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Search Results (807)

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19 pages, 4376 KiB  
Article
Tracing the 2018 Sulawesi Earthquake and Tsunami’s Impact on Palu, Indonesia: A Remote Sensing Analysis
by Youshuang Hu, Aggeliki Barberopoulou and Magaly Koch
J. Mar. Sci. Eng. 2025, 13(1), 178; https://doi.org/10.3390/jmse13010178 - 19 Jan 2025
Viewed by 311
Abstract
The 2018 Sulawesi Earthquake and Tsunami serves as a backdrop for this work, which employs simple and straightforward remote sensing techniques to determine the extent of the destruction and indirectly evaluate the region’s vulnerability to such catastrophic events. Documenting damage from tsunamis is [...] Read more.
The 2018 Sulawesi Earthquake and Tsunami serves as a backdrop for this work, which employs simple and straightforward remote sensing techniques to determine the extent of the destruction and indirectly evaluate the region’s vulnerability to such catastrophic events. Documenting damage from tsunamis is only meaningful shortly after the disaster has occurred because governmental agencies clean up debris and start the recovery process within a few hours after the destruction has occurred, deeming impact estimates unreliable. Sentinel-2 and Maxar WorldView-3 satellite images were used to calculate well-known environmental indices to delineate the tsunami-affected areas in Palu, Indonesia. The use of NDVI, NDSI, and NDWI indices has allowed for a quantifiable measure of the changes in vegetation, soil moisture, and water bodies, providing a clear demarcation of the tsunami’s impact on land cover. The final tsunami inundation map indicates that the areas most affected by the tsunami are found in the urban center, low-lying regions, and along the coast. This work charts the aftermath of one of Indonesia’s recent tsunamis but may also lay the groundwork for an easy, handy, and low-cost approach to quickly identify tsunami-affected zones. While previous studies have used high-resolution remote sensing methods such as LiDAR or SAR, our study emphasizes accessibility and simplicity, making it more feasible for resource-constrained regions or rapid disaster response. The scientific novelty lies in the integration of widely used environmental indices (dNDVI, dNDWI, and dNDSI) with threshold-based Decision Tree classification to delineate tsunami-affected areas. Unlike many studies that rely on advanced or proprietary tools, we demonstrate that comparable results can be achieved with cost-effective open-source data and straightforward methodologies. Additionally, we address the challenge of differentiating tsunami impacts from other phenomena (et, liquefaction) through index-based thresholds and propose a framework that is adaptable to other vulnerable coastal regions. Full article
(This article belongs to the Special Issue Coastal Disaster Assessment and Response)
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Figure 1
<p>(<b>Top</b>) Relative location of Sulawesi Island and Palu City (red circle) in Indonesia. (<b>Lower left</b>) Study Area: Palu City (red). (<b>Lower right</b>) Palu City Administrative Division.</p>
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<p>Remote Sensing Workflow. To derive the final tsunami-affected areas, a Decision Tree classification method was employed, integrating the dNDVI, dNDSI, and dNDWI values obtained from pre- and post-event indices.</p>
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<p>False color composite after the tsunami (2018/02/10 (SWIR, VNIR, and RED as RGB bands).</p>
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<p>Reclassified NDVI for pre-tsunami (2018/09/27) (<b>left</b>) and post-tsunami imagery (2018/10/02) based on the NDVI classification criteria developed by Al-Doski et al. [<a href="#B51-jmse-13-00178" class="html-bibr">51</a>] (<b>right</b>).</p>
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<p>Reclassified NDWI for pre-tsunami (2018/09/27) (<b>left</b>) and post-tsunami imagery (2018/10/02) (<b>right</b>).</p>
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<p>Reclassified NDSI for pre-tsunami (2018/09/27) (<b>left</b>) and post-tsunami imagery (2018/10/02) (<b>right</b>).</p>
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<p>Computed NDVI, NDSI, and NDWI for pre-tsunami (2018/09/27) and post-tsunami (2018/10/02) for a small area of Palu. Dashed line delineates the area affected by the tsunami in the post-disaster images.</p>
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<p>Cumulative frequency distribution of the differences between NDVI, NDSI, and NDWI.</p>
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<p>Mapping NDVI, NDSI, and NDWI based on the threshold values. Dark pixels indicate areas impacted by the tsunami.</p>
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<p>Final tsunami inundation map using a Decision Tree classification method, integrating the dNDVI, dNDSI, and dNDWI values obtained from pre- and post-event indices.</p>
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16 pages, 848 KiB  
Article
Coal Tar Naphtha Refining: Phenol Alkylation with 1-Hexene and the Impact of Pyridine
by Yuhan Xia and Arno de Klerk
Processes 2025, 13(1), 194; https://doi.org/10.3390/pr13010194 - 12 Jan 2025
Viewed by 440
Abstract
Coal tar naphtha is produced from coal carbonization, moving bed coal gasification, and thermal liquefaction of coal. The naphtha can contain up to 60% aromatics and 15% olefins, as well as nitrogen-, oxygen-, and sulfur-containing compounds. Usually only hydrotreating is considered, but when [...] Read more.
Coal tar naphtha is produced from coal carbonization, moving bed coal gasification, and thermal liquefaction of coal. The naphtha can contain up to 60% aromatics and 15% olefins, as well as nitrogen-, oxygen-, and sulfur-containing compounds. Usually only hydrotreating is considered, but when producing motor gasoline, olefin–aromatic alkylation could reduce the associated octane number loss due to olefin hydrogenation by converting olefins to alkylated phenols and aromatics. The plausibility of using acid-catalyzed alkylation with coal tar naphtha, which contains nitrogen bases, was investigated by studying a model system comprising phenol and 1-hexene in the absence and presence of pyridine. It was found that pyridine only inhibited conversion over a range of amorphous silica–alumina catalysts. The most effective catalyst was Siral 30 (30% silica, 70% alumina) and at 315 °C, 0.05 wt% pyridine caused a 35% inhibition of phenol conversion compared to conversion in the absence of pyridine. Catalyst activity could be restored by rejuvenating the catalyst with clean feed at a higher temperature. The results supported a description of phenol alkylation with olefins that took place by at least two pathways, one involving protonation of the olefin (typical for Friedel–Crafts alkylation) and one where the olefin is the nucleophile. Full article
(This article belongs to the Special Issue Synthesis, Catalysis and Applications of Organic Chemistry)
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<p>Electron impact fragmentation of phenolic species used for identification.</p>
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<p>Conversion of an equimolar phenol and 1-hexene mixture in a flow reactor over amorphous silica–alumina catalysts (Siral 30 ●, Siral 40 ■) at 315 °C, near atmospheric pressure, and WHSV of 17 h<sup>−1</sup>. Pyridine (▲) was added to the feed at concentrations in the range of 0.05–0.25%, as indicated.</p>
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<p>Self-catalyzed phenol–olefin alkylation.</p>
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14 pages, 3172 KiB  
Article
Fabrication and Performance Enhancement of Wood Liquefaction-Based Carbon Fibers Modified with Alumina Nanoparticles
by Linshuang Gan, Yijing Liu, Zaibirinisa Yimin, Jianglong Wu, Jialin Lv and Zhigao Liu
Polymers 2025, 17(2), 155; https://doi.org/10.3390/polym17020155 - 9 Jan 2025
Viewed by 391
Abstract
In this paper, alumina-modified wood liquefaction (AL-WP) was prepared by blending nano-alumina (Al2O3) into wood liquefaction phenolic resin (WP) using a co-blending method. Alumina-modified wood liquefaction protofilament fiber (AL-WPF) was obtained by melt-spinning, curing, and thermo-curing processes, which were [...] Read more.
