Machine Learning-Based Optimal Mesh Generation in Computational Fluid Dynamics

TL;DR

This paper introduces a machine learning approach that predicts optimal mesh densities for CFD simulations, significantly reducing computational effort and enabling high-quality mesh generation for diverse geometries.

Contribution

It presents a convolutional neural network trained on classical CFD meshes to accurately predict optimal mesh densities for arbitrary geometries, streamlining the mesh generation process.

Findings

Achieved over 98.7% accuracy in predicting optimal mesh densities.

Validated on 60,000 wind tunnel simulations with a training set of 20,000.

Enables CFD engineers to generate high-quality meshes without complex computations.

Abstract

Computational Fluid Dynamics (CFD) is a major sub-field of engineering. Corresponding flow simulations are typically characterized by heavy computational resource requirements. Often, very fine and complex meshes are required to resolve physical effects in an appropriate manner. Since all CFD algorithms scale at least linearly with the size of the underlying mesh discretization, finding an optimal mesh is key for computational efficiency. One methodology used to find optimal meshes is goal-oriented adaptive mesh refinement. However, this is typically computationally demanding and only available in a limited number of tools. Within this contribution, we adopt a machine learning approach to identify optimal mesh densities. We generate optimized meshes using classical methodologies and propose to train a convolutional network predicting optimal mesh densities given arbitrary geometries. The proposed concept is validated along 2d wind tunnel simulations with more than 60,000 simulations. Using a training set of 20,000 simulations we achieve accuracies of more than 98.7%. Corresponding predictions of optimal meshes can be used as input for any mesh generation and CFD tool. Thus without complex computations, any CFD engineer can start his predictions from a high quality mesh.