Scholarlink

<p>This paper presents the application of a novel AI-based approach, Neural Physics, to produce high-fidelity simulations of train aerodynamics. Neural Physics is built upon convolutional neural networks (CNNs), where the weights are explicitly determined by classical numerical discretisation schemes rather than by training. By leveraging the power of AI technology, this recent approach results in code that can run easily on GPUs and AI processors, achieving high computational speed without sacrificing accuracy. The approach uses an implicit large eddy simulation method based on a non-linear Petrov-Galerkin method to model the unresolved turbulence. Furthermore, for higher-order finite elements, the convolutional finite element method (ConvFEM) is used, which greatly simplifies the implementation of higher-order elements within the NN4DPEs approach. We demonstrate the capability of Neural Physics by simulating a freight Locomotive Class 66 and a partially loaded freight train operating in an open field environment with and without cross wind. This is the first time that ConvFEM has been applied to high-speed fluid flow problems in complex geometries. The results are validated against existing numerical results and experimental measurements, and show good agreement in terms of pressure and velocity distributions around the train body.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Accelerated physics-based simulations of train aerodynamics using machine learning libraries

  • 期刊论文

  • 发表日期：

    2025年12月1日

查看全文
  • Bo-yang Chen,
  • Zhen Liu,
  • Zi-jian Guo,
  • Claire E. Heaney,
  • Christopher C. Pain

摘要

This paper presents the application of a novel AI-based approach, Neural Physics, to produce high-fidelity simulations of train aerodynamics. Neural Physics is built upon convolutional neural networks (CNNs), where the weights are explicitly determined by classical numerical discretisation schemes rather than by training. By leveraging the power of AI technology, this recent approach results in code that can run easily on GPUs and AI processors, achieving high computational speed without sacrificing accuracy. The approach uses an implicit large eddy simulation method based on a non-linear Petrov-Galerkin method to model the unresolved turbulence. Furthermore, for higher-order finite elements, the convolutional finite element method (ConvFEM) is used, which greatly simplifies the implementation of higher-order elements within the NN4DPEs approach. We demonstrate the capability of Neural Physics by simulating a freight Locomotive Class 66 and a partially loaded freight train operating in an open field environment with and without cross wind. This is the first time that ConvFEM has been applied to high-speed fluid flow problems in complex geometries. The results are validated against existing numerical results and experimental measurements, and show good agreement in terms of pressure and velocity distributions around the train body.