Identification of the Discrete Element Model Parameters for Rock-Like Brittle Materials
An inverse method for parameters identification of discrete element model combined with experiment is proposed. The inverse problem of parameter identification is transmitted to solve an optimization problem by minimizing the distance between the numerical calculations and experiment responses. In t...
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| Veröffentlicht in: | Computer modeling in engineering & sciences 2020-01, Vol.123 (2), p.717-737 |
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| container_title | Computer modeling in engineering & sciences |
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| creator | Chen, Rui Wang, Yong Peng, Ruitao Jiang, Shengqiang Hu, Congfang Zhao, Ziheng |
| description | An inverse method for parameters identification of discrete element model combined with experiment is proposed. The inverse problem of parameter identification is transmitted to solve an optimization problem by minimizing the distance between the numerical calculations and experiment
responses. In this method, the discrete element method is employed as numerical calculator for the forward problem. Then, the orthogonal experiment design with range analysis was used to carry out parameters sensitivity analysis. In addition, to improve the computational efficiency, the approximate
model technique is used to replace the actual computational model. The intergeneration projection genetic algorithm (IP-GA) is employed as the optimization algorithm. Consequently, the parameters of the discrete element model are determined. To verify the effectiveness and accuracy of the
inverse results, the comparisons of shape deviation experiments with discrete element simulations are provided. It indicates that the effective and reliable discrete element model parameters can be quickly obtained through several sets of experimental data. Hence, this inverse method can be
applied more widely to determine the parameters of discrete element model for other materials. |
| doi_str_mv | 10.32604/cmes.2020.07438 |
| format | Article |
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responses. In this method, the discrete element method is employed as numerical calculator for the forward problem. Then, the orthogonal experiment design with range analysis was used to carry out parameters sensitivity analysis. In addition, to improve the computational efficiency, the approximate
model technique is used to replace the actual computational model. The intergeneration projection genetic algorithm (IP-GA) is employed as the optimization algorithm. Consequently, the parameters of the discrete element model are determined. To verify the effectiveness and accuracy of the
inverse results, the comparisons of shape deviation experiments with discrete element simulations are provided. It indicates that the effective and reliable discrete element model parameters can be quickly obtained through several sets of experimental data. Hence, this inverse method can be
applied more widely to determine the parameters of discrete element model for other materials.</description><identifier>ISSN: 1526-1492</identifier><identifier>ISSN: 1526-1506</identifier><identifier>EISSN: 1526-1506</identifier><identifier>DOI: 10.32604/cmes.2020.07438</identifier><language>eng</language><publisher>Henderson: Tech Science Press</publisher><subject>Brittle materials ; Computer simulation ; Design of experiments ; Discrete element method ; Discrete Element Model ; Experiments ; Forward problem ; Genetic algorithms ; Inverse Method ; Inverse problems ; Ip-Ga ; Mathematical models ; Nondestructive testing ; Optimization ; Parameter Determination ; Parameter identification ; Parameter sensitivity ; Rock-Like Materials ; Sensitivity analysis</subject><ispartof>Computer modeling in engineering & sciences, 2020-01, Vol.123 (2), p.717-737</ispartof><rights>2020. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c442t-7f544d4ab81d7579e83aaa2ea6a7550465db630d95c52185745f6ab5688025453</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Chen, Rui</creatorcontrib><creatorcontrib>Wang, Yong</creatorcontrib><creatorcontrib>Peng, Ruitao</creatorcontrib><creatorcontrib>Jiang, Shengqiang</creatorcontrib><creatorcontrib>Hu, Congfang</creatorcontrib><creatorcontrib>Zhao, Ziheng</creatorcontrib><title>Identification of the Discrete Element Model Parameters for Rock-Like Brittle Materials</title><title>Computer modeling in engineering & sciences</title><description>An inverse method for parameters identification of discrete element model combined with experiment is proposed. The inverse problem of parameter identification is transmitted to solve an optimization problem by minimizing the distance between the numerical calculations and experiment
