An iterative method to improve the calibration accuracy of flat-joint models: Catch-up penalty algorithm

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Zilong Yang , Yong Hu , Mingxu Xu , Jiyu Tian , Hao Pang , Xiangyang Liu
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引用次数: 0

Abstract

Parameter calibration is a critical step in accurately modeling using the discrete element method (DEM), but the time-consuming and complex calibration process limits the practical utilization of DEM. Herein, a catch-up penalty algorithm was proposed to simultaneously adjust multiple micro parameters of the flat-joint model through iterations. The effect of micro parameters on macro parameters was investigated by conducting 64 sets of orthogonal tests in PFC3D and analyzing the results by ANOVA. Regression analysis was used to establish the preliminary formulas for directly obtaining initial values of micro parameters and the trend equations for deriving iterative formulas. Based on the preliminary and iterative formulas, the calibration process for the algorithm was proposed, in which the micro parameters of each iteration can be calculated, thereby reducing researchers' dependence on the experience. The calibration capability of the algorithm was verified on four types of rocks, and the results showed that the average calibration error between the simulation results and the target values was reduced to within 5 % after six iterations, proving the reliability and applicability of the algorithm.

提高平接模型校准精度的迭代法:追赶惩罚算法
参数校准是利用离散元法(DEM)精确建模的关键步骤,但耗时且复杂的校准过程限制了 DEM 的实际应用。本文提出了一种追赶惩罚算法,通过迭代同时调整平关节模型的多个微观参数。通过在 PFC3D 中进行 64 组正交试验和方差分析,研究了微观参数对宏观参数的影响。利用回归分析建立了用于直接获得微观参数初始值的初步公式和用于推导迭代公式的趋势方程。在初步公式和迭代公式的基础上,提出了该算法的校准过程,在此过程中可以计算出每次迭代的微观参数,从而减少了研究人员对经验的依赖。在四种岩石上验证了该算法的校准能力,结果表明,经过六次迭代,模拟结果与目标值之间的平均校准误差减小到 5 % 以内,证明了该算法的可靠性和适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
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