摘要
为了验证离散元方法标定微米级颗粒离散元仿真参数的可行性,选取一种粒径分布在10~400μm的粉体活性染料作为微米级颗粒物质的代表开展参数标定实验。采集粉体活性染料堆积角实验中的料堆图像进行数字图像处理,计算堆积角并以此作为响应值、选择JKR接触模型进行Plackett-Burman实验,寻找影响粉体活性染料流动特性的3个最显著因素;通过2次最陡爬坡实验,确定各因素最优值所在的区间;根据Box-Behnken实验建立并优化堆积角与显著性参数的二阶回归模型,求解显著性参数的最佳组合;开展验证实验,将其结果与物理实验实测值进行对比。结果表明:相对误差为1.19%,表明此参数标定实验是可行的,获取的参数可用于离散元仿真。
In order to verify the feasibility of the discrete element method to calibrate the discrete element simulation parameters of micron sized particles,a powder reactive dye with particle size distribution of 10 ~ 400 μm in the printing and dyeing industry was selected as the representative to carry out the parameter calibration test. Performing digital image processing on the pile image in the test of measuring the accumulation angle of the dye,calculateing its accumulation angle and use this as the response value,selecting"Hertz-Mindlin with JKR"as a contact model,the Plackett-Burman test was performed to find the three most significant factors affecting the flow characteristics of the dye. Two steepest ascent tests were carried out to determine the optimal value range of each factor. Then,according to box Behnken test,the second-order regression model of accumulation angle and saliency parameters was established and optimized,and the best combination of saliency parameters was calculated. Finally,the simulation parameters were used to carry out the verification test,and the results were compared with the measured values of physical experiments. The reults show that relative error of the verification test is 1. 19%,which proves that the parameter calibration test is feasible.
作者
韩伟
王绍宗
张倩
田宇航
HAN Wei;WANG Shaozong;ZHANG Qian;TIAN Yuhang(State Key Laboratory of Advanced Forming Technology and Equipment,China Academy of Machinery Science and Technology,Beijing 100044,China)
出处
《中国粉体技术》
CAS
CSCD
2021年第6期60-69,共10页
China Powder Science and Technology
基金
国家重点研发计划项目,编号:2016YFC0700905-04。
关键词
微米级颗粒
粉体活性染料
参数标定
离散元模拟
堆积角
二阶回归模型
micron sized particles
powder reactive dyes
parameter calibration
discrete element simulation
accumulation angle
second-order regression mode
