PYCNIC: A Machine Learning Approach for Detecting Convergence of CFD Simulations

Speaker: Joshua Diaz, Science and Technology Corporation, NASA Ames Research Center
February 8, 2024

Presentation

Abstract

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 the foundation for a python module currently under development dubbed Python Classifier for Numerical Iterative Convergence (PYCNIC), which seeks to implement the proposed model for multiple solvers by utilizing NASA’s open-source Computational Aerosciences Productivity & Execution (CAPE) software and provide a machine learning pipeline for users to seamlessly integrate unique ML models.

This work is supported by NASA’s SLS Program.

Biography

Joshua Diaz is a Junior Research Scientist at NASA Ames Research Center in the Computational Aerosciences Branch. He has been a member of the NASA Ames SLS aerodynamics team for close to three years, and his interests are in applied CFD and machine learning applications in CFD. Prior to working at NASA Ames, he worked for two and half years at NAVAIR China Lake's Aeromechanics and Thermal Analysis Branch. He received his B.S. in General Engineering with a concentration in fluid mechanics from California Polytechnic State University, San Luis Obispo and an M.S. in Aerospace Engineering from San Jose State University.

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