Surrogate Modeling of High-Order Physics-Based Fluid Modeling Tools

A neural network surrogate modeling methodology was used to reproduce a two-dimensional flowfield distribution over a set of NACA airfoils. Once trained and validated, the surrogate model has the potential to generate subsequent CFD quality predictions in 5-6 orders of magnitude less computational effort, in terms of cpu*hours, than traditional methods. A suite of 250 RANS-based Computational Fluid Dynamics (CFD) solutions, for varying NACA airfoil shapes and angles of attacks, was utilized as training data to a machine learning algorithm. The resultant tuned surrogate model was validated, and the differences between the CFD and the surrogate model predictions were compared on a node-by-node basis for mean shift and standard deviation. For these validation cases, the average of these metrics was -7.230e-05 and 1.710e-03, respectively for the Mach number. The surrogate model was then applied to three specific engineering problem classes of interest: (1) initial guess / accelerated convergence of a CFD model, (2) generating derived quantities (pressure envelopes) and (3) optimization of the airfoil geometry toward an objective function. Conclusions and recommendations are reported as to the appropriateness of using the surrogate model towards expediting these problem classes.