An iterative data-driven turbulence modeling framework based on Reynolds stress representation

Abstract

Data-driven turbulence modeling studies have reached such a stage that the fundamental framework is basically settled, but several essential issues remain that strongly affect the performance, including accuracy, smoothness, and generalization capacity. Two problems are studied in the current research: (1) the processing of the Reynolds stress tensor and (2) the coupling method between the machine learning turbulence model and flow solver. The first determines the form of predicting targets and the resulting physical completeness and interpretability. The second determines the training process and intrinsic relevance between the mean flow features and Reynolds stress. For the Reynolds stress processing issue, we perform the theoretical derivation to extend the relevant tensor arguments of Reynolds stress in addition to the strain rate and rotation rate. Then, the tensor representation theorem is employed to give the complete irreducible invariants and integrity basis. In addition, an adaptive regularization term is employed to enhance the representation performance. For the coupling issue, an iterative coupling data-driven turbulence modeling framework with consistent convergence is proposed. The training data preparation, predicting target selection, and computation platform are illustrated. The framework is then applied to a canonical separated flow for verification. The mean flow results obtained by coupling computation of the trained machine learning model and flow solver have high consistency with the direct numerical simulation true values, which proves the validity of the current approach.

References in corpus (15)

• Scikit-learn: Machine Learning in Python
• 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
• Quantifying and Reducing Model-Form Uncertainties in Reynolds-Averaged Navier-Stokes Simulations: A Data-Driven, Physics-Based Bayesian Approach
• Perspectives on Machine Learning-augmented Reynolds-averaged and Large Eddy Simulation Models of Turbulence
• Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression
• RANS Equations with Explicit Data-Driven Reynolds Stress Closure Can Be Ill-Conditioned
• Physics-guided deep learning framework for predictive modeling of the Reynolds stress anisotropy
• Feature selection and processing of turbulence modeling based on an artificial neural network
• Learning nonlocal constitutive models with neural networks
• An Iterative Machine-Learning Framework for RANS Turbulence Modeling
• Data-driven turbulence modeling in separated flows considering physical mechanism analysis
• Data augmented turbulence modeling for three-dimensional separation flows

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• Revisiting Tensor Basis Neural Networks for Reynolds stress modeling: application to plane channel and square duct flows
• Data-driven approach for modeling Reynolds stress tensor with invariance preservation
• Numerical Simulation of Three-dimensional High-Lift Configurations Using Data-Driven Turbulence Model