摘要：The prediction of gas diffusion concentration has practical significance. Because the process simulation is based on complex mechanism, it will not be able to be calculated in real time. Moreover, the requirements of computational fluid dynamics on computers limit its application. This paper proposes the computational fluid dynamics simulation surrogate models based on the GA-BP neural network to predict the concentration after aerosol dispersion. Considering the relevant influence parameters of time, space coordinates and concentration, two different models of input and output variables are constructed. The results reveal that when the prediction object is affected by high-dimensional complex factors, the GA-BP neural network can generate accurate prediction results. Compared with the traditional BP neural network, the prediction accuracies can be improved by 40.65% and 77.61%, respectively, which exhibits excellent performance for data prediction. The method proposed in this paper successfully verifies the computational fluid dynamics simulation of the aerosol dispersion processes, and the research has potential application value for environmental safety assessment.
© 2020 IEEE.

卷、期、页：p51-56

发表日期：2020-07-01

收录情况：EI（工程索引）

发表期刊名称：2020 IEEE 6th International Conference on Control Science and Systems Engineering

参与作者：陈韬,殷卫兵

通讯作者：赵羚宇

第一作者：高小永,左信

论文类型：会议论文

论文概要：赵羚宇,高小永,陈韬,殷卫兵,左信，Ga-bp neural network based meta-model method for computational fluid dynamic approximation，2020 IEEE 6th International Conference on Control Science and Systems Engineering，2020，p51-56

论文题目：Ga-bp neural network based meta-model method for computational fluid dynamic approximation