In this paper, alumina-modified wood liquefaction (AL-WP) was prepared by blending nano-alumina (Al2O3) into wood liquefaction phenolic resin (WP) using a co-blending method. Alumina-modified wood liquefaction protofilament fiber (AL-WPF) was obtained by melt-spinning, curing, and thermo-curing processes, which were followed by carbonization to obtain alumina-modified wood liquefaction carbon fiber (AL-WCF). This paper focuses on the enhancement effect of nano-alumina doping on the mechanical properties and heat resistance of wood liquefaction carbon fiber (WCF), explores the evolution of graphite microcrystalline structure during the high-temperature carbonization process, and optimizes the curing conditions of AL-WPF. The results showed that the introduction of Al2O3 significantly improved the mechanical properties and heat resistance of carbon fibers. When 1.5% Al2O3 was doped and carbonized at 1000 °C, the tensile strength of AL-WCF was increased from 33.78 MPa to 95.74 MPa, there was an enhancement of 183%, its residual carbon rate could reach 79.2%, which was better than that of the undoped wood liquefaction (WCF), and it exhibited a more substantial heat-resistant property. In addition, the best curing process for alumina nanoparticle wood liquefiers was obtained by optimizing the curing conditions: hydrochloric acid concentration of 16%, formaldehyde concentration of 18.5%, temperature increase rate of 15 °C/min, holding time of 3 h, and holding temperature of 100 °C. These studies provide a theoretical basis and technical support for developing and applying carbon fibers from alumina-modified wood liquefiers. Full article
(This article belongs to the Special Issue Natural Fiber-Based Green Materials)
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Graphical abstract
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<p>SEM images of initial fibers with different alumina additions: (<b>a</b>) cross-sectional surface of WP, (<b>a<sub>1</sub></b>) radial surface of WP; (<b>b</b>) cross-sectional surface of 0.5% AL-WP, (<b>b<sub>1</sub></b>) radial surface of 0.5% AL-WP; (<b>c</b>) cross-sectional surface of 1% AL-WP, (<b>c<sub>1</sub></b>) radial surface of 1% AL-WP; (<b>d</b>) cross-sectional surface of 1.5% AL-WP, (<b>d<sub>1</sub></b>) radial surface of 1.5% AL-WP; (<b>e</b>) cross-sectional surface of 2% AL-WP, (<b>e<sub>1</sub></b>) radial surface of 2% AL-WP; (<b>f</b>) cross-sectional surface of 2.5% AL-WP, (<b>f<sub>1</sub></b>) radial surface of 2.5% AL-WP.</p>
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<p>(<b>a</b>) Infrared spectra of WP and AL-WP; (<b>b</b>) XRD patterns of WP and AL-WP; (<b>c</b>) TG curves of WP and AL-WP; (<b>d</b>) DTG curves of WP and AL-WP.</p>
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<p>(<b>a</b>) Infrared spectra of WPF and AL-WPF under thermal curing conditions; (<b>b</b>) tensile strength and elongation at break of WPF and AL-WPF under thermal curing conditions; (<b>c</b>) TG curves of AL-WPF under curing conditions with different hydrochloric acid concentrations; (<b>d</b>) TG curves of AL-WPF under curing conditions with different holding times; (<b>e</b>) TG curves of AL-WPF under curing conditions with different holding temperatures.</p>
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<p>Tensile strength and elongation at break of nano-alumina-modified wood liquefaction carbon fibers at different temperatures: (<b>a</b>) WCF; (<b>b</b>) AL-WCF.</p>
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<p>The SEM images of carbon fibers at a carbonization temperature of 1000 °C: (<b>a</b>) represents the radial surface of WCF; (<b>b</b>) represents the radial surface of AL-WCF; (<b>c</b>) shows the cross-sectional surface of WCF; (<b>d</b>) shows the cross-sectional surface of AL-WCF.</p>
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<p>(<b>a</b>) TG curves of WCF and AL-WCF; (<b>b</b>) DTG curves of WCF and AL-WCF; (<b>c</b>) XRD spectra of WCF at different carbonization temperatures; (<b>d</b>) XRD spectra of AL-WCF at different carbonization temperatures; (<b>e</b>) Raman spectra of WCF at different carbonization temperatures; (<b>f</b>) Raman spectra of AL-WCF at different carbonization temperatures.</p>
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19 pages, 1966 KiB  
Article
Polymeric Coatings with Electrolyzed Acidic Water: A Novel Approach to Extending Egg Shelf Life and Quality
by Gina Parra A, Claudia Clavijo, Alejandro Castillo and Rodrigo Ortega-Toro
Polymers 2025, 17(1), 84; https://doi.org/10.3390/polym17010084 - 31 Dec 2024
Viewed by 383
Abstract
Electrolyzed acidic water (EAW) contains hypochlorous acid as its active compound, which is a potent antimicrobial. It was encapsulated in polymeric coatings and applied to the surface of eggs. The antimicrobial activity and the ability to extend the shelf life of eggs at [...] Read more.
Electrolyzed acidic water (EAW) contains hypochlorous acid as its active compound, which is a potent antimicrobial. It was encapsulated in polymeric coatings and applied to the surface of eggs. The antimicrobial activity and the ability to extend the shelf life of eggs at ambient temperature for 45 days were evaluated, by physical, microbiological, and sensory analyses. The analysis also included the evaluation of mechanical, thermal, and crystallinity properties and the interaction between the coating components and the eggshell. The results showed that eggs from young, middle-aged, and adult hens, encapsulated and coated with EAW, hydroxypropyl methylcellulose, polyvinyl alcohol, and chitosan, gained resistance and a glossy appearance. The thickness of the coating was 2.9 µm for young and adult hens’ eggs and 2.60 µm for those of old hens, as observed by SEM. Shelf life was extended to 45 days under refrigeration and more than 30 days at ambient temperature. Coated eggs were acceptable for 85% of the panelists compared to 57% acceptance of non-coated eggs. The encapsulation and coating with EAW as an antimicrobial agent improved the surface protection of commercial eggs, reduced albumen liquefaction, and maintained quality by acting as a barrier against air, thereby preserving sensory characteristics. Full article
(This article belongs to the Section Polymer Applications)
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<p>SEM images of cross-sectional views of eggshell surfaces after coating application on eggs laid by young, middle-aged, and adult hens. (<b>A</b>) View of an eggshell in the control sample, (<b>B</b>) view of an eggshell from an egg laid by middle-aged hens, and (<b>C</b>) view of an eggshell of an adult hen-laid egg.</p>
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<p>FTIR spectra on eggshell surfaces with encapsulation and coating. (<b>A</b>) spectra on control eggshells (no EC) and (<b>B</b>) spectra for eggshells with EC.</p>
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<p>X-ray diffractograms of materials used for encapsulation and coating.</p>
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<p>Crystallinity analysis of materials used for eggshell encapsulation and coating. The values were obtained from X-ray diffraction analysis.</p>
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<p>Heat flux of eggshell with EC, as determined by differential scanning calorimetry.</p>
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<p>Thermal stability of the eggshells with EC. Thermal stability was expressed by weight loss using thermogravimetric analysis.</p>
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<p>Microbial counts on eggs with and without coating at the end of a 45 d storage.</p>
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21 pages, 12918 KiB  
Article
Structural Designing of Supersonic Swirling Devices Based on Computational Fluid Dynamics Theory
by Qian Huang, Huirong Huang, Xueyuan Long, Yuan Tian and Jiang Meng
Appl. Sci. 2025, 15(1), 151; https://doi.org/10.3390/app15010151 - 27 Dec 2024
Viewed by 353
Abstract
The supersonic swirling device is a new apparatus that can be used for natural-gas liquefaction. The structure of the supersonic swirling device has an important impact on the liquefaction efficiency. Therefore, this study presents a structural design method for supersonic cyclones based on [...] Read more.
