基于EDEM的猪粪接触参数标定

为准确快速获得畜禽粪便的接触参数,该研究通过物理堆积试验与仿真方法对猪粪接触参数进行了标定。测定了不同含水率下猪粪的堆积角,建立了含水率与堆积角的回归方程;基于Hertz-Mindlin with JKR球体粘结模型,进行了离散元仿真模拟;采用筛选试验设计(Plackett-BurmanDesign,P-BD)对10个初始参数进行了筛选,发现JKR(Johnso-Kendall-Roberts)表面能、颗粒间滚动摩擦系数、颗粒间碰撞恢复系数对猪粪堆积角影响显著;并根据响应曲面试验设计(Box-BehnkenDesign,B-BD)建立了堆积角与显著性参数的二阶回归模型,得到了3个显著性参数值分别为JKR表面能0.03J/m~2、颗粒间滚动摩擦系数0.27、颗粒间碰撞恢复系数0.54;将仿真所得堆积角与物理试验值进行对比验证,相对误差为4.27%。结果表明,该研究提出的标定方法能准确模拟物理堆积试验,可为畜禽粪便接触参数的标定提供参考。

Calibration of contact parameters for pig manure based on EDEM

To obtain the contact parameters of livestock and poultry manure accurately and quickly, this study calibrated the contact parameters of pig manure by physical stacking test and simulation method. A stacking angle measurement test bench was designed, and the contour of the stacking slope was obtained by the image-digital simulation method, and a linear fitting was performed. Through the natural air-drying and the deionized water adjustment methods, the pig manure accumulation angle under different water content was measured, and a polynomial fitting model between the water content and the pig manure accumulation angle under different water contents was established. To obtain the actual stacking angle, the Hertz-Mindlin with JKR sphere bonding model was used, and the discrete element simulation of the physical stacking test was performed by EDEM2.7 Screening experiment design (Plackett-Burman Design, P-BD) was used to screen 10 initial parameters. It was found that JKR (Johnso-Kendall-Roberts) surface energy, particle-particle rolling friction coefficient, and particle-particle collision recovery coefficient had significant effects on the swine manure accumulation angle, and the other 7 factors had no significant effect on the accumulation angle. The best range of three significant influencing factors was determined by the steepest climbing test. The 7 non-significant influencing factors in this test were the intermediate values of the initial range, and the 3 significant parameters gradually were increased until the relative error between the simulated value and the physical test value reached the minimum. Based on the results of the response surface experiment design (Box-Behnken Design, B-BD), a quadratic polynomial model between the stacking angle and the three significant parameters was created. The analysis of the quadratic polynomial model variance showed that the model was meaningful. Under the condition that the model was significant and the miss-fit terms were not significant, the terms that did not significantly affect the results were removed, and the regression model was optimized to obtain a new quadratic polynomial regression model. The coefficient of variation of the optimized model dropped to 0.81%, indicating that the reliability of the model had been further increased. The determination coefficient R2 =0.994 1 and the correction determination coefficient R2adj=0.986 1 were both close to 1, indicating the model fitted better. The precision (precision) was 37.023, improved before optimization, which could be used to predict the particle accumulation angle. Through the optimization of the optimized quadratic polynomial regression model, the best parameter combination of 3 significant influencing factors were obtained. The results illustrated that the surface energy of JKR was 0.03 J/m2, the coefficient of rolling friction between pig manure and pig manure was 0.27, and the coefficient of recovery of the collision between pig manure and pig manure particles was 0.54. The discrete element stacking test simulation was carried out based on the calibrated optimal values of the discrete element parameters of pig manure, and the error between the simulated stacking angle result and the actual test result was 4.27%, which showed that the calibration results were credible. The results could provide a reference for the selection and calibration of discrete element model parameters to other agricultural livestock and poultry manure.