Discrete Element Simulation Parameter Calibration of Wheat Straw Feed Using Response Surface Methodology and Particle Swarm Optimization-Backpropagation Hybrid Algorithm

文献类型： 外文期刊

第一作者： Hu, Zhigao

作者： Hu, Zhigao;Feng, Bin;Ma, Juan;Li, Hao;Kong, Lingzhuo;Tian, Xiang;An, Shiguan;Feng, Bin;Ma, Juan;Shi, Xuming

作者机构：

关键词： wheat straw feedstock; discrete element method; response surface methodology; particle swarm optimization; backpropagation neural network

期刊名称：APPLIED SCIENCES-BASEL （ 影响因子：2.5； 五年影响因子：2.7 ）

ISSN：

年卷期： 2025 年 15 卷 14 期

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收录情况： SCI

摘要： To establish a fundamental property database for discrete elements targeting long-fiber materials and address the issue of response surface methodology (RSM) being prone to local optima in high-dimensional nonlinear optimization, this study conducted parameter calibration experiments and validated the calibrated parameters through a combined approach of simulation and physical testing. The Plackett-Burman design and steepest ascent test were employed to screen significant factors. Using the angle of repose (42.3 degrees) obtained from physical experiments as the response value, response surface methodology (RSM) and a particle swarm optimization-back propagation (PSO-BP) neural network model were independently applied to optimize and compare the critical parameters. The results demonstrated that the dynamic friction coefficient between wheat straw particles, the static friction coefficient between wheat straw and steel plate, and the JKR surface energy were the most influential factors on the simulated angle of repose. The PSO-BP model exhibited superior optimization performance compared to RSM, yielding an optimal parameter combination of 0.17, 0.46, and 0.03. The simulated repose angle under these conditions was 41.67 degrees, exhibiting a relative error of only 1.5% compared to the physical experiment. These findings provide a robust theoretical foundation for discrete element simulations of wheat straw feedstock.

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