Convolutional-neural-network-based DES-level aerodynamic flow field generation from URANS data

Abstract

The present paper culminates several investigations into the use of convolutional neural networks (CNNs) as a post-processing step to improve the accuracy of unsteady Reynolds-averaged Navier–Stokes (URANS) simulations for subsonic flows over airfoils at low angles of attack. Time-averaged detached eddy simulation (DES)-generated flow fields serve as the target data for creating and training CNN models. CNN post-processing generates flow-field data comparable to DES resolution, but after using only URANS-level resources and properly training CNN models. This document outlines the underlying theory and progress toward the goal of improving URANS simulations by looking at flow predictions for a class of simple, two-dimensional, streamlined profiles (i.e., an NACA0006 airfoil simulated at Mach 0.3 over an angle-of-attack range of −8° to 8°). After several design iterations, two trained CNN models predict the pressure and density fields immediately surrounding the airfoils, and those results compare well to DES ground truth data. Generally, the trained models match the DES resolution for the model training data. However, to accurately predict the flow conditions outside of the training set, other approaches are necessary. For this portion of the research, all computational fluid dynamic calculations use NASA’s fully-unstructured-Navier–Stokes-3D solver. The rest of the software comes from Python libraries within the public domain to encourage follow-on research. These libraries include TensorFlow for machine learning models and the sequential model-based optimization to generate the algorithm configuration for hyperparameter optimization.