AirCANS: CFD 2D Mesh Optimisation-based Airfoil Classification and Assessment using Neural Networks

TL;DR

This paper introduces AirCANS, a neural network framework that automates the classification and assessment of CFD mesh data for airfoils, aiming to reduce computational time and improve efficiency.

Contribution

The study develops a novel CFD-based neural network framework, AirCANS, tailored for airfoil mesh data, demonstrating neural networks' effectiveness in CFD data analysis.

Findings

CNNs are adaptable to CFD mesh data structures

AirCANS can classify and assess CFD airfoil meshes effectively

Potential to refine meshes and accelerate CFD solutions

Abstract

This study explores the possibilities of automating the loading, classification and assessment of Computational Fluid Dynamics (CFD) mesh data by Convolutional Neural Networks (CNNs). The research aim is finding a feasible way to quickly make classification and assessment on airfoil mesh data. For this purpose, this study designed a new framework named CFD-based airfoil Classification and Assessment Network (AirCANS) for CFD mesh data which including the data loader and improved the CNN structure to achieve our target. In our research, we found that CNNs are fully adaptable as well as understandable to CFD airfoil mesh data structures, which suggests that our hypothesis is successful and that neural networks can be used to have a greater positive impact on the CFD industry, such as it can be used to refine the mesh and accelerate the solution. This could allow CFD to spend much less time.