DEM parameter calibration approach for cohesive ores based on PSO-BP neural network

DEM parameter calibration approach for cohesive ores based on PSO-BP neural network

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Fangping Ye^a^,^b, Yanan Zhang^a, Craig Wheeler^b^,^c, Bin Chen^c, Chao Zhou^a, Lei Nie^a^,^*

Particuology | 2026, 115 : 309 - 320

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Particuology | 2026, 115: 309-320

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DEM parameter calibration approach for cohesive ores based on PSO-BP neural network

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Fangping Ye^a^,^b, Yanan Zhang^a, Craig Wheeler^b^,^c, Bin Chen^c, Chao Zhou^a, Lei Nie^a^,^*

Affiliations

^aKey Lab of Modern Manufacture Quality Engineering, Hubei University of Technology, Wuhan, 430068, China

^bSchool of Engineering, The University of Newcastle, Callaghan, 2308, Australia

^cTUNRA Bulk Solids, The University of Newcastle, Callaghan, 2308, Australia

Published: 2026-08-10 doi: 10.1016/j.partic.2026.06.004

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Abstract

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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., static/rolling 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.

Key words

Cohesive ores / DEM parameter calibration / PSO-BP neural network / Angle of repose / Shear test

Cite this Article

Fangping Ye, Yanan Zhang, Craig Wheeler, Bin Chen, Chao Zhou, Lei Nie. DEM parameter calibration approach for cohesive ores based on PSO-BP neural network[J]. Particuology, 2026 , 115 : 309 -320 . DOI: 10.1016/j.partic.2026.06.004

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Funding

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International Science and Technology Joint Research Project of Hubei, China(2024EHA007)

Appendix

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Year 2026 volume 115 Issue 0

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Article Info

doi: 10.1016/j.partic.2026.06.004

Receive Date：2026-04-16
Online Date：2026-08-20
Published：2026-08-10

Article Data

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History

Received：2026-04-16
Revised：2026-05-26
Accepted：2026-06-03

Funding

International Science and Technology Joint Research Project of Hubei, China(2024EHA007)

Affiliations

^aKey Lab of Modern Manufacture Quality Engineering, Hubei University of Technology, Wuhan, 430068, China

^bSchool of Engineering, The University of Newcastle, Callaghan, 2308, Australia

^cTUNRA Bulk Solids, The University of Newcastle, Callaghan, 2308, Australia

Corresponding:

^* E-mail address: leinie@hbut.edu.cn (L. Nie).

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表12种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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