Zhi Wu 2024a - Revision history

https://www.scipedia.com/wd/index.php?action=history&feed=atom&title=Zhi_Wu_2024a Zhi Wu 2024a - Revision history 2026-05-09T01:37:29Z Revision history for this page on the wiki MediaWiki 1.27.0-wmf.10 https://www.scipedia.com/wd/index.php?title=Zhi_Wu_2024a&diff=305762&oldid=prev JSanchez: JSanchez moved page Draft Sanchez Pinedo 622429267 to Zhi Wu 2024a 2024-07-01T12:42:24Z

<p>JSanchez moved page <a href="/public/Draft_Sanchez_Pinedo_622429267" class="mw-redirect" title="Draft Sanchez Pinedo 622429267">Draft Sanchez Pinedo 622429267</a> to <a href="/public/Zhi_Wu_2024a" title="Zhi Wu 2024a">Zhi Wu 2024a</a></p> <table class="diff diff-contentalign-left" data-mw="interface"> <tr style='vertical-align: top;' lang='en'> <td colspan='1' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='1' style="background-color: white; color:black; text-align: center;">Revision as of 12:42, 1 July 2024</td> </tr><tr><td colspan='2' style='text-align: center;' lang='en'><div class="mw-diff-empty">(No difference)</div> </td></tr></table>

JSanchez https://www.scipedia.com/wd/index.php?title=Zhi_Wu_2024a&diff=305761&oldid=prev JSanchez at 12:42, 1 July 2024 2024-07-01T12:42:20Z

<p></p> <table class="diff diff-contentalign-left" data-mw="interface"> <col class='diff-marker' /> <col class='diff-content' /> <col class='diff-marker' /> <col class='diff-content' /> <tr style='vertical-align: top;' lang='en'> <td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 12:42, 1 July 2024</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l3" >Line 3:</td> <td colspan="2" class="diff-lineno">Line 3:</td></tr> <tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr> <tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Granular flow is a phenomenon widely presented in both the natural and engineering fields. Here granular materials could be either solid particles, e.g. rocks, soil, and grains, or liquid particles, e.g. mud and fresh concrete mortar. Soil landslides, particle transport, and grain accumulation have been edge-cutting hot research topics. Discrete Element Method (DEM) has been regarded as one of the most important methods to simulate granular flows and to investigate discontinuous and large deformation problems. The basic principle of DEM was to view the simulated object as consisting of discrete particles, to define specific constitutive relationships for the particles, and to study the macroscopic properties of the simulated object from a microscopic perspective based on the interactions between particles. However, DEM simulations usually consume very high computational cost for particle contact searching and detection. To accelerate the computational process of discrete element simulation, the Graph Neural Network (GNN) based deep learning model was proposed in this paper. In GNNs, graph nodes and graph edges represent the particles and their interactions. The training and testing datasets were generated using an open-source software named YADE, while the neural network model was constructed using PyTorch and Deep Graph Library (DGL). Replacing the direct calculation of particle collisions in DEM with the trained neural network model, the state of the particles at the next moment could be predicted based on the current state of the particles. It significantly increased computational speed. The proposed technique was applied in various examples, such as drum rotation and hopper stacking, and its accuracy had been verified. This study established a solid foundation and provided robust support for further research and applications of granular flow simulation based on GNN</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Granular flow is a phenomenon widely presented in both the natural and engineering fields. Here granular materials could be either solid particles, e.g. rocks, soil, and grains, or liquid particles, e.g. mud and fresh concrete mortar. Soil landslides, particle transport, and grain accumulation have been edge-cutting hot research topics. Discrete Element Method (DEM) has been regarded as one of the most important methods to simulate granular flows and to investigate discontinuous and large deformation problems. The basic principle of DEM was to view the simulated object as consisting of discrete particles, to define specific constitutive relationships for the particles, and to study the macroscopic properties of the simulated object from a microscopic perspective based on the interactions between particles. However, DEM simulations usually consume very high computational cost for particle contact searching and detection. To accelerate the computational process of discrete element simulation, the Graph Neural Network (GNN) based deep learning model was proposed in this paper. In GNNs, graph nodes and graph edges represent the particles and their interactions. The training and testing datasets were generated using an open-source software named YADE, while the neural network model was constructed using PyTorch and Deep Graph Library (DGL). Replacing the direct calculation of particle collisions in DEM with the trained neural network model, the state of the particles at the next moment could be predicted based on the current state of the particles. It significantly increased computational speed. The proposed technique was applied in various examples, such as drum rotation and hopper stacking, and its accuracy had been verified. This study established a solid foundation and provided robust support for further research and applications of granular flow simulation based on GNN</div></td></tr> <tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr> <tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">== Full Paper ==</ins></div></td></tr> <tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"><pdf>Media:Draft_Sanchez Pinedo_622429267123.pdf</pdf></ins></div></td></tr>  </table>

JSanchez https://www.scipedia.com/wd/index.php?title=Zhi_Wu_2024a&diff=305759&oldid=prev JSanchez at 12:42, 1 July 2024 2024-07-01T12:42:18Z

<p></p> <table class="diff diff-contentalign-left" data-mw="interface"> <col class='diff-marker' /> <col class='diff-content' /> <col class='diff-marker' /> <col class='diff-content' /> <tr style='vertical-align: top;' lang='en'> <td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 12:42, 1 July 2024</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1" >Line 1:</td> <td colspan="2" class="diff-lineno">Line 1:</td></tr> <tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">                                </ins></div></td></tr> <tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">==Abstract==</ins></div></td></tr> <tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr> <tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">Granular flow is a phenomenon widely presented in both the natural and engineering fields. Here granular materials could be either solid particles, e.g. rocks, soil, and grains, or liquid particles, e.g. mud and fresh concrete mortar. Soil landslides, particle transport, and grain accumulation have been edge-cutting hot research topics. Discrete Element Method (DEM) has been regarded as one of the most important methods to simulate granular flows and to investigate discontinuous and large deformation problems. The basic principle of DEM was to view the simulated object as consisting of discrete particles, to define specific constitutive relationships for the particles, and to study the macroscopic properties of the simulated object from a microscopic perspective based on the interactions between particles. However, DEM simulations usually consume very high computational cost for particle contact searching and detection. To accelerate the computational process of discrete element simulation, the Graph Neural Network (GNN) based deep learning model was proposed in this paper. In GNNs, graph nodes and graph edges represent the particles and their interactions. The training and testing datasets were generated using an open-source software named YADE, while the neural network model was constructed using PyTorch and Deep Graph Library (DGL). Replacing the direct calculation of particle collisions in DEM with the trained neural network model, the state of the particles at the next moment could be predicted based on the current state of the particles. It significantly increased computational speed. The proposed technique was applied in various examples, such as drum rotation and hopper stacking, and its accuracy had been verified. This study established a solid foundation and provided robust support for further research and applications of granular flow simulation based on GNN</ins></div></td></tr>  </table>

JSanchez https://www.scipedia.com/wd/index.php?title=Zhi_Wu_2024a&diff=305758&oldid=prev JSanchez: Created blank page 2024-07-01T12:42:16Z

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