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© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by-nc-nd/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

In this paper, we investigate the feasibility of using DNS data and machine learning algorithms to assist RANS turbulence model development. High-fidelity DNS data are generated with the incompressible Navier–Stokes solver implemented in the spectral/hp element software framework Nektar++. Two test cases are considered: a turbulent channel flow and a stationary serpentine passage, representative of internal turbo-machinery cooling flow. The Python framework TensorFlow is chosen to train neural networks in order to address the known limitations of the Boussinesq approximation and a clustering based on flow features is run upfront to enable training on selected areas. The resulting models are implemented in the Rolls-Royce solver HYDRA and a posteriori predictions of velocity field and wall shear stress are compared to baseline RANS. The paper presents the fundamental elements of procedure applied, including a brief description of the tools and methods and improvements achieved.

Details

Title
A Machine Learning Approach to Improve Turbulence Modelling from DNS Data Using Neural Networks
Author
Yuri Frey Marioni 1   VIAFID ORCID Logo  ; Enrique Alvarez de Toledo Ortiz 2 ; Cassinelli, Andrea 2   VIAFID ORCID Logo  ; Montomoli, Francesco 2 ; Adami, Paolo 3 ; Vazquez, Raul 4 

 Aeronautical Engineering Department, Imperial College London, London SW7 2AZ, UK; [email protected] (E.A.d.T.O.); [email protected] (A.C.); [email protected] (F.M.); Rolls-Royce Plc, Derby DE24 8ZF, UK; [email protected] 
 Aeronautical Engineering Department, Imperial College London, London SW7 2AZ, UK; [email protected] (E.A.d.T.O.); [email protected] (A.C.); [email protected] (F.M.) 
 Rolls-Royce Deutschland, 15827 Dahlewitz, Germany; [email protected] 
 Rolls-Royce Plc, Derby DE24 8ZF, UK; [email protected] 
First page
17
Publication year
2021
Publication date
2021
Publisher
MDPI AG
e-ISSN
2504186X
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2551700878
Copyright
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by-nc-nd/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.