) cross-sectional view.</p>
Full article ">Figure 2
<p>Grain size distribution of the sediments (coal and kaolin) used in the experiments.</p>
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<p>Time evolution chart of main parameters for the 5 runs of Series I. The dashed blue line indicates the variation in density (see <a href="#jmse-12-01476-f004" class="html-fig">Figure 4</a> and <a href="#jmse-12-01476-t002" class="html-table">Table 2</a>), and the red line shows the variation in flow rate. Each run is marked at the top of the graphic.</p>
Full article ">Figure 4
<p>Video-based analysis of the deposit evolution in Series I composed of a sequence of runs (<b>E1</b>–<b>E5</b>). Subfigure <b>E1</b>–<b>E5</b>: each one corresponds to a run and includes a photo of the corresponding final deposit, representation of some of the main flow deposits, data from the run, and a schematic profile (vertical exaggeration of 10 times) of the final deposit marked on the photo (a–b profile). These profiles were overlaid in each run to illustrate the continuity of the experiment and the final deposit situation.</p>
Full article ">Figure 5
<p>Schematic profiles from the 3D model of the final deposits of each run (E3, E4, and E5). (<b>a</b>) Front view with a transverse cut; (<b>b</b>) Top view; (<b>c</b>) Transverse slice from (<b>a</b>). The vertical exaggeration is 10 times. The figures were created from Paradigm’s GOCAD<sup>®</sup>.</p>
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<p>Time evolution chart of the main parameters of the 3 runs of Series II. The dashed blue line indicates the density variation, and the red line indicates the flow rate variation.</p>
Full article ">Figure 7
<p>(<b>a</b>) A perspective view of ADV probe at the measuring position (<b>b</b>) and its top view. Note the red circle around the ADV probe (<b>a</b>,<b>b</b>). (<b>c</b>) An image of the final deposit showing the locations where measurements were taken. The numbers and values correspond to the regions in <a href="#jmse-12-01476-t003" class="html-table">Table 3</a>.</p>
Full article ">Figure 8
<p>A photo of the final deposit formed at the end of run 8 in the tank. Dotted lines indicate the conduit generated.</p>
Full article ">Figure 9
<p>Scheme representing the vertical evolution of the deposit. Subfigures <b>E1</b>–<b>E4</b>: the deposits after each run, including a transverse profile of the deposit at the end of each run. Subfigure <b>E4</b> final deposit: shows sediment waves (indicated by orange traced zones). Also, in yellow, the ‘future position’ of the conduit is shown, related to the asymmetry induced by erosive flow (indicated by black traced zones).</p>
Full article ">Figure 10
<p>E5 evolution with the main instant of conduit creation (red curved line). From 7 min: the asymmetry that forms the conduit is remarkable.</p>
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<p>Some moments of flow from E6. (<b>a</b>) The initial moment where the dyed saline flow began to utilize the conduit to confine part of its flow; (<b>b</b>,<b>c</b>) the middle and final moments, respectively, showing the flow concentrated within the conduit; (<b>d</b>) flow lines traced at different moments of the flow.</p>
Full article ">Figure 12
<p>(<b>a</b>) A scheme adapted from [<a href="#B97-jmse-12-01476" class="html-bibr">97</a>], illustrating an incipient condition where the rugosity on top of debrite helps localize flows. (<b>b</b>) An image adapted from [<a href="#B50-jmse-12-01476" class="html-bibr">50</a>] showing a straight gully shape similar to this experiment and the growth of a gully without apparent erosion.</p>
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<p>Two examples of constructional conduits: (<b>a</b>) a photograph and (<b>b</b>) interpretative drawing from [<a href="#B111-jmse-12-01476" class="html-bibr">111</a>] showing topographic depressions between the tongues, visible after draining the tank at the end of the experiment. Ravines, gullies, and furrows formed after the deposition of tongue-shaped features are visible. (<b>c</b>) A bathymetric map of the South China Sea and 3D perspective view (<b>d</b>) showing three canyons in the region from [<a href="#B50-jmse-12-01476" class="html-bibr">50</a>] demonstrate that submarine canyons, which might be interpreted as erosional features when analyzing only the present-day seafloor, can in some cases be net-depositional features.</p>
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<p>An illustration depicting the evolutionary process of submarine conduit inception in a region characterized by a slight slope (shelf/upper slope). (<b>a</b>) turbulent flow entering the region; (<b>b</b>) aggradation at the central part; (<b>c</b>) flow interaction with floor and assymetrical deposition; (<b>d</b>) flow confinement at conduit. The yellow arrows indicate the preferential path of flow within the system, with its size reflecting the volume of displaced material in that region.</p>
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16 pages, 4734 KiB  
Technical Note
Experimental Analysis of Rock Boulder Impacts on Brick Walls to Support Numerical Modelling of Building Damage
by Olga Mavrouli, Xuanmei Fan, Zhou Li, Dongpo Wang and Qiang Xu
Geosciences 2024, 14(9), 226; https://doi.org/10.3390/geosciences14090226 - 23 Aug 2024
Viewed by 377
Abstract
To estimate the expected damage due to rockfalls and debris flows for buildings and people, it is essential to assess the response of brick walls to boulder impacts. There are scarce physical tests of the impact of boulders on brick walls, which are [...] Read more.