The supersonic swirling device is a new apparatus that can be used for natural-gas liquefaction. The structure of the supersonic swirling device has an important impact on the liquefaction efficiency. Therefore, this study presents a structural design method for supersonic cyclones based on CFD theory. Using the production parameters of a liquefied natural gas (LNG) peak-shaving station as the study case, a detailed design and design comparison of each part of the supersonic swirling separator are carried out. An optimum LNG supersonic swirling separator design was obtained. To ensure that the designed supersonic swirling separator achieved better liquefaction effectiveness, it was ascertained that no large shockwaves were generated in the de Laval nozzle, the pressure loss on the swirler was small, and the swirler was able to produce a large centripetal acceleration. The opening angle of the diffuser and the length of the straight tube were designed considering the location at which normal shockwaves were generated. The location at which shockwaves are generated and the friction effect are important parameters that determine the gap size. With this design guidance, the optimal structural dimensions of the supersonic swirling device for a given processing capacity were determined as follows: a swirler with six vanes and an 8 mm wide channel; a 10D-long straight tube, an opening angle of 20° between the straight tube and the divergent section, and a gap size of 2 mm. Compared with “Twister II”, the new device has better liquefaction efficiency. Full article
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<p>Structure of supersonic swirling separator.</p>
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<p>Schematic of the radial swirl generator.</p>
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<p>Schematic of blade dimension calculation.</p>
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<p>Grid generation of supersonic swirling separator.</p>
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<p>Distribution curve of temperature along the nozzle axis at different cells.</p>
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<p>Comparison of static pressure ratio of nozzle axis with experimental data. (<b>a</b>) inlet steam partial pressure, 1.0 kPa; (<b>b</b>) inlet steam partial pressure, 0.5 kPa; and (<b>c</b>) inlet steam partial pressure 0.26 kPa.</p>
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<p>Pressure distribution at different nozzle sections: (<b>a</b>) four vanes, 6 mm wide channel; (<b>b</b>) four vanes, 8 mm wide channel; (<b>c</b>) six vanes, 6 mm wide channel; (<b>d</b>) six vanes, 8 mm wide channel; (<b>e</b>) eight vanes, 6 mm wide channel; and (<b>f</b>) eight vanes, 8 mm wide channel.</p>
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<p>Mach number distributions at different nozzle sections: (<b>a</b>) four vanes, 6 mm wide channel; (<b>b</b>) four vanes, 8 mm wide channel; (<b>c</b>) six vanes, 6 mm wide channel; (<b>d</b>) six vanes, 8 mm wide channel; (<b>e</b>) eight vanes, 6 mm wide channel; and (<b>f</b>) eight vanes, 8 mm wide channel.</p>
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<p>Centripetal acceleration distribution at different nozzle sections: (<b>a</b>) four vanes, 6 mm wide channel; (<b>b</b>) four vanes, 8 mm wide channel; (<b>c</b>) six vanes, 6 mm wide channel; (<b>d</b>) six vanes, 8 mm wide channel; (<b>e</b>) eight vanes, 6 mm wide channel; and (<b>f</b>) eight vanes, 8 mm wide channel.</p>
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<p>Distribution cloud of Mach numbers in the nozzle.</p>
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<p>Distribution curve of temperature along the nozzle axis.</p>
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<p>Distribution curve of pressure along the nozzle axis.</p>
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<p>The distribution curve of pressure along the nozzle axis.</p>
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<p>Pressure distribution of straight section at lengths 6D, 8D, and 10D and expansion angles (<b>a</b>) 20°, (<b>b</b>) 24°, and (<b>c</b>) 28°.</p>
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<p>Pressure distributions at different lengths of the straight pipe at 20°, 24°, and 28°: (<b>a</b>) 6D; (<b>b</b>) 8D; and (<b>c</b>) 10D.</p>
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<p>Temperature distributions in the discharge gap under different clearance sizes: (<b>a</b>) 1 mm; (<b>b</b>) 2 mm; and (<b>c</b>) 3 mm.</p>
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<p>Dimensions of the supersonic cyclone separator for natural gas liquefaction.</p>
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<p>Influence of inlet pressure on liquefaction rate.</p>
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30 pages, 7974 KiB  
Article
The Complex Valorization of Black Alder Bark Biomass in Compositions of Rigid Polyurethane Foam
by Alexandr Arshanitsa, Matiss Pals, Laima Vevere, Lilija Jashina and Oskars Bikovens
Materials 2025, 18(1), 50; https://doi.org/10.3390/ma18010050 - 26 Dec 2024
Viewed by 450
Abstract
The use of black alder (BA) bark biomass in rigid polyurethane (PUR) foam compositions was the main task of investigation. Extractive compounds isolated from the bark through hot water extraction were used as precursors for bio-polyol synthesis via acid-free liquefaction with the polyether [...] Read more.
The use of black alder (BA) bark biomass in rigid polyurethane (PUR) foam compositions was the main task of investigation. Extractive compounds isolated from the bark through hot water extraction were used as precursors for bio-polyol synthesis via acid-free liquefaction with the polyether polyol Lupranol 3300 and through oxypropylation with propylene carbonate. The OH functionality and composition of the polyols were analyzed via wet chemistry and FTIR spectroscopy. The solid remaining after the isolation of extractive compounds was also utilized as a natural filler in PUR foams. The effects of replacing commercial polyols with bio-polyols on the foam rising rate and their mechanical properties, morphology, thermal conductivity, and thermal degradation characteristics were examined. The oxypropylated extractive-based PUR compositions demonstrated the most favorable balance between the biomass content and material properties. At an apparent density of 40 kg/m3, the compressive strength of the produced foams was enhanced by 1.4–1.5 times, while the maximum thermal degradation rate in air decreased by 3.8–6.5 times compared to reference materials without adversely affecting the foam morphology. The composition based on liquefied extractives showed lower performance but still improved properties relative to the reference foams. Introducing 3.7–14% of extracted bark into the foam compositions increased the biomass content to 22–24%, although this led to a decrease in the compressive strength and thermal stability. It was shown that partially substituting fossil-derived components with renewable bark biomass in the composition of PUR foams allows for materials with characteristics similar or better to petrochemical-based materials to be obtained. Therefore, the results presented can be considered a contribution to addressing environmental problems and promoting the development of a sustainable economy. Full article
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<p>The content of OH groups of different originations in BA bark extractives according to <sup>31</sup>P NMR.</p>
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<p>The content of liquefied biomass in bio-polyols as a function of the duration and temperature of processing at different biomass contents in the starting suspension: 10% (<b>a</b>), 20% (<b>b</b>), 30% (<b>c</b>), 40% (<b>d</b>).</p>
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<p>Plots of liquefied biomass content in bio-polyol vs. biomass content in the initial suspension after 6 h of extractive liquefaction at different temperatures (<b>a</b>), and the effect of the duration on the yield of liquefied BA bark extractives, independent of the temperature and biomass content in the initial suspension (<b>b</b>).</p>
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<p>FTIR spectra of BA extract (<b>a</b>) pure Lupranol 3300 and bio-polyols with varying biomass contents liquefied at 150 °C for 6 h (<b>b</b>).</p>
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<p>FTIR spectra in the region of 1800–1500 cm<sup>−1</sup> of pure Lupranol 3300 and bio-polyols with varying biomass contents liquefied at 150 °C during 6 h (<b>a</b>) and FTIR spectra absorbance ratio (A<sub>1710 cm</sub><sup>−1</sup>/A<sub>1515 cm</sub><sup>−1</sup>) of bio-polyols synthesized at different temperatures vs. the biomass content in them (<b>b</b>).</p>
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<p>Experimental and calculated content of phenolic and OH<sub>COOH</sub> groups (in bio-polyols obtained at different temperatures) dependent on the liquified biomass content.</p>
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<p>OHV (<b>a</b>) and acid numbers (<b>b</b>) of bio-polyols obtained at different temperatures and dependence on the liquified biomass content in them.</p>
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<p>Flow curves (<b>a</b>) and dynamic viscosity at 25 °C (<b>b</b>) of bio-polyols with different contents of biomass synthesized at 150 °C.</p>
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<p>The DTG (<b>a</b>) and DSC (<b>b</b>) curves of untreated and extracted bark in air media.</p>
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<p>The particle size distribution of ground-extracted bark.</p>
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<p>SEM image of ground BA bark at different magnifications: ×500 (<b>left</b>) and ×5000 (<b>right</b>).</p>
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<p>The height (<b>a</b>) and rise rate (<b>b</b>) of PUR foams versus time.</p>
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<p>Effect of extracted bark content as a filler in PUR foam on the height (<b>a</b>,<b>c</b>,<b>e</b>) and foam rise rate (<b>b</b>,<b>d</b>,<b>f</b>) across different PUR foam compositions: Ref. 2 (<b>a</b>,<b>b</b>); BP-3/F (<b>c</b>,<b>d</b>); and BP-5/F (<b>e</b>,<b>f</b>).</p>
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<p>FTIR spectra of rigid PUR foams.</p>
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<p>Apparent density, normalized strength, and Young’s modulus under axial compression parallel to the foaming direction for reference and bio-polyol-based rigid PUR foams (sample abbreviations are consistent with <a href="#materials-18-00050-t003" class="html-table">Table 3</a>).</p>
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<p>Apparent density, normalized strength, and Young’s modulus under axial compression parallel to the foaming direction of reference and bio-polyol-based rigid PUR foams as a function of the filler content (sample abbreviations are consistent with <a href="#materials-18-00050-t003" class="html-table">Table 3</a>).</p>
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<p>The closed-cell content in reference and bio-polyol based PUR foams (<b>a</b>); the effect of filler content on the closed-cell content in bio-polyol based foams (<b>b</b>) (sample abbreviations are consistent with <a href="#materials-18-00050-t003" class="html-table">Table 3</a> and <a href="#materials-18-00050-t004" class="html-table">Table 4</a>).</p>
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<p>SEM images of reference and bio-polyols-based PUR foams in parallel with foaming directions. Ref. 2 (<b>a</b>); BP-3 (<b>b</b>); BP-4 (<b>c</b>).</p>
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<p>The TG and DTG curves of reference PUR foams and bio-polyol-based PUR foams in air.</p>
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<p>TG and DTG curves in air for unfilled (BP-3, BP-5) (<b>a</b>) and 13%-filler-containing (BP-3/F, BP-5/F) bio-polyol-based PUR foams (<b>b</b>).</p>
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22 pages, 6005 KiB  
Article
A New Method for Evaluating Liquefaction by Energy-Based Pore Water Pressure Models
by Jianlei Zhang, Qiangong Cheng, Haozhen Fan, Mengjie Dai, Yan Li, Jiujiang Wu and Yufeng Wang
Coatings 2025, 15(1), 7; https://doi.org/10.3390/coatings15010007 - 24 Dec 2024
Viewed by 438
Abstract
Liquefaction-induced damage can be mitigated through remediation methods, contingent upon a thorough evaluation of liquefaction, which necessitates comprehensive investigation. This paper presents a novel energy-based pore pressure model for the assessment of liquefaction potential, utilizing cyclic triaxial numerical tests. In this model, the [...] Read more.
