航空科学技术2023，Vol.34Issue(12)：37-42,6.DOI:10.19452/j.issn1007-5453.2023.12.005
基于深度神经网络的含运动边界非定常流场预测方法研究
Research on the Prediction Method of Unsteady Flow Field with Moving Boundary Based on Deep Neural Network
摘要
Abstract
In order to meet the requirement of rapid prediction of fluid-structure interaction system in aircraft design,a data-driven unsteady flow field modeling strategy was explored to shorten the time spent on flow field evolution solution and accelerate the simulation speed of fluid-structure interaction system.The solution of flow field evolution in fluid-structure interaction system is partially equivalent to the evolution of unsteady flow field with moving boundary.This paper proposes a flow field prediction model based on neural networks to learn and predict the evolution of unsteady flow fields with moving boundaries.This neural network can predict the flow field at next timestep based on the current flow field and boundary motion information.The prediction accuracy and generalization ability of the proposed neural network model were tested by the flow around a moving cylinder under different vibration frequencies and amplitudes.The predicted flow fields of the neural network are in accordance with the computational fluid dynamics simulation results.The aerodynamic force obtained by integrating the pressure on the boundary of the predicted flow field data also has a high accuracy.The test results demonstrate that the good predictive performance of the neural network model,so this method can be used to quickly and accurately obtain the unsteady flow field state around the moving boundary.关键词
运动边界/非定常/神经网络/深度学习/快速预测Key words
moving boundary/unsteady/neural network/deep learning/fast prediction引用本文复制引用
韩仁坤,杜焦喜,刘子扬,李立,陈刚..基于深度神经网络的含运动边界非定常流场预测方法研究[J].航空科学技术,2023,34(12):37-42,6.基金项目
航空科学基金(20200014070001) Aeronautical Science Foundation of China(20200014070001) （20200014070001）
