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DEM parameter calibration approach for cohesive ores based on PSO-BP neural network 认领 引用
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作者 Fangping Ye Yanan Zhang +3 位作者 Craig Wheeler Bin Chen Chao Zhou Lei Nie 《Particuology》 SCIE EI CAS CSCD 2026年第8期309-320,共12页
To enhance the calibration efficiency and accuracy of Discrete Element Method(DEM)parameters for cohesive bulk materials,a collaborative method integrating Particle Swarm Optimization(PSO)and Backpropagation(BP)neural... To enhance the calibration efficiency and accuracy of Discrete Element Method(DEM)parameters for cohesive bulk materials,a collaborative method integrating Particle Swarm Optimization(PSO)and Backpropagation(BP)neural networks is proposed.Key macroscopic indicators(steady-state shear stress,angle of repose)are obtained via Jenike shear and funnel tests across a 0-50%moisture range.Orthogonal experiments determine micro-parameters(e.g.,staticolling friction,surface energy)to build a macro-micro mapping database.The core of the PSO-BP dual-model lies in its collaborative mechanism:the forward BP model predicts macroscopic responses to replace time-consuming DEM simulations,while the PSO algorithm optimizes the inverse BP model to accurately infer optimal micro-parameters from experimental macro-indicators(steady-state shear stress,angle of repose).Validation shows low errors(1.14%for angle of repose,1.63%for steady-state shear stress)and good chute flow velocity agreement.This method overcomes traditional limitations of arbitrariness and ignored parameter coupling,providing reliable support for DEM simulation and equipment design for cohesive bulk materials. 展开更多
关键词 Cohesive ores DEM parameter calibration PSO-BP neural network Angle of repose Shear test
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