TY - JOUR
T1 - A priori screening of data-enabled turbulence models.
AU - E. S. Chen, Peng
AU - Bin, Yuanwei
AU - I. A. Yang, Xiang
AU - Shi, Yipeng
AU - Abkar, Mahdi
AU - I. Park, George
PY - 2023/12/21
Y1 - 2023/12/21
N2 - Assessing the compliance of a white-box turbulence model with known turbulent knowledge is straightforward. It enables users to screen conventional turbulence models and identify apparent inadequacies, thereby allowing for a more focused and fruitful validation and verification. However, comparing a black-box machine-learning model to known empirical scalings is not straightforward. Unless one implements and tests the model, it would not be clear if a machine-learning model, trained at finite Reynolds numbers preserves the known high Reynolds number limit. This is inconvenient, particularly because model implementation involves retraining and reinterfacing. This work attempts to address this issue, allowing fast a priori screening of machine-learning models that are based on feed-forward neural networks (FNN). The method leverages the mathematical theorems we present in the paper. These theorems offer estimates of a network's limits even when the exact weights and biases are unknown. For demonstration purposes, we screen existing machine-learning wall models and RANS models for their compliance with the log layer physics and the viscous layer physics in an a priori manner. In addition, the theorems serve as essential guidelines for future machine-learning models.
AB - Assessing the compliance of a white-box turbulence model with known turbulent knowledge is straightforward. It enables users to screen conventional turbulence models and identify apparent inadequacies, thereby allowing for a more focused and fruitful validation and verification. However, comparing a black-box machine-learning model to known empirical scalings is not straightforward. Unless one implements and tests the model, it would not be clear if a machine-learning model, trained at finite Reynolds numbers preserves the known high Reynolds number limit. This is inconvenient, particularly because model implementation involves retraining and reinterfacing. This work attempts to address this issue, allowing fast a priori screening of machine-learning models that are based on feed-forward neural networks (FNN). The method leverages the mathematical theorems we present in the paper. These theorems offer estimates of a network's limits even when the exact weights and biases are unknown. For demonstration purposes, we screen existing machine-learning wall models and RANS models for their compliance with the log layer physics and the viscous layer physics in an a priori manner. In addition, the theorems serve as essential guidelines for future machine-learning models.
UR - http://www.scopus.com/inward/record.url?scp=85180971320&partnerID=8YFLogxK
U2 - 10.1103/PhysRevFluids.8.124606
DO - 10.1103/PhysRevFluids.8.124606
M3 - Journal article
SN - 2469-9918
VL - 8
JO - Physical Review Fluids
JF - Physical Review Fluids
IS - 12
M1 - 124606
ER -