A data-driven approach for the closure of RANS models by the divergence of the Reynolds Stress Tensor

• URL: http://arxiv.org/abs/2203.16944v1
• Date: Thu, 31 Mar 2022 11:08:54 GMT
• Title: A data-driven approach for the closure of RANS models by the divergence of the Reynolds Stress Tensor
• Authors: Stefano Berrone and Davide Oberto
• Abstract summary: A new data-driven model to close and increase accuracy of RANS equations is proposed. The choice is driven by the presence of the divergence of RST in the RANS equations. Once this data-driven approach is trained, there is no need to run any turbulence model to close the equations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the present paper a new data-driven model to close and increase accuracy of RANS equations is proposed. It is based on the direct approximation of the divergence of the Reynolds Stress Tensor (RST) through a Neural Network (NN). This choice is driven by the presence of the divergence of RST in the RANS equations. Furthermore, once this data-driven approach is trained, there is no need to run any turbulence model to close the equations. Finally, it is well known that a good approximation of a function it is not necessarily a good approximation of its derivative. The architecture and inputs choices of the proposed network guarantee both Galilean and coordinates-frame rotation invariances by looking to a vector basis expansion of the divergence of the RST. Two well-known test cases are used to show advantages of the proposed method compared to classic turbulence models.

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