1. Reconstruction of numerical inlet boundary conditions using machine learning: Application to the swirling flow inside a conical diffuser
- Author
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Pedro Véras, Claire Ségoufin, Didier Georges, Antoine Bombenger, Guillaume Balarac, Olivier Métais, Laboratoire des Écoulements Géophysiques et Industriels [Grenoble] (LEGI), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Institut Universitaire de France (IUF), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.), GIPSA - Infinite Dimensional Dynamics (GIPSA-INFINITY), GIPSA Pôle Automatique et Diagnostic (GIPSA-PAD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), and GE Renewable Energy
- Subjects
Computational Mechanics ,Machine learning ,computer.software_genre ,01 natural sciences ,010305 fluids & plasmas ,Diffuser (thermodynamics) ,[SPI.MECA.MEFL]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Fluids mechanics [physics.class-ph] ,Physics::Fluid Dynamics ,Flow separation ,0103 physical sciences ,Boundary value problem ,0101 mathematics ,Fluid Flow and Transfer Processes ,Physics ,geography ,geography.geographical_feature_category ,Computer simulation ,business.industry ,Turbulence ,Mechanical Engineering ,Condensed Matter Physics ,Inlet ,010101 applied mathematics ,Mechanics of Materials ,Turbulence kinetic energy ,Artificial intelligence ,business ,Reynolds-averaged Navier–Stokes equations ,computer - Abstract
International audience; A new approach to determine proper mean and fluctuating inlet boundary conditions is proposed. It is based on data driven techniques, i.e., machine learning approach, and its goal is to use any known information about the downstream flow to reconstruct the unknown or incomplete inlet boundary conditions for a numerical simulation. The European Research Community On Flow, Turbulence And Combustion (ERCOFTAC) test case of the swirling