Liquefaction-induced damage can be mitigated through remediation methods, contingent upon a thorough evaluation of liquefaction, which necessitates comprehensive investigation. This paper presents a novel energy-based pore pressure model for the assessment of liquefaction potential, utilizing cyclic triaxial numerical tests. In this model, the energy of the earthquake is quantified using the Arias intensity. The validity of the energy-based pore pressure model was corroborated by the results of cyclic triaxial tests. Based on the validated model, a new methodology that incorporates permeability and the shear stress reduction coefficient was proposed for the evaluation of liquefaction potential. This new approach was further validated through centrifuge tests and numerical simulations. The findings indicate that the proposed method can accurately predict the generation and accumulation of excess pore pressure, thereby demonstrating its efficacy in evaluating ground liquefaction potential. Full article
(This article belongs to the Special Issue Advances in Pavement Materials and Civil Engineering)
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<p>Relationship between excess pore pressure ratio and Arias intensity (<span class="html-italic">I<sub>a</sub></span>).</p>
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<p>Stress conditions of soil element under earthquake.</p>
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<p>Stress conditions of soil element in two-directional cyclic triaxial test and unidirectional cyclic triaxial test.</p>
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<p>Relationship of soil element stress conditions in uni-directional and two-directional cyclic triaxial test.</p>
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<p>Numerical model of two-directional cyclic triaxial test.</p>
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<p>Relationship between pore water pressure and cyclic stress ratio (<span class="html-italic">CSR</span>) in the two-directional cyclic triaxial numerical test for (<b>a</b>) <span class="html-italic">D<sub>r</sub></span> = 60% and (<b>b</b>) <span class="html-italic">D<sub>r</sub></span> = 90%.</p>
Full article ">Figure 7
<p>Relationship between pore water pressure and the modified cyclic stress ratio (<span class="html-italic">M<sub>csr</sub></span>) in the two-directional cyclic triaxial numerical test for (<b>a</b>) <span class="html-italic">D<sub>r</sub></span> = 60% and (<b>b</b>) <span class="html-italic">D<sub>r</sub></span> = 90%.</p>
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<p>Relationship between <span class="html-italic">I<sub>a</sub></span> and <span class="html-italic">CSR</span> in soil element when initial liquefaction triggered in saturated soil for (<b>a</b>) <span class="html-italic">D<sub>r</sub></span> = 60% and (<b>b</b>) <span class="html-italic">D<sub>r</sub></span> = 90%.</p>
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<p>Relationship between <span class="html-italic">MI<sub>a</sub></span> and <span class="html-italic">CSR</span> in soil element when initial liquefaction triggered in saturated soil for (<b>a</b>) <span class="html-italic">D<sub>r</sub></span> = 60% and (<b>b</b>) <span class="html-italic">D<sub>r</sub></span> = 90%.</p>
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<p>Relationship between the modified <span class="html-italic">CRS</span> (<span class="html-italic">M<sub>csr</sub></span>) and consolidation pressure for (<b>a</b>) <span class="html-italic">D<sub>r</sub></span> = 60% and (<b>b</b>) <span class="html-italic">D<sub>r</sub></span> = 90%.</p>
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<p>Relationship between the modified <span class="html-italic">CRS</span> (<span class="html-italic">M<sub>csr</sub></span>) and consolidation pressure ratio for (<b>a</b>) <span class="html-italic">D<sub>r</sub></span> = 60% and (<b>b</b>) <span class="html-italic">D<sub>r</sub></span> = 90%.</p>
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<p>Comparison of computed and experimental pore pressure time histories for (<b>a</b>) NingHe wave and (<b>b</b>) TangShan wave [<a href="#B15-coatings-15-00007" class="html-bibr">15</a>].</p>
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<p>Curve of shear stress reduction function [<a href="#B23-coatings-15-00007" class="html-bibr">23</a>].</p>
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<p>Schematic of permeability coefficient function.</p>
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<p>Numerical model of free field.</p>
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<p>Comparison of experimental, numerical, and computed pore pressure.</p>
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<p>Comparison of numerical and computed maximum excess pore water pressure ratio.</p>
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19 pages, 4816 KiB  
Article
Optimization of Enzymatic Hydrolysis and Fermentation Processing for Set-Type Oat Yogurt with Favorable Acidity and Coagulated Texture
by Wenjie Xu, Xinzhu Wu, Chen Xia, Zicong Guo, Zhengyuan Zhai, Yongqiang Cheng and Ju Qiu
Foods 2024, 13(24), 4180; https://doi.org/10.3390/foods13244180 - 23 Dec 2024
Viewed by 446
Abstract
The key role of enzymatic hydrolysis and fermentation in the sensory quality of set yogurt made from whole oats was demonstrated. The optimal process was established by the orthogonal and response surface methodology based on the acidity, textural, and rheological properties. The results [...] Read more.
The key role of enzymatic hydrolysis and fermentation in the sensory quality of set yogurt made from whole oats was demonstrated. The optimal process was established by the orthogonal and response surface methodology based on the acidity, textural, and rheological properties. The results indicated that the enzymatic hydrolysis appropriately consisted of liquefaction with 12 U/mL α-amylase at 70 °C and pH 6.5 for 60 min, followed by saccharification with 400 U/mL α-1,4-glucan glucohydrolase at 60 °C and pH 4.5 for 60 min. The Streptococcus thermophilus ST15 and Lactobacillus bulgaricus 20249 strains were the most efficacious strains, with a 0.1% inoculation for the fermentation at 42 °C for 16 h. So, a soft semisolid oat yogurt formed with an 8% solid–liquid ratio, which exhibited an acidity of 73.17 °T, a cohesiveness ratio of 0.51, and a maximum apparent viscosity of 1902.67 Pa·s. The coagulated texture of the oat yogurt was closely associated with the exopolysaccharide (EPS) yield up to 304.99 mg/L. These findings supported the optimal processing of oat yogurt, especially its correlation with the high capacity of EPS production by strains. It is an innovative and feasible way to improve the properties of set-type oat yogurt, especially the utilization of the whole oat. Full article
