Spatial clustering of microscopic dynamics governs the slip avalanche of sheared granular materials
• Deep learning quantitively links microscopic dynamics to macroscopic response.• Microscopic dynamics shows different spatial patterns at stick and slip stages.• Spatial pattern of microscopic dynamics acts as “fingerprint” of macro response.• The causes for different spatial patterns of microscopi...
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| Published in | International journal of plasticity Vol. 163; p. 103570 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.04.2023
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| Subjects | |
| Online Access | Get full text |
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| Abstract | • Deep learning quantitively links microscopic dynamics to macroscopic response.• Microscopic dynamics shows different spatial patterns at stick and slip stages.• Spatial pattern of microscopic dynamics acts as “fingerprint” of macro response.• The causes for different spatial patterns of microscopic dynamics are proposed.
Establishing quantifiable links between individual-particle dynamics and macroscopic response of granular materials has been a longstanding challenge, with implications in material science, geology and industry. Despite sustained efforts in uncovering generic features in both macroscopic flow and microscopic dynamics, further advance on the subject matter demands quantitative correlations to be established. We propose a 3D convolution neural network (CNN) to quantify the link between microscopic dynamics and macroscopic stress fluctuations, including both stress recharge (stick regime) and stress drop (slip regime). Through the model interpretation, microscopic dynamics is found to demonstrate distinctive spatial patterns in the stick and slip regimes, which root in the result of free volume-induced structural rearrangements and contact network dynamics, respectively. We conclude that the spatial clustering of microscopic dynamics governs the occurrence of slip avalanches and acts as the “fingerprint” of macroscopic stress fluctuation. The data-driven framework developed in this paper can be readily extended to other amorphous solids for building cross-scale relations, paving a new way to understand the complex behavior of amorphous solids. |
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| AbstractList | • Deep learning quantitively links microscopic dynamics to macroscopic response.• Microscopic dynamics shows different spatial patterns at stick and slip stages.• Spatial pattern of microscopic dynamics acts as “fingerprint” of macro response.• The causes for different spatial patterns of microscopic dynamics are proposed.
Establishing quantifiable links between individual-particle dynamics and macroscopic response of granular materials has been a longstanding challenge, with implications in material science, geology and industry. Despite sustained efforts in uncovering generic features in both macroscopic flow and microscopic dynamics, further advance on the subject matter demands quantitative correlations to be established. We propose a 3D convolution neural network (CNN) to quantify the link between microscopic dynamics and macroscopic stress fluctuations, including both stress recharge (stick regime) and stress drop (slip regime). Through the model interpretation, microscopic dynamics is found to demonstrate distinctive spatial patterns in the stick and slip regimes, which root in the result of free volume-induced structural rearrangements and contact network dynamics, respectively. We conclude that the spatial clustering of microscopic dynamics governs the occurrence of slip avalanches and acts as the “fingerprint” of macroscopic stress fluctuation. The data-driven framework developed in this paper can be readily extended to other amorphous solids for building cross-scale relations, paving a new way to understand the complex behavior of amorphous solids. |
| ArticleNumber | 103570 |
| Author | Mei, Jiangzhou Tang, Longwen Gao, Ke Cao, Wanda Ma, Gang Zhou, Wei |
| Author_xml | – sequence: 1 givenname: Jiangzhou orcidid: 0000-0001-9062-8851 surname: Mei fullname: Mei, Jiangzhou organization: State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, PR. China – sequence: 2 givenname: Gang surname: Ma fullname: Ma, Gang email: magang630@whu.edu.cn organization: State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, PR. China – sequence: 3 givenname: Longwen surname: Tang fullname: Tang, Longwen organization: Department of Civil and Environmental Engineering, University of California, Los Angeles, California 90095, USA – sequence: 4 givenname: Ke orcidid: 0000-0002-0908-7056 surname: Gao fullname: Gao, Ke organization: Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen 518055, Guangdong, PR. China – sequence: 5 givenname: Wanda surname: Cao fullname: Cao, Wanda organization: State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, PR. China – sequence: 6 givenname: Wei surname: Zhou fullname: Zhou, Wei organization: State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, PR. China |
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| Title | Spatial clustering of microscopic dynamics governs the slip avalanche of sheared granular materials |
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