Ensemble Kalman method for learning turbulence models from indirect observation data

Abstract

In this work, we propose using an ensemble Kalman method to learn a nonlinear eddy viscosity model, represented as a tensor basis neural network, from velocity data. Data-driven turbulence models have emerged as a promising alternative to traditional models for providing closure mapping from the mean velocities to Reynolds stresses. Most data-driven models in this category need full-field Reynolds stress data for training, which not only places stringent demand on the data generation but also makes the trained model ill-conditioned and lacks robustness. This difficulty can be alleviated by incorporating the Reynolds-averaged Navier–Stokes (RANS) solver in the training process. However, this would necessitate developing adjoint solvers of the RANS model, which requires extra effort in code development and maintenance. Given this difficulty, we present an ensemble Kalman method with an adaptive step size to train a neural-network-based turbulence model by using indirect observation data. To our knowledge, this is the first such attempt in turbulence modelling. The ensemble method is first verified on the flow in a square duct, where it correctly learns the underlying turbulence models from velocity data. Then the generalizability of the learned model is evaluated on a family of separated flows over periodic hills. It is demonstrated that the turbulence model learned in one flow can predict flows in similar configurations with varying slopes.

A data‐driven turbulence modeling for the Reynolds stress tensor transport equation

“…The generalizability of ML on different flow types than those used in training was assessed by McConkey et al 34 The RST predictions were considerably less accurate when extending the ML to unseen flow configurations, indicating that a desired universality in ML models may not be feasible at this moment, with the techniques and data available. As a consequence McConkey et al 34 highlights that the ML corrections should remain limited to similar flows with minor differences, this was also observed by Wang et al 30 The RST was also the target of the NN trained by Zhang et al 35 using the ensemble Kalman method with sparse data. The network parameters were updated based on a cost function computed on the discrepancy of the corrected velocity field, rather than on the RST itself.…”