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<p>Changes in dextrose equivalent (DE) value of enzymatic hydrolysate under different liquefaction ((<b>A</b>) enzyme concentration; (<b>B</b>) temperature; (<b>C</b>) time; (<b>D</b>) pH) or saccharification conditions ((<b>E</b>) enzyme concentration; (<b>F</b>) temperature; (<b>G</b>) time; (<b>H</b>) pH). Lowercase letters expressed the statistical significance among different yogurt groups at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Water-holding capacity (<b>A</b>), cohesiveness (<b>B</b>), and viscosity (<b>C</b>) of fermented oat hydrolysate (<b>D</b>), oat paste (<b>E</b>), and set-type oat yogurt (<b>F</b>). For the fermented oat hydrolysate, the enzymatic hydrolysate of oats was fermented directly; for the oat paste, the enzymatic hydrolysate and flour mixture was heated; for the set-type oat yogurt, oat paste was fermented. Lowercase letters in the column express the statistical significance among different yogurt groups at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Acidity (<b>A</b>), cohesiveness (<b>B</b>), and viable bacterial count (<b>C</b>) of set-type oat yogurt fermented by different strain combinations, as well as the ability of these combinations to produce exopolysaccharides (EPSs) (<b>D</b>). <span class="html-italic">ST15</span>, <span class="html-italic">Streptococcus thermophilus15</span>; <span class="html-italic">ST20370</span>, <span class="html-italic">Streptococcus thermophilus 20370</span>; <span class="html-italic">LB20247</span>, <span class="html-italic">Lactobacillus bulgaricus 20247</span>; <span class="html-italic">LB20249</span>, <span class="html-italic">Lactobacillus bulgaricus 20249</span>; <span class="html-italic">LB20271</span>, <span class="html-italic">Lactobacillus bulgaricus 20271</span>. Lowercase letters in the column express the statistical significance among different yogurt groups at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Changes in acidity (<b>A</b>), pH (<b>B</b>), and cohesiveness (<b>C</b>) of set-type oat yogurt at different temperatures, times, solid–liquid ratios, and inoculation volumes. The x-axis labels A, B, C, D, and E correspond to the variable conditions defined in the legend, in the specified order.</p>
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<p>Rheological properties of set-type oat yogurt. Plots of steady shear analysis (<b>A</b>), storage modulus and loss modulus (<b>B</b>), and tan δ (G″/G′ ratio) (<b>C</b>) for set-type oat yogurt with different temperatures (1), times (2), solid–liquid ratios (3), and inoculation volumes (4).</p>
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<p>Response surface and contour plots for acidity (<b>A</b>), pH (<b>B</b>), cohesiveness (<b>C</b>), and apparent viscosity (<b>D</b>). 1, inoculation volume and solid–liquid ratio; 2, inoculation volume and temperature; 3, solid–liquid ratio and temperature. Red color means the higher response value (acidity, pH, cohesiveness, and apparent viscosity), while green/blue color means the lower response value (acidity, pH, cohesiveness, and apparent viscosity).</p>
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<p>Spider plot for electronic tongue sensory score of fermented oat hydrolysate, oat paste, and set-type oat yogurt (<b>A</b>). Correlation analysis of EPS and set-type oat yogurt cohesiveness, apparent viscosity, acidity, pH, and viable bacterial count (<b>B</b>). Principal component analysis (PCA) of EPS and set-type oat yogurt cohesiveness, apparent viscosity, acidity, pH, and viable bacterial count (<b>C</b>).</p>
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19 pages, 3151 KiB  
Article
Catalyst-Free Depolymerization of Methanol-Fractionated Kraft Lignin to Aromatic Monomers in Supercritical Methanol
by Shubho Ghosh, Masud Rana and Jeong-Hun Park
Energies 2024, 17(24), 6482; https://doi.org/10.3390/en17246482 - 23 Dec 2024
Viewed by 455
Abstract
Lignin is considered a renewable source for the production of valuable aromatic chemicals and liquid fuel. Solvent depolymerization of lignin is a fruitful strategy for the valorization of lignin. However, Kraft lignin is highly prone to produce char (a by-product) during the hydrothermal [...] Read more.
Lignin is considered a renewable source for the production of valuable aromatic chemicals and liquid fuel. Solvent depolymerization of lignin is a fruitful strategy for the valorization of lignin. However, Kraft lignin is highly prone to produce char (a by-product) during the hydrothermal depolymerization process due to its poor solubility in organic solvents. Therefore, the minimization of char formation remains challenging. The purpose of the present study was to fractionate Kraft lignin in methanol to obtain low-molecular-weight fractions that could be further depolymerized in supercritical methanol to produce aromatic monomers and to suppress char formation. The results showed that the use of methanol-soluble lignin achieved a bio-oil yield of 45.04% and a char yield of 39.6% at 280 °C for 2 h compared to 28.57% and 57.73%, respectively, when using raw Kraft lignin. Elemental analysis revealed a high heating value of 30.13 MJ kg−1 and a sulfur content of only 0.09% for the bio-oil derived from methanol-soluble lignin. The methanol extraction process reduced the oxygen content and increased the hydrogen and carbon contents in the modified lignin and bio-oil, indicating that the extracted lignin fraction had an enhanced deoxygenation capability and a higher energy content. These findings highlight the potential of methanol-soluble Kraft lignin as a valuable resource for sustainable energy production and the production of aromatic compounds. Full article
(This article belongs to the Special Issue Cutting-Edge Developments in Waste-to-Energy Technologies)
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<p>Preparation of methanol-soluble lignin from raw Kraft lignin.</p>
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<p>Lignin depolymerization and bio-oil separation processes.</p>
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<p>FT-IR analysis of (A) raw Kraft lignin and (B) methanol-soluble Kraft lignin.</p>
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<p>Yields of depolymerized products obtained after hydrothermal liquefaction of methanol-soluble Kraft lignin in supercritical methanol under different (<b>A</b>) temperatures and (<b>B</b>) reaction times.</p>
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<p>Yields of depolymerized products obtained after hydrothermal liquefaction of raw Kraft lignin and methanol-soluble Kraft lignin in supercritical methanol at 280 °C after 2 h.</p>
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<p>Yields of major compounds obtained from raw Kraft lignin and methanol-soluble lignin depolymerization at 280 °C after 2 h.</p>
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<p>(<b>A</b>) FT-IR analysis of bio-oil obtained from methanol-soluble Kraft lignin with a reaction time of 2 h and temperatures of (a) 260 °C, (b) 280 °C, (c) 300 °C, and (d) 320 °C. (<b>B</b>) FT-IR analysis of bio-oil obtained from methanol-soluble Kraft lignin at a temperature of 280 °C and reaction times of (a) 1 h, (b) 2 h, (c) 3 h, and (d) 4 h.</p>
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<p>Van Krevelen diagrams of Kraft lignin, methanol-soluble Kraft lignin, and bio-oil. (<b>A</b>) O/C vs. H/C and (<b>B</b>) S/C vs. H/C ratios for Kraft lignin (a and a′), methanol-soluble Kraft lignin (b and b′), bio-oil obtained from Kraft lignin at 280 °C and 2 h (c and c′), and bio-oil obtained from methanol-soluble Kraft lignin at 260 °C and 2 h (d and d′), 280 °C and 1 h (e and e′), 280 °C and 2 h (f and f′), and 280 °C and 4 h (g and g′).</p>
Full article ">Scheme 1
<p>Plausible decomposition pathway for methanol-soluble Kraft lignin in supercritical methanol.</p>
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22 pages, 7473 KiB  
Article
Pore Water Pressure Generation and Energy Dissipation Characteristics of Sand–Gravel Mixtures Subjected to Cyclic Loading
by Abilash Pokhrel and Gabriele Chiaro
Geotechnics 2024, 4(4), 1282-1303; https://doi.org/10.3390/geotechnics4040065 - 19 Dec 2024
Viewed by 480
Abstract
At least 32 case histories have shown that liquefaction can occur in gravelly soils (both natural deposits and manmade reclamations) during severe earthquakes, causing large ground deformations and severe damage to civil infrastructures. Gravelly soils, however, pose major challenges in geotechnical earthquake engineering [...] Read more.
