Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates

TL;DR

This paper introduces Loop2Net, a deep learning-based system for generating and optimizing CFD meshes of airfoils from sparse boundary data, improving mesh quality through a novel neural network architecture and loss functions.

Contribution

It presents a new deep convolutional neural network architecture, Loop2Net, for data-driven airfoil mesh generation and optimization, incorporating innovative loss functions and penalty mechanisms.

Findings

Effective mesh prediction from sparse boundary coordinates

Improved mesh quality through optimized loss functions

Continuous performance enhancement during training

Abstract

In this study, an innovative intelligent optimization system for mesh quality is proposed, which is based on a deep convolutional neural network architecture, to achieve mesh generation and optimization. The core of the study is the Loop2Net generator and loss function, it predicts the mesh based on the given wing coordinates. And the model's performance is continuously optimised by two key loss functions during the training. Then discipline by adding penalties, the goal of mesh generation was finally reached.