Modeling of the Drag Force in Polydisperse Gas–Solid Flow via an Efficient Supervised Machine Learning Approach

Author:

Li Xin1,Ouyang Jie1,Wang Xiaodong1ORCID,Dou Jingxi1

Affiliation:

1. School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an 710129, China

Abstract

Most granular flow in nature and industrial processing has the property of polydispersity, whereas we are always restricted to using the monodisperse drag force model in simulations since the drag force model with polydispersity is difficult to establish. Ignoring polydispersity often results in obvious deviations between simulation and experimental outcomes. Generally, it is very hard for us to describe the characteristics of polydispersity in drag force by using a function with analytic expression. Recently, the artificial neural network (ANN) model provides us the advantages of estimating these kinds of outcomes with better accuracy. In this work, the ANN is adopted to model the drag force in polydisperse granular flows. In order to construct a reasonable ANN algorithm for modeling the polydisperse drag force, the structures of ANN are elaborately designed. As training for the ANN drag model, a direct numerical simulation method is proposed, based on the lattice Boltzmann method (LBM), to generate the training data, and an adaptive data filtering algorithm, termed as the optimal contribution rate algorithm (OCRA), is introduced to effectively improve the training efficiency and avoid the over-fitting problems. The results support that the polydispersity of the system can be well scaled by the ANN drag model in a relatively wide range of particle concentrations, and the predicted results coincide well with the experimental ones. Moreover, the ANN drag model is not only effective for polydisperse systems, but compatible with monodisperse systems, which is impossible using traditional drag models.

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3