Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates
This work addresses mesh quality optimization for computational fluid dynamics simulations in aerospace engineering, representing an incremental improvement.
The study tackled the problem of generating and optimizing airfoil CFD meshes from sparse boundary coordinates using a deep convolutional neural network called Loop2Net, achieving mesh generation and optimization through a proposed intelligent system.
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.