Calibration of parallel bond parameters in bonded particle models via physics-informed adaptive moment optimisation

Qu, Tongming; Feng, Y. T.; Wang, Min; Jiang, Shengqiang

doi:10.1016/j.powtec.2020.02.077

Title: Calibration of parallel bond parameters in bonded particle models via physics-informed adaptive moment optimisation

Journal Article · Mon Mar 02 00:00:00 EST 2020 · Powder Technology

DOI:https://doi.org/10.1016/j.powtec.2020.02.077· OSTI ID:1604018

Qu, Tongming ^[1];

^[1];

^[2];

^[3]

Swansea Univ., Wales (United Kingdom)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Xiangtan Univ., Hunan (China)

This study proposes an automated calibration procedure for bond parameters in bonded discrete element modelling. By exploring the underlying physical correlations between microscopic parameters of bonds and macroscopic strength parameters of the continuum to be modelled, the microscopic shear strength and tensile strength are identified as independent variables for calibration purpose. Then a physics-informed iterative scheme is proposed to automatically approximate the bond parameters by viewing the micro-macro relation as an implicitly defined mathematical mapping function. As a result of highly non-convex features of this implicit mapping, the adaptive moment estimation (Adam), which is especially suitable for problems with noisy gradients, is adopted as the basic iterative scheme, in conjunction with other numerical techniques to approximately evaluate the partial derivatives involved. The whole procedure offers a simple and effective framework for bond parameter calibration. Finally, a numerical example of SiC ceramic is provided for validation. By compared with some existing calibration methods, the proposed method shows significant advantages in terms of calibration efficiency and accuracy.

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Research Organization:: Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 1604018

Report Number(s):: LA-UR-20-22051

Journal Information:: Powder Technology, Vol. 366, Issue C; ISSN 0032-5910

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

Citation Metrics:

Cited by: 21 works

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