Related Data for: Accelerating Fluid Simulations with Graph Convolution Network Predicted Flow Fields

The field of computational fluid dynamics (CFD) is integral to engineering disciplines, particularly for designing systems that operate under complex fluid flow conditions. Accurate simulation of flow fields is essential for optimizing performance across a variety of applications, including aviation, automotive, marine, and renewable energy sectors. Recent advancements in deep learning, particularly graph convolution networks (GCNs), offer promising alternatives for improving simulation processes. This work introduces a novel approach to accelerating fluid simulations using GCNs for flow field initialization. In this dataset repository, over 2000 sets of RANS simulation results of various NACA airfoil shapes and flow conditions are presented to demonstrate that GCN-based initialization significantly reduces computational resources while maintaining high accuracy, achieving a 30%--50% reduction in simulation time compared to the conventional CFD initialization method.