Higher-order nonlinear shock wave discriminant based on deep neural network

YANG Hua; CHEN Shusheng; LI Meng; PANG Chao; GAO Zhenghong; XIANG Xinghao

doi:10.7638/kqdlxxb-2022.0082

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Higher-order nonlinear shock wave discriminant based on deep neural network

更新时间：2026-05-28

- Higher-order nonlinear shock wave discriminant based on deep neural network
- Vol. 41, Issue 7, Pages: 56-63(2023)
- 作者机构：
  
  1. 西北工业大学航空学院,西安,710072
  2. 中航工业第一飞机设计研究院,西安,710089
  3. 空气动力学国家重点实验室,绵阳,621000
- 作者简介：
- 基金信息：
- DOI：10.7638/kqdlxxb-2022.0082
  CLC： V211.3
- Published：2023
- 稿件说明：
移动端阅览
杨华, 陈树生, 李猛, 等. 基于深度神经网络的高阶非线性激波判别式[J]. 空气动力学学报, 2023, 41(7): 56−63.

YANG Hua, CHEN Shusheng, LI Meng, et al. Higher-order nonlinear shock wave discriminant based on deep neural network[J]. 2023, 41(7): 56-63.
杨华, 陈树生, 李猛, 等. 基于深度神经网络的高阶非线性激波判别式[J]. 空气动力学学报, 2023, 41(7): 56−63. DOI： 10.7638/kqdlxxb-2022.0082

YANG Hua, CHEN Shusheng, LI Meng, et al. Higher-order nonlinear shock wave discriminant based on deep neural network[J]. 2023, 41(7): 56-63. DOI： 10.7638/kqdlxxb-2022.0082.

摘要

超声速流动数值模拟中准确识别激波位置对求解飞行器气动特性十分关键，传统的人工构造激波识别方法严重依赖于经验参数，在实际应用中存在一定局限性。为了建立更加通用的激波识别方法，采用深度神经网络构建了高阶非线性激波判别式。在生成了训练数据集与测试数据集的基础上，构建深度前馈神经网络并训练该模型使其满足激波识别预测精度，并通过一维Lax、Sod激波问题及二维超声速无黏圆柱绕流算例验证了该判别式的准确性。结果表明，本文构建的高阶非线性激波判别式相较于人工构造的激波识别器能更加精准地判别光滑区域和激波区域，在不同来流马赫数、不同网格分布条件下均能准确识别出激波位置，但激波识别精度依赖于网格分辨率。

Abstract

In the numerical simulation of supersonic flow

accurately identifying shock wave position is essential to solve an aircraft's aerodynamic and thermal characteristics. The traditional artificial shock identification method seriously depends on empirical coefficients

which has some limitations in practical application. A higher-order nonlinear shock wave discriminant is constructed by using a deep neural network to establish a more general shock identification method. Based on the training data set and the test data set

the depth Feedforward Neural Network is constructed

and the model is trained to meet the prediction accuracy of shock wave identification; it is applied to one-dimensional Lax and Sod shock problems and two-dimensional supersonic inviscid circular cylinder to verify the accuracy of the discriminant. The results show that this paper's high-order nonlinear shock discriminant can more accurately distinguish the smooth region and shock region than the artificial shock discriminant. Secondly

the shock discriminant can accurately identify the shock position under different Mach numbers and grid distributions

but the accuracy of shock identification depends on the grid resolution.

关键词

Keywords

references

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