EAGER: Embedded Deep Neural Nets for Predicting Reynolds Stresses in Complex Flows

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Engineers rely on computational simulation of turbulent flows prior to costly experimental testing to design automobiles, ships, jet engines, wind turbine arrays, and many other flow systems. Realistic turn-around times from concept to solution requires using approximate models to represent the effects of turbulence in the design process. Direct numerical simulations capturing all details of the flow are too expensive for practical full-scale systems, and standard turbulence models are unable to accurately predict the complex, three-dimensional flows pervasive throughout engineering systems. Contemporary machine learning algorithms are creating a paradigm shift in the information that can be gleaned from data and the scale of the data sets that can be efficiently processed. The goal of this project is to improve turbulence models using a data-driven approach which leverages the massive data sets produced by direct numerical simulation of the flows. The fundamental hypothesis of this research is that a machine learning model which accurately predicts the turbulent stresses for a given mean flow field will improve simulation results when implemented in a Reynolds-Averaged Navier-Stokes code. This project will develop improved models for the anisotropy tensor and terms in the turbulent kinetic energy transport equation using deep neural networks. Neural networks will be trained using only direct numerical simulation data and implemented directly in a computational fluid dynamics solver so that the predictions are independent of errors in any baseline model: a substantial advancement over existing discrepancy-based methods. Development of interpretability methods will elevate neural networks from black box tools to trustable models with clearer links between predictions and the underlying flow structures. Techniques for identifying flow regions where the neural net is poorly trained will produce robust machine-learned models that do not degrade computational fluid dynamics predictions below baseline model performance. Our approach also introduces corrective terms into the basic governing equations. Therefore, the lessons learned will provide a framework for future modeling work in any mechanics-based engineering discipline. The models will be tested using experimental data for a number of industrially relevant flows that have caused difficulty for conventional models.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.