Inverse analysis of friction coefficient in discrete element method using powder properties by support vector regression
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- Murata Hidenobu
- Department of Materials Science, Osaka Metropolitan University
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- Yamanoi Mikio
- Prometech Software, Inc.
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- Suzuki Yoshihiro
- Prometech Software, Inc.
抄録
<p>This study demonstrates the inverse prediction of the discrete-element method (DEM) input friction coefficient from the DEM output powder properties such as the outflow rate, aerated bulk density, and repose angle. Support vector (SV) regression can reasonably reproduce the friction coefficient from the DEM output powder properties. Among the powder properties considered in this study, the outflow rate is found to be the most effective for predicting the friction coefficient. Other powder properties also contribute to improving the prediction accuracy. The accuracy of the SV model depends on the number of cases used for the inverse regression of the friction coefficient, and models with practical accuracy can be obtained for 100–200 cases. These results will lead to further use of machine learning in DEM simulations.</p>
収録刊行物
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- Journal of the Ceramic Society of Japan
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Journal of the Ceramic Society of Japan 132 (4), 189-192, 2024-04-01
公益社団法人 日本セラミックス協会
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キーワード
詳細情報 詳細情報について
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- CRID
- 1390581148795062656
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- ISSN
- 13486535
- 18820743
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
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- 抄録ライセンスフラグ
- 使用不可