Reynolds Averaged Navier Stokes (RANS) models are the most popular tool for modeling turbulent flow. RANS models require modeling of an unclosed term called the Reynolds stress tensor, but state-of-the-art Reynolds st...
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ISBN:
(数字)9781624105951
ISBN:
(纸本)9781624105951
Reynolds Averaged Navier Stokes (RANS) models are the most popular tool for modeling turbulent flow. RANS models require modeling of an unclosed term called the Reynolds stress tensor, but state-of-the-art Reynolds stress closures such as Linear Eddy Viscosity and Non-Linear Eddy Viscosity models are inadequate for many complex flows of industrial interest, especially those exhibiting flow separation. In this article, we introduce Generalized Non-Linear Eddy Viscosity models as a candidate for Reynolds stress closure. The model form error in the conventional Linear Eddy Viscosity models and Non-Linear Eddy Viscosity models is mitigated by introducing a dependence on additional flow variables, in particular the mean pressure gradient. A frame invariant tensor basis formulation is used to arrive at the general model form. A dense feed-forward neural network is used as a surrogate model for the functional mapping in the model. The proposed model also ensures frame invariance and Galilean invariance in the model form. Numerical results for various flow configurations are presented to demonstrate the effectiveness of data-driven Generalized Non-Linear Eddy Viscosity models. We also discuss challenges to completely couple data-driven models and traditional RANS models.
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