Abstract
Accurate discrete element method (DEM) simulation of postharvest goji berry processing equipment is limited by the difficulty of directly measuring microscopic contact parameters. This study proposes an integrated DEM parameter calibration framework for fresh goji berries by combining optimal Latin hypercube sampling (OLHS), a back-propagation (BP) neural network, and a multi-island genetic algorithm (MIGA). Fresh ‘Ningqi No. 7’ goji berries were used as the test material. Physical experiments determined particle density (840.47 kg/m3), elastic modulus (1.1352 × 105 Pa), Poisson’s ratio (0.32), shear modulus (4.3 × 104 Pa), average angle of repose (AoR, 32.87 ± 2.20°), and pile height (Height, 65.57 ± 4.10 mm). Based on optimal Latin hypercube sampling (OLHS), 150 virtual DEM experiments were designed. Six contact parameters, including the coefficients of restitution, static friction, and rolling friction for goji–goji and goji–PVC contacts, were used as inputs, while AoR and Height were selected as macroscopic responses. A BP neural network was trained to establish the nonlinear mapping between microscopic parameters and macroscopic responses, and MIGA was used to optimize the surrogate model globally. The BP model achieved test-set R2 values of 0.9415 and 0.9429, and RMSE values of 1.0633 and 0.8447 for AoR and Height, respectively. MIGA yielded 90 feasible parameter sets, with relative errors between predicted and DEM-simulated values below 5% at all validation points. Physical validation confirmed that the representative solution reproduced the macroscopic piling behavior of goji berries, providing effective DEM parameters for postharvest equipment simulation and optimization.