To estimate the expected damage due to rockfalls and debris flows for buildings and people, it is essential to assess the response of brick walls to boulder impacts. There are scarce physical tests of the impact of boulders on brick walls, which are typical of residential buildings. A simple and low-cost experimental setup for investigating the damage of unreinforced brick walls that are subjected to a boulder’s impact is presented. The setup consists of a ramp that is adjusted with a light steel structure. Seven pilot tests for five single-leaf brick walls of a 1000 × 1000 mm2 area, hit by a 72.925 kg granite boulder, and from five release heights (0.25 m, 0.50, 1.00 m, 2.50 m, and 3.00 m) are performed. The observed damage indicates that wall breakthrough occurs for translational kinetic energies as low as 500 J. The prevalent failure mechanism is local shear damage. Additionally, a numerical model to simulate the physical tests was developed using the FEM. Using the same properties as in the physical testing, the numerical model is found to realistically reproduce the displacement of a node at the centre of the impact, as well as the translational impact velocity and energy, for the same five boulder release heights. Full article
(This article belongs to the Section Natural Hazards)
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Figure 1
<p>Experimental setting for the impact test: The boulder is released from different heights on the ramp. The wall sample is placed at the bottom of the ramp. The impact is registered by 4 video cameras, placed laterally and orthogonally to the ramp.</p>
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<p>Wall damage for tests 1a, 1b, and 1c. The direct impact area is on the lower half of the wall. The purple lines indicate open cracks with a width smaller than 10 mm, and the yellow lines indicate cracks with a width from 10 to 20 mm. The damage occurs mainly due to the failure of the brick–mortar bond. For test 1b, the vertical crack is additionally formed through brick failure. The deflection of the wall for the 3 tests is shown in green. The upper half of the wall does not have visible damage.</p>
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<p>Wall damage for tests 2, 3, 4, and 5. For test 2, a flexural mechanism of failure around the vertical axis is observed with a deflection of 200 mm, leading to the opening of a crack of 120 mm, which crosses the bricks and mortar. For tests 3, 4, and 5, full collapse is reached, and the boulders traverse the wall through a local breach which is formed by shear failure.</p>
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<p>Relation of boulder impact velocity and translational energy to wall damage.</p>
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<p>Wall damage results for the simulation of the experiments using the FEM.</p>
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<p>Time evolution of the displacement of a node in the centre of the boulder–wall interaction area indicating elastic and plastic response of the wall for H = 0.25 m and H = 0.5 m.</p>
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<p>Time evolution of the velocity and kinetic energy considering different boulder release heights. The continuous lines indicate the numerical modelling results and the dotted lines the experimental testing results.</p>
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20 pages, 6359 KiB  
Review
A Review of the Occurrence and Causes for Wildfires and Their Impacts on the Geoenvironment
by Arvin Farid, Md Khorshed Alam, Venkata Siva Naga Sai Goli, Idil Deniz Akin, Taiwo Akinleye, Xiaohui Chen, Qing Cheng, Peter Cleall, Sabatino Cuomo, Vito Foresta, Shangqi Ge, Luca Iervolino, Pierrette Iradukunda, Charles H. Luce, Eugeniusz Koda, Slobodan B. Mickovski, Brendan C. O’Kelly, Evan K. Paleologos, Dario Peduto, Evan John Ricketts, Mojtaba Sadegh, Theo S. Sarris, Devendra N. Singh, Prithvendra Singh, Chao-Sheng Tang, Guillermo Tardio, Magdalena Daria Vaverková, Max Veneris and Jan Winkleradd Show full author list remove Hide full author list
Fire 2024, 7(8), 295; https://doi.org/10.3390/fire7080295 - 22 Aug 2024
Viewed by 1202
Abstract
Wildfires have short- and long-term impacts on the geoenvironment, including the changes to biogeochemical and mechanical properties of soils, landfill stability, surface- and groundwater, air pollution, and vegetation. Climate change has increased the extent and severity of wildfires across the world. Simultaneously, anthropogenic [...] Read more.
