Deep Structured Neural Networks for Turbulence Closure Modelling

Abstract

Despite well-known limitations of Reynolds-averaged Navier-Stokes (RANS) simulations, this methodology remains the most widely used tool for predicting many turbulent flows, due to computational efficiency. Machine learning is a promising approach to improve the accuracy of RANS simulations. One major area of improvement is using machine learning models to represent the complex relationship between the mean flow field gradients and the Reynolds stress tensor. In the present work, modifications to improve the stability of previous optimal eddy viscosity approaches for RANS simulations are presented and evaluated. The optimal eddy viscosity is reformulated with a non-negativity constraint, which promotes numerical stability. We demonstrate that the new formulation of the optimal eddy viscosity improves the conditioning of the RANS equations for a periodic hills test case. To demonstrate the suitability of this proportional/orthogonal tensor decomposition for use in a physics-informed data-driven turbulence closure, we use two neural networks (structured on this specific tensor decomposition which is incorporated as an inductive bias into the network design) to predict the newly reformulated linear and non-linear parts of the Reynolds stress tensor. Injecting these network model predictions for the Reynolds stresses into a RANS simulation improves predictions of the velocity field, even when compared to a sophisticated (state of the art) physics-based turbulence closure model. Finally, we apply SHAP (SHapley Additive exPlanations) values to obtain insights from the learned representation for the inner workings of the neural network used to predict the optimal eddy viscosity from the input feature data.

arXiv comment18 pages, 23 figures. Article submitted to Physics of Fluids

References in corpus (14)

• Scikit-learn: Machine Learning in Python
• A Unified Approach to Interpreting Model Predictions
• Machine Learning for Fluid Mechanics
• Turbulence Modeling in the Age of Data
• A Physics Informed Machine Learning Approach for Reconstructing Reynolds Stress Modeling Discrepancies Based on DNS Data
• A Comprehensive Physics-Informed Machine Learning Framework for Predictive Turbulence Modeling
• Physics-Informed Machine Learning Approach for Augmenting Turbulence Models: A Comprehensive Framework
• Perspectives on Machine Learning-augmented Reynolds-averaged and Large Eddy Simulation Models of Turbulence
• Physics-guided deep learning framework for predictive modeling of the Reynolds stress anisotropy
• Data-Driven Modelling of the Reynolds Stress Tensor using Random Forests with Invariance
• Feature selection and processing of turbulence modeling based on an artificial neural network
• Neural Network Models for the Anisotropic Reynolds Stress Tensor in Turbulent Channel Flow
• An Iterative Machine-Learning Framework for RANS Turbulence Modeling
• A Data-driven Approach for Turbulence Modeling

Cited by in corpus (5)

• On the generalizability of machine-learning-assisted anisotropy mappings for predictive turbulence modelling
• Assessment of Neural Network Augmented Reynolds Averaged Navier Stokes Turbulence Model in Extrapolation Modes
• A Divide-and-Conquer Machine Learning Approach for Modelling Turbulent Flows
• Progressive augmentation of Reynolds stress tensor models for secondary flow prediction by computational fluid dynamics driven surrogate optimisation
• turbo-RANS: Straightforward and Efficient Bayesian Optimization of Turbulence Model Coefficients