responses. In this method, the discrete element method is employed as numerical calculator for the forward problem. Then, the orthogonal experiment design with range analysis was used to carry out parameters sensitivity analysis. In addition, to improve the computational efficiency, the approximate
model technique is used to replace the actual computational model. The intergeneration projection genetic algorithm (IP-GA) is employed as the optimization algorithm. Consequently, the parameters of the discrete element model are determined. To verify the effectiveness and accuracy of the
inverse results, the comparisons of shape deviation experiments with discrete element simulations are provided. It indicates that the effective and reliable discrete element model parameters can be quickly obtained through several sets of experimental data. Hence, this inverse method can be
applied more widely to determine the parameters of discrete element model for other materials.</description><subject>Brittle materials</subject><subject>Computer simulation</subject><subject>Design of experiments</subject><subject>Discrete element method</subject><subject>Discrete Element Model</subject><subject>Experiments</subject><subject>Forward problem</subject><subject>Genetic algorithms</subject><subject>Inverse Method</subject><subject>Inverse problems</subject><subject>Ip-Ga</subject><subject>Mathematical models</subject><subject>Nondestructive testing</subject><subject>Optimization</subject><subject>Parameter Determination</subject><subject>Parameter identification</subject><subject>Parameter sensitivity</subject><subject>Rock-Like Materials</subject><subject>Sensitivity analysis</subject><issn>1526-1492</issn><issn>1526-1506</issn><issn>1526-1506</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp1kM1P3EAMxUdVkUq33Hscqecsns8kNz4KFGkRCIF6tLzJBAayyXZmtlL713dCqOilvtiy33uWfox9FrBU0oI-bDYuLiVIWEKpVfWO7QsjbSEM2Pd_Z13LD-xjjE8Ayqqq3mffL1s3JN_5hpIfBz52PD06_tXHJrjk-FnvNlnAr8bW9fyGAm3yOkTejYHfjs1zsfLPjp8En1Lv-BXlo6c-fmJ7XW7u4LUv2P352d3pt2J1fXF5erwqGq1lKsrOaN1qWleiLU1Zu0oRkXRkqTQGtDXt2ipoa9MYKSpTatNZWhtbVSCNNmrBvsy52zD-2LmY8GnchSG_RKnqUlplhMoqmFVNGGMMrsNt8BsKv1AAvuDDCR9O-PAFX7ZczxY_PGQA9JbrG0xxixPRCSj-FFINcvIKqIVFoaHG1nW06xMmCvjwG2PWLNjRfxKnuH_-T5X18wAgkUKaV38A71qQvg</recordid><startdate>20200101</startdate><enddate>20200101</enddate><creator>Chen, Rui</creator><creator>Wang, Yong</creator><creator>Peng, Ruitao</creator><creator>Jiang, Shengqiang</creator><creator>Hu, Congfang</creator><creator>Zhao, Ziheng</creator><general>Tech Science Press</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TB</scope><scope>8FD</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>20200101</creationdate><title>Identification of the Discrete Element Model Parameters for Rock-Like Brittle Materials</title><author>Chen, Rui ; 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The inverse problem of parameter identification is transmitted to solve an optimization problem by minimizing the distance between the numerical calculations and experiment
responses. In this method, the discrete element method is employed as numerical calculator for the forward problem. Then, the orthogonal experiment design with range analysis was used to carry out parameters sensitivity analysis. In addition, to improve the computational efficiency, the approximate
model technique is used to replace the actual computational model. The intergeneration projection genetic algorithm (IP-GA) is employed as the optimization algorithm. Consequently, the parameters of the discrete element model are determined. To verify the effectiveness and accuracy of the
inverse results, the comparisons of shape deviation experiments with discrete element simulations are provided. It indicates that the effective and reliable discrete element model parameters can be quickly obtained through several sets of experimental data. Hence, this inverse method can be
applied more widely to determine the parameters of discrete element model for other materials.</abstract><cop>Henderson</cop><pub>Tech Science Press</pub><doi>10.32604/cmes.2020.07438</doi><tpages>21</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Brittle materials Computer simulation Design of experiments Discrete element method Discrete Element Model Experiments Forward problem Genetic algorithms Inverse Method Inverse problems Ip-Ga Mathematical models Nondestructive testing Optimization Parameter Determination Parameter identification Parameter sensitivity Rock-Like Materials Sensitivity analysis |
| title | Identification of the Discrete Element Model Parameters for Rock-Like Brittle Materials |
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