At least 32 case histories have shown that liquefaction can occur in gravelly soils (both natural deposits and manmade reclamations) during severe earthquakes, causing large ground deformations and severe damage to civil infrastructures. Gravelly soils, however, pose major challenges in geotechnical earthquake engineering in terms of assessing their deformation characteristics and potential for liquefaction. In this study, aimed at providing valuable insights into this important topic, a series of isotropically consolidated undrained cyclic triaxial tests were carried out on selected sand–gravel mixtures (SGMs) with varying degrees of gravel content (Gc) and relative density (Dr). The pore water pressure generation and liquefaction resistance were examined and then further scrutinized using an energy-based method (EBM) for liquefaction assessment. It is shown that the rate of pore water pressure development is influenced by the cyclic resistance ratio (CSR), Gc and Dr of SGMs. However, a unique correlation exists between the pore water pressure ratio and cumulative normalized dissipated energy during liquefaction. Furthermore, the cumulative normalized energy is a promising parameter to describe the cyclic resistance ratio (CRR) of gravelly soils at various post-liquefaction axial strain levels, considering the combined effects of Gc and Dr on the liquefaction resistance. Full article
(This article belongs to the Special Issue Recent Advances in Geotechnical Engineering (2nd Edition))
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<p>Particle size distribution curves of the tested materials.</p>
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<p>Relationships between <span class="html-italic">CSR</span> and the number of loading cycles required to cause initial liquefaction (<span class="html-italic">N</span><sub>L</sub> at <span class="html-italic">r</span><sub>u</sub> ≥ 0.95.) and cyclic failure (<span class="html-italic">N</span><sub>F</sub> at <span class="html-italic">ε<sub>SA</sub></span> = 5%) for SGMs with different gravel content (<span class="html-italic">G</span><sub>c</sub>): (<b>a</b>) 0%, (<b>b</b>) 10%, (<b>c</b>) 25%, and (<b>d</b>) 45%.</p>
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<p>Typical undrained cyclic response of loose SGMs with gravel content of 0, 10, 25 and 40% (<span class="html-italic">D</span><sub>r</sub> = 26–30%; <span class="html-italic">CSR</span> = 0.2).</p>
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<p>Variation in pore water pressure generation for loose SGMs with <span class="html-italic">G</span><sub>C</sub> = 0, 10, 25, 40% [<a href="#B25-geotechnics-04-00065" class="html-bibr">25</a>].</p>
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<p>Variation in pore water pressure generation with normalized number of stress cycles to liquefaction for loose SGMs (<span class="html-italic">D</span><sub>r</sub> = 26–33%) subjected to various <span class="html-italic">CSR</span> conditions: (<b>a</b>) <span class="html-italic">G</span><sub>C</sub> = 0%, (<b>b</b>) <span class="html-italic">G</span><sub>C</sub> = 10%, (<b>c</b>) <span class="html-italic">G</span><sub>C</sub> = 25%, and (<b>d</b>) <span class="html-italic">G</span><sub>C</sub> = 40%.</p>
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<p>Variation in pore water pressure generation with normalized number of stress cycles to liquefaction for medium-dense SGMs (<span class="html-italic">D</span><sub>r</sub> = 47–54%) subjected to various <span class="html-italic">CSR</span> conditions: (<b>a</b>) <span class="html-italic">G</span><sub>C</sub> = 0%, (<b>b</b>) <span class="html-italic">G</span><sub>C</sub> = 10%, (<b>c</b>) <span class="html-italic">G</span><sub>C</sub> = 25%, and (<b>d</b>) <span class="html-italic">G</span><sub>C</sub> = 40%.</p>
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<p>Comparison between pore water pressure generation functions reported in the literature for gravelly soils and SGMs [<a href="#B8-geotechnics-04-00065" class="html-bibr">8</a>,<a href="#B23-geotechnics-04-00065" class="html-bibr">23</a>,<a href="#B25-geotechnics-04-00065" class="html-bibr">25</a>,<a href="#B49-geotechnics-04-00065" class="html-bibr">49</a>], and those obtained in this study for SGMs.</p>
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<p>Typical undrained cyclic response of medium-dense SGM with <span class="html-italic">G</span><sub>C</sub> = 10% subjected to <span class="html-italic">CSR</span> = 0.26: (<b>a</b>) deviator stress–axial strain relationship; (<b>b</b>) pore water pressure ratio variation with <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Σ</mi> <mi>W</mi> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Σ</mi> <mi>W</mi> </mrow> </semantics></math> variation with number of loading cycles; (<b>d</b>) <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Σ</mi> <mi>W</mi> </mrow> </semantics></math>–double amplitude axial strain relationship; and (<b>e</b>) <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Σ</mi> <mi>W</mi> </mrow> </semantics></math>–axial strain relationship.</p>
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<p>Relationships between <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Σ</mi> <mi>W</mi> </mrow> </semantics></math> and the number of loading cycles required to cause initial liquefaction (<span class="html-italic">N</span><sub>L</sub> at <span class="html-italic">r</span><sub>u</sub> ≥ 0.95) and cyclic failure (<span class="html-italic">N</span><sub>F</sub> at <span class="html-italic">ε</span><sub>a</sub> = 5%) for SGMs with different gravel content (<span class="html-italic">G</span><sub>C</sub>): (<b>a</b>) 0%, (<b>b</b>) 10%, (<b>c</b>) 25%, and (<b>d</b>) 40%.</p>
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<p>Relationships between <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Σ</mi> <mi>W</mi> </mrow> </semantics></math> and <span class="html-italic">CSR</span> for SGMs with different gravel content (<span class="html-italic">G</span><sub>C</sub>): (<b>a</b>) 0%, (<b>b</b>) 10%, (<b>c</b>) 25%, and (<b>d</b>) 40% for initial liquefaction (<span class="html-italic">N</span><sub>L</sub> at <span class="html-italic">r</span><sub>u</sub> ≥ 0.95) and cyclic failure (<span class="html-italic">N</span><sub>F</sub> at <span class="html-italic">ε</span><sub>a</sub> = 5%).</p>
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<p>Correlations between <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Σ</mi> <mi>W</mi> </mrow> </semantics></math> and <span class="html-italic">CRR</span> for SGMs [<a href="#B39-geotechnics-04-00065" class="html-bibr">39</a>,<a href="#B40-geotechnics-04-00065" class="html-bibr">40</a>], this study considering various liquefaction criteria: (<b>a</b>) <span class="html-italic">r</span><sub>u</sub> ≥ 95% and <span class="html-italic">ε<sub>SA</sub></span> = 5%; and (<b>b</b>) <span class="html-italic">ε<sub>DA</sub></span> = 1, 3 and 5%.</p>
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<p><math display="inline"><semantics> <mrow> <mi>Σ</mi> <mi>W</mi> </mrow> </semantics></math>−<span class="html-italic">εDA</span> relationships for SGMs with <span class="html-italic">D</span><sub>r</sub> = 26–33% and 47–60% and subjected to various <span class="html-italic">CSR</span>: (<b>a</b>) <span class="html-italic">G</span><sub>C</sub> = 0%; (<b>b</b>) <span class="html-italic">G</span><sub>C</sub> = 10%; (<b>c</b>) <span class="html-italic">G</span><sub>C</sub> = 25%; and (<b>d</b>) <span class="html-italic">G</span><sub>C</sub> = 40%.</p>
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<p>Σ<span class="html-italic">W</span>–<span class="html-italic">r</span><sub>u</sub> relationships for loose SGMs (<span class="html-italic">D</span><sub>r</sub> = 26–33%) subjected to various <span class="html-italic">CSR</span>: (<b>a</b>) <span class="html-italic">G</span><sub>C</sub> = 0%; (<b>b</b>) <span class="html-italic">G</span><sub>C</sub> = 10%; (<b>c</b>) <span class="html-italic">G</span><sub>C</sub> = 25%; and (<b>d</b>) <span class="html-italic">G</span><sub>C</sub> = 40% [<a href="#B38-geotechnics-04-00065" class="html-bibr">38</a>].</p>
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<p>Σ<span class="html-italic">W</span>–<span class="html-italic">r</span><sub>u</sub> relationships for medium-dense SGMs (<span class="html-italic">D</span><sub>r</sub> = 47–54%) subjected to various <span class="html-italic">CSR</span>: (<b>a</b>) <span class="html-italic">G</span><sub>C</sub> = 0%; (<b>b</b>) <span class="html-italic">G</span><sub>C</sub> = 10%; (<b>c</b>) <span class="html-italic">G</span><sub>C</sub> = 25%; and (<b>d</b>) <span class="html-italic">G</span><sub>C</sub> = 40% [<a href="#B38-geotechnics-04-00065" class="html-bibr">38</a>].</p>
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<p>Comparison of Σ<span class="html-italic">W</span>–<span class="html-italic">r</span><sub>u</sub> relationships obtained for all SGMs tested in this study.</p>
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28 pages, 1662 KiB  
Review
Numerical Simulation of Earthquake Impacts on Marine Structures: A Comprehensive Review
by Adel Kabi, Jersson X. Leon-Medina and Francesc Pozo
Buildings 2024, 14(12), 4039; https://doi.org/10.3390/buildings14124039 - 19 Dec 2024
Viewed by 536
Abstract
Marine and underwater structures, such as seawalls, piers, breakwaters, and pipelines, are particularly susceptible to seismic events. These events can directly damage the structures or destabilize their supporting soil through phenomena like liquefaction. This review examines advanced numerical modeling approaches, including CFD, FEM, [...] Read more.
