Deep Neural Network Based Convergence Classification for Computational Fluid Dynamics

A supervised deep learning approach is coupled with heuristic convergence criteria to construct
a classification model for detecting the completion (convergence) of computational fluid dynamics
(CFD) simulations. Heuristic convergence criteria alone are not always sufficient and more complex
decisions are often left to a human analyst. The proposed approach leverages heuristic convergence
criteria as well as two deep neural network (DNN) models, one binary and one multi-class, to improve
the efficiency and consistency of convergence classification across a wide range of flight regimes. The
DNN models presented are each trained on a subset of ascent aerodynamic CFD simulations for
NASA’s Space Launch System and were produced using NASA’s unstructured Navier-Stokes solver
FUN3D. Individual solutions are analyzed intermittently and are classified as sufficiently converged,
further iterations required, or switch from steady Reynolds Averaged Navier-Stokes (RANS) to unsteady
RANS CFD based on the iterative histories of four aerodynamic coefficients. The implemented
classification model is shown to produce solutions that closely correlate to solutions produced by a human analyst. This work lays groundwork for expanding the capabilities of DNNs for automating and
improving more of the CFD process.