Inverse analysis of friction coefficient in discrete element method using powder properties by support vector regression

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...

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Veröffentlicht in:	Journal of the Ceramic Society of Japan 2024/04/01, Vol.132(4), pp.189-192
Hauptverfasser:	Murata, Hidenobu, Yamanoi, Mikio, Suzuki, Yoshihiro
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Aerated bulk density Aeration Angle of repose Bulk density Coefficient of friction Discrete element method Friction coefficient Machine learning Outflow Outflow rate Regression Repose angle Support vector machines
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