Marine and underwater structures, such as seawalls, piers, breakwaters, and pipelines, are particularly susceptible to seismic events. These events can directly damage the structures or destabilize their supporting soil through phenomena like liquefaction. This review examines advanced numerical modeling approaches, including CFD, FEM, DEM, FVM, and BEM, to assess the impacts of earthquakes on these structures. These methods provide cost-effective and reliable simulations, demonstrating strong alignment with experimental and theoretical data. However, challenges persist in areas such as computational efficiency and algorithmic limitations. Key findings highlight the ability of these models to accurately simulate primary forces during seismic events and secondary effects, such as wave-induced loads. Nonetheless, discrepancies remain, particularly in capturing energy dissipation processes in existing models. Future advancements in computational capabilities and techniques, such as high-resolution DNS for wave–structure interactions and improved near-field seismoacoustic modeling show potential for enhancing simulation accuracy. Furthermore, integrating laboratory and field data into unified frameworks will significantly improve the precision and practicality of these models, offering robust tools for predicting earthquake and wave impacts on marine environments. Full article
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<p>(<b>a</b>) Hull with transverse stiffeners CAD detail, (<b>b</b>) Preparation of mesh and for a hull with transverse stiffeners and (<b>c</b>) result of the vibration mode of the hull transversely stiffened at frequency 11.209 Hz [<a href="#B17-buildings-14-04039" class="html-bibr">17</a>].</p>
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<p>CFD-DEM simulation of particle ejection test. (<b>a</b>) Setup and (<b>b</b>) particle motion trajectory with and without Magnus force [<a href="#B21-buildings-14-04039" class="html-bibr">21</a>].</p>
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<p>Marine current turbine. Wake geometry of IBEM model at different operating conditions. From left to right, TSR = 3, 6, 9. The diameter of the turbine was 700 mm [<a href="#B27-buildings-14-04039" class="html-bibr">27</a>].</p>
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<p>Schematic of numerical wave tank: (<b>a</b>) cross-section and (<b>b</b>) plan view [<a href="#B31-buildings-14-04039" class="html-bibr">31</a>].</p>
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<p>(<b>a</b>) STL files for the bottom geometry and cylinder, and (<b>b</b>) Computational domain with bottom slope and vertical cylinder [<a href="#B31-buildings-14-04039" class="html-bibr">31</a>]. The dimensions correspond to those described in <a href="#buildings-14-04039-f004" class="html-fig">Figure 4</a>.</p>
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<p>Results of waves2Foam simulations in four time steps from 31.10 s, until 31.90 s [<a href="#B31-buildings-14-04039" class="html-bibr">31</a>].</p>
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<p>Computational domain and coordinate system for DNS of wind over steep and breaking waves [<a href="#B36-buildings-14-04039" class="html-bibr">36</a>]. (<b>a</b>) 3D View of the waves, (<b>b</b>) 2D view dash line shows the level <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> and red line indicate the wave.</p>
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<p>Beach profiles at Pont del Petroli. The original beach profile from the design report is indicated by a blue line. In red, the two profiles surveyed by LIM/UPC before and after storm Gloria [<a href="#B39-buildings-14-04039" class="html-bibr">39</a>].</p>
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<p>2D view of a pipe inside the lattice Boltzmann grid points [<a href="#B43-buildings-14-04039" class="html-bibr">43</a>].</p>
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24 pages, 4949 KiB  
Article
Preliminary Assessment of a Hydrogen Farm Including Health and Safety and Capacity Needs
by Esmaeil Alssalehin, Paul Holborn and Pericles Pilidis
Energies 2024, 17(24), 6395; https://doi.org/10.3390/en17246395 - 19 Dec 2024
Viewed by 489
Abstract
The safety engineering design of hydrogen systems and infrastructure, worker education and training, regulatory compliance, and engagement with other stakeholders are significant to the viability and public acceptance of hydrogen farms. The only way to ensure these are accomplished is for the field [...] Read more.
The safety engineering design of hydrogen systems and infrastructure, worker education and training, regulatory compliance, and engagement with other stakeholders are significant to the viability and public acceptance of hydrogen farms. The only way to ensure these are accomplished is for the field of hydrogen safety engineering (HSE) to grow and mature. HSE is described as the application of engineering and scientific principles to protect the environment, property, and human life from the harmful effects of hydrogen-related mishaps and accidents. This paper describes a whole hydrogen farm that produces hydrogen from seawater by alkaline and proton exchange membrane electrolysers, then details how the hydrogen gas will be used: some will be stored for use in a combined-cycle gas turbine, some will be transferred to a liquefaction plant, and the rest will be exported. Moreover, this paper describes the design framework and overview for ensuring hydrogen safety through these processes (production, transport, storage, and utilisation), which include legal requirements for hydrogen safety, safety management systems, and equipment for hydrogen safety. Hydrogen farms are large-scale facilities used to create, store, and distribute hydrogen, which is usually produced by electrolysis using renewable energy sources like wind or solar power. Since hydrogen is a vital energy carrier for industries, transportation, and power generation, these farms are crucial in assisting the global shift to clean energy. A versatile fuel with zero emissions at the point of use, hydrogen is essential for reaching climate objectives and decarbonising industries that are difficult to electrify. Safety is essential in hydrogen farms because hydrogen is extremely flammable, odourless, invisible, and also has a small molecular size, meaning it is prone to leaks, which, if not handled appropriately, might cause fires or explosions. To ensure the safe and dependable functioning of hydrogen production and storage systems, stringent safety procedures are required to safeguard employees, infrastructure, and the surrounding environment from any mishaps. Full article
(This article belongs to the Special Issue Hydrogen Economy in the Global Energy Transition)
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<p>Methodology approach.</p>
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<p>Hydrogen supply chain.</p>
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<p>Schematic of the advanced AWE stack. This work is licensed under the Creative Commons Attribution—No Derivatives License (CC BY-ND 4.0).</p>
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<p>Alkaline electrolyser.</p>
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<p>Schematic of the advanced PEM stack. This work is licensed under the Creative Commons Attribution—No Derivatives License (CC BY-ND 4.0).</p>
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<p>Proton exchange membrane electrolyser.</p>
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<p>Map of Libya, courtesy of Encyclopædia Britannica (Edinburgh, Scotland), Inc., copyright 2002; used with permission.</p>
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<p>The 20 GW alkaline hydrogen farm.</p>
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<p>The 20 GW PEM hydrogen farm.</p>
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21 pages, 408 KiB  
Article
Life Cycle Assessment of Greenhouse Gas Emissions in Hydrogen Production via Water Electrolysis in South Korea
by Kyeong-Mi Kim and Dongwoo Kim
Sustainability 2024, 16(24), 11010; https://doi.org/10.3390/su162411010 - 16 Dec 2024
Viewed by 870
Abstract
This study evaluated the greenhouse gas (GHG) emissions associated with hydrogen production in South Korea (hereafter referred to as Korea) using water electrolysis. Korea aims to advance hydrogen as a clean fuel for transportation and power generation. To support this goal, we employed [...] Read more.
This study evaluated the greenhouse gas (GHG) emissions associated with hydrogen production in South Korea (hereafter referred to as Korea) using water electrolysis. Korea aims to advance hydrogen as a clean fuel for transportation and power generation. To support this goal, we employed a life cycle assessment (LCA) approach to evaluate the emissions across the hydrogen supply chain in a well-to-pump framework, using the Korean clean hydrogen certification tiers. Our assessment covered seven stages, from raw material extraction for power plant construction to hydrogen production, liquefaction, storage, and distribution to refueling stations. Our findings revealed that, among the sixteen power sources evaluated, hydroelectric and onshore wind power exhibited the lowest emissions, qualifying as the Tier 2 category of emissions between 0.11 and 1.00 kgCO2e/kg H2 under a well-to-pump framework and Tier 1 category of emissions below 0.10 kgCO2e/kg H2 under a well-to-gate framework. They were followed by photovoltaics, nuclear energy, and offshore wind, all of which are highly dependent on electrolysis efficiency and construction inputs. Additionally, the study uncovered a significant impact of electrolyzer type on GHG emissions, demonstrating that improvements in electrolyzer efficiency could substantially lower GHG outputs. We further explored the potential of future energy mixes for 2036, 2040, and 2050, as projected by Korea’s energy and environmental authorities, in supporting clean hydrogen production. The results suggested that with progressive decarbonization of the power sector, grid electricity could meet Tier 2 certification for hydrogen production through electrolysis, and potentially reach Tier 1 when considering well-to-gate GHG emissions. Full article
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<p>The system boundary of hydrogen production used in this study.</p>
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<p>GHG emissions according to power sources. The results include well-to-pump (from stages J1 to J7) and well-to-gate (from stages J1 and J2) assessments (kg <math display="inline"><semantics> <msub> <mi>CO</mi> <mn>2</mn> </msub> </semantics></math>e/kg <math display="inline"><semantics> <msub> <mi mathvariant="normal">H</mi> <mn>2</mn> </msub> </semantics></math>).</p>
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19 pages, 8336 KiB  
Article
Analysis of the Differences Between Two Landslides on One Slope in Yongguang Village Based on Physical Models and Groundwater Identification
by Fucun Lu, Kun Liu, Shunhua Xu, Jianyu Zhang and Dingnan Guo
Water 2024, 16(24), 3591; https://doi.org/10.3390/w16243591 - 13 Dec 2024
Viewed by 530
Abstract
In 2013, a Ms 6.6 earthquake occurred at the boundary of Min County and Zhang County, triggering numerous landslides. Notably, two landslides with significantly different sliding characteristics emerged less than 100 m apart in Yongguang Village, Min County. The eastern landslide was characterized [...] Read more.
