非线性多元颗粒堆积模型及陶瓷颗粒空隙率预测研究  

Study on Nonlinear Multi-particle Stacking Model and Porosity Prediction of Ceramic Particles

作　　者：董健 陆晓峰[1] 朱晓磊[1] 陈成[1] DONG Jian;LU Xiao-feng;ZHU Xiao-lei;CHEN Cheng(College of Mechanical and Power Engineering,Nanjing Tech University,Nanjing 211816,China)

机构地区：[1]南京工业大学机械与动力工程学院,南京211816

出　　处：《当代化工》2022年第7期1520-1526,共7页Contemporary Chemical Industry

基　　金：国家自然科学基金面上项目(项目编号:11772147)。

摘　　要：陶瓷颗粒涂层具有优异的耐腐蚀、耐磨损的优点,是石化设备理想涂层材料,涂层质量受到颗粒级配影响较大。目前除了直接测量外,很少有能直接预测不同级配颗粒空隙率的方法。针对颗粒分布与空隙率的非线性特征,推导二元、三元颗粒堆积模型,并将该方法推广到多元颗粒堆积模型,在此基础上,应用遗传算法对模型参数进行反演,并将计算结果与实验结果进行了对比。结果表明:非线性颗粒堆积模型可以预测不同形状颗粒空隙率;通过遗传算法可以预测不同配比下的颗粒混合物的空隙率,取得良好效果,具有较好的工程应用价值。Ceramic particle coating has the advantages of excellent corrosion resistance and wear resistance. It is an ideal coating material for petrochemical equipment. The coating quality is greatly affected by particle grading. At present, there are few direct methods to predict the porosity of different gradation particles except direct measurement.In this paper, based on the nonlinear characteristics of particle distribution and porosity, the binary and ternary particle accumulation models were established, and the method was extended to the multi-component particle accumulation model. Genetic algorithm was used to invert the calculation results of model parameters and compare them with the experimental results. The results showed that, the nonlinear particle accumulation model can predict the porosity of different shapes of particles. The porosity of particle mixtures with different proportions can be predicted by genetic algorithm, which has a good effect and good engineering application value.

关 键 词：颗粒堆积模型 空隙率 遗传算法 陶瓷颗粒 

分 类 号：TQ577.77[化学工程—精细化工]