Wildfires have short- and long-term impacts on the geoenvironment, including the changes to biogeochemical and mechanical properties of soils, landfill stability, surface- and groundwater, air pollution, and vegetation. Climate change has increased the extent and severity of wildfires across the world. Simultaneously, anthropogenic activities—through the expansion of urban areas into wildlands, abandonment of rural practices, and accidental or intentional fire-inception activities—are also responsible for a majority of fires. This paper provides an overall review and critical appraisal of existing knowledge about processes induced by wildfires and their impact on the geoenvironment. Burning of vegetation leads to loss of root reinforcement and changes in soil hydromechanical properties. Also, depending on the fire temperature, soil can be rendered hydrophobic or hydrophilic and compromise soil nutrition levels, hinder revegetation, and, in turn, increase post-fire erosion and the debris flow susceptibility of hillslopes. In addition to direct hazards, wildfires pollute air and soil with smoke and fire suppression agents releasing toxic, persistent, and relatively mobile contaminants into the geoenvironment. Nevertheless, the mitigation of wildfires’ geoenvironmental impacts does not fit within the scope of this paper. In the end, and in no exhaustive way, some of the areas requiring future research are highlighted. Full article
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Graphical abstract

Graphical abstract
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<p>(<b>a</b>) Map of Western U.S. active fires in September 2020 [<a href="#B22-fire-07-00295" class="html-bibr">22</a>]; (<b>b</b>) near-surface smoke (µg/m<sup>3</sup>) over Western U.S.A. on 17 September 2020 [<a href="#B23-fire-07-00295" class="html-bibr">23</a>].</p>
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<p>(<b>a</b>) Map of Western U.S. active fires in September 2020 [<a href="#B22-fire-07-00295" class="html-bibr">22</a>]; (<b>b</b>) near-surface smoke (µg/m<sup>3</sup>) over Western U.S.A. on 17 September 2020 [<a href="#B23-fire-07-00295" class="html-bibr">23</a>].</p>
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<p>U.S. drought monitor map of 4 October 2022 [<a href="#B28-fire-07-00295" class="html-bibr">28</a>].</p>
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<p>Wildfire regions in Europe, Western Asia, and North Africa on 24 August 2021 [<a href="#B34-fire-07-00295" class="html-bibr">34</a>].</p>
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<p>Number of fires in Mediterranean countries from 1980 to 2017 [<a href="#B36-fire-07-00295" class="html-bibr">36</a>].</p>
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<p>Photo (<b>a</b>) before and; (<b>b</b>) after the August 2021 megafire on the Island of Euboea, Greece.</p>
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<p>Hypothesized long-term changes in soil water repellence [<a href="#B83-fire-07-00295" class="html-bibr">83</a>]. Solid line: overall response; dotted line: short-term changes generated from fire; dashed line: long-term changes induced by increased biotic activity.</p>
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<p>Water Droplet Penetration Test (best-fit third-order polynomial) for a coarse-grained soil sample at hydrophobicity-inducing surrogate dilutions of (<b>a</b>) 0.6%, (<b>b</b>) 0.7%, and (<b>c</b>) 0.8%.</p>
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<p>Water Droplet Penetration Test (best-fit third-order polynomial) for a coarse-grained soil sample at hydrophobicity-inducing surrogate dilutions of (<b>a</b>) 0.6%, (<b>b</b>) 0.7%, and (<b>c</b>) 0.8%.</p>
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<p>Schematic diagram of root–soil system failure evolution with time after fire: (<b>a</b>) before the fire; (<b>b</b>) in the event of a fire; (<b>c</b>) one year after the fire; (<b>d</b>) two years after the fire, where <span class="html-fig-inline" id="fire-07-00295-i001"><img alt="Fire 07 00295 i001" src="/fire/fire-07-00295/article_deploy/html/images/fire-07-00295-i001.png"/></span> indicates infiltration (created based on [<a href="#B128-fire-07-00295" class="html-bibr">128</a>]).</p>
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