In 2013, a Ms 6.6 earthquake occurred at the boundary of Min County and Zhang County, triggering numerous landslides. Notably, two landslides with significantly different sliding characteristics emerged less than 100 m apart in Yongguang Village, Min County. The eastern landslide was characterized by instability induced by seismic inertial forces, whereas the western landslide exhibited flow slides triggered by liquefaction in loess. To further analyze the causes of these landslides, this study employed a 1 m depth ground temperature survey to probe the shallow groundwater in the area, aiming to understand the distribution of shallow groundwater. Based on the results from the 1 m depth ground temperature survey, a random forest model was applied to regressively predict the initial groundwater levels. The TRIGRS model was utilized to evaluate the influence of pre-earthquake rainfall conditions on landslide stability, and the pore water pressure outputs from TRIGRS were integrated with the Scoops3D model to analyze landslide stability under seismic effects. The results indicate that the combination of the 1 m depth ground temperature survey with high-density electrical methods and random forest approaches effectively captures the initial groundwater levels across the region. Notably, the heavy rainfall occurring one day prior to the earthquake did not significantly reduce the stability of the landslide in Yongguang Village. Instead, the abundant groundwater in the source area of the western landslide, combined with several months of pre-earthquake rainfall, resulted in elevated groundwater levels that created favorable conditions for its occurrence. While the primary triggering factor for both landslides in Yongguang Village was the earthquake, the distinct topographic and groundwater conditions led to significantly different sliding characteristics under seismic influence at the same slope. Full article
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<p>Topographic and Google Earth imagery of Yongguang Village.</p>
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<p>Flowchart showing the methodology of this study.</p>
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<p>Schematic diagram of 1 m depth ground temperature survey.</p>
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<p>Schematic diagram of cell grid (cited from Li et al. [<a href="#B34-water-16-03591" class="html-bibr">34</a>]).</p>
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<p>Results of 1 m depth ground temperature survey: (<b>a</b>) distribution of temperature measurement points; (<b>b</b>) distribution of 1 m ground temperature and inferred direction of groundwater flow vein in Yongguang Village.</p>
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<p>Results of the high-density electrical method and distribution of measurement lines.</p>
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<p>Initial groundwater level in the study area.</p>
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<p>Prediction accuracy of the random forest (RF) model.</p>
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<p>Illustration of the auxiliary variable used in co-kriging interpolation of soil layer thickness and the resulting interpolation outcomes: (<b>a</b>) schematic diagram illustrating the relative position P<sub>1</sub>, P<sub>2</sub> at a point on the slope surface; (<b>b</b>) soil thickness in the study area.</p>
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<p>Results of the TRIGRS model: (<b>a</b>) dry conditions, (<b>b</b>) actual rainfall conditions, and (<b>c</b>) heavy rainfall conditions.</p>
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<p>Results of the Scoops3D model: (<b>a</b>) conditions without groundwater; (<b>b</b>) conditions with actual rainfall and groundwater.</p>
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<p>Groundwater storage capacity.</p>
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<p>Historical landslide distribution in the study area.</p>
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15 pages, 11963 KiB  
Article
Seabed Liquefaction Risk Assessment Based on Wave Spectrum Characteristics: A Case Study of the Yellow River Subaqueous Delta, China
by Hongan Sun, Jishang Xu, Zhenhuan Tian, Lulu Qiao, Zhixing Luan, Yaxin Zhang, Shaotong Zhang, Xingmin Liu and Guangxue Li
J. Mar. Sci. Eng. 2024, 12(12), 2276; https://doi.org/10.3390/jmse12122276 - 11 Dec 2024
Viewed by 499
Abstract
Seabed liquefaction induced by wave loading poses considerable risks to marine structures and requires careful consideration in marine engineering design and construction. Traditional methods relying on statistical wave parameters for analyzing random waves often underestimate the potential for seabed liquefaction. To address this [...] Read more.
Seabed liquefaction induced by wave loading poses considerable risks to marine structures and requires careful consideration in marine engineering design and construction. Traditional methods relying on statistical wave parameters for analyzing random waves often underestimate the potential for seabed liquefaction. To address this underestimation, the present study employs field observations and numerical simulations to examine wave characteristics and liquefaction distribution across various wave return periods in the Chengdao Sea area of the Yellow River subaqueous delta. The research results indicated that the wave decay phase exhibited a higher liquefaction potential than the growth phase, primarily because of the prevalence of low-frequency swell waves. The China Hydrological Code Spectrum (CHC Spectrum) effectively captured the wave characteristics in the study area, with parameterization grounded in measured data. The poro-elastic wave–sediment interaction model further elucidated the liquefaction distribution under extreme wave conditions, revealing a maximum liquefaction depth exceeding 3 m and prominent liquefaction zones at water depths of 5–15 m. Notably, seabed properties emerged as a critical factor for liquefaction and overshadowed water depth, with non-liquefaction zones occurring at water depths of less than 15 m at high clay content, highlighting the general liquefaction risk of silty seabed. This study enhances understanding of the seabed liquefaction process and offers valuable insights into engineering safety. Full article
(This article belongs to the Section Coastal Engineering)
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<p>Overview of the study area. (<b>a</b>) Location of the study area; (<b>b</b>–<b>d</b>) represent clay content, silt content, and sand content, respectively. The black dots indicate seabed surface sediment sampling stations, the magenta mark represents the wave observation station (CB), and the black lines denote water depth contour.</p>
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<p>Wave conditions in the study area. (<b>a</b>) Time series of significant wave height <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>H</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> (red line) and peak wave period <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>p</mi> </mrow> </msub> </mrow> </semantics></math> (blue line). (<b>b</b>) Relationship between <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>p</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>H</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math>. (<b>c</b>) Relationship between <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>H</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math>. (<b>d</b>) Relationship between <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>p</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>Wave spectrum of (<b>a</b>) wave growth and (<b>b</b>) wave decay processes. Note: The same colors in (<b>a</b>,<b>b</b>) indicate similar wave conditions.</p>
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<p>Relationship between significant wave height <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>H</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> and CHC Spectrum parameters of (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>α</mi> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>P</mi> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>Q</mi> </mrow> </semantics></math>, (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>A</mi> </mrow> </semantics></math>, (<b>e</b>) <math display="inline"><semantics> <mrow> <mi>B</mi> </mrow> </semantics></math>, and (<b>f</b>) <math display="inline"><semantics> <mrow> <msup> <mrow> <mi>H</mi> </mrow> <mrow> <mi mathvariant="normal">*</mi> </mrow> </msup> </mrow> </semantics></math>. The black line represents the fit line from the original data, and the pink shadow represents the 95% confidence interval. The parameters are grouped according to the range of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>H</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math>, and the average value of the parameters within the group is represented as a blue dot.</p>
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<p>Comparison of the CHC Spectrum estimated by parameters and measured spectrum.</p>
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<p>Calculation of extreme wave conditions at CB station in different return periods through Pearson-III fitting. The scattered blue solid dots are the annual extreme values of FVCOM-simulated <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>H</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> between 2010 and 2020, and the red line is the Pearson-III fitting curve.</p>
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<p>Spatial distribution of significant wave height in the 50-year return period. The black lines in the figure represent the water depth contour.</p>
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<p>Distribution of maximum liquefaction depth on 22 February 2015.</p>
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<p>Liquefaction process of (<b>a</b>) wave growth and (<b>b</b>) wave decay stage. Liquefaction depth (<b>a1</b>,<b>b1</b>) and liquefaction degree resulting from different wave components of the infra-gravity wave band (<b>a2</b>,<b>b2</b>), swell wave band (<b>a3</b>,<b>b3</b>), and wind wave band (<b>a4</b>,<b>b4</b>). Note: the corresponding wave spectrum is shown in <a href="#jmse-12-02276-f003" class="html-fig">Figure 3</a>.</p>
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<p>The relationship between silt content and clay content, and the color indicates the water depth.</p>
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<p>Spatial distribution of liquefaction depth under (<b>a</b>) 2-year, (<b>b</b>) 5-year, (<b>c</b>) 10-year, (<b>d</b>) 20-year, and (<b>e</b>) 50-year return periods. The black lines in the figure show the water depth contour lines.</p>
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