Advancing CFD Simulations Through Machine-Learning-Enabled Mesh Refinement Analysis

As computational fluid dynamics (CFD) has become more mainstream in production engineering workflows, new demands have been introduced that require high-quality meshes to accurately capture the complex geometries. This evolution has created the need for mesh generation frameworks that help engineers design optimized meshing structures for each new geometry. However, many simulation workflows rely on the experience and intuition of senior engineers rather than systematic frameworks. In this paper, a novel technique for determining mesh convergence is created using machine learning (ML). This method seeks to provide process engineers with a visual feedback mechanism of flow regions that require mesh refinement. The work was accomplished by creating three grid sensitivity studies on various geometries: zero-pressure-gradient flat plate, bump in channel, and axisymmetric free jet. The cases were then simulated using the Reynolds Averaged Navier-Stokes (RANS) models in OpenFOAM (v2306) and had the ML method applied post-hoc using Python (v3.12.6). To apply the method to each case, the flow field was regionalized and clustered using an unsupervised ML model. The ML clustering results were then converted into a similarity score, which compares two grid levels to inform the user whether the region of the flow had converged. To prove this framework, the similarity scores were compared to flow field probes used to determine mesh convergence at key points in the flow. The method was found to be in agreement with the flow field probes on the level of mesh refinement that created convergence. The approach was also seen to provide refinement region recommendations in regions of the flow that align with human intuition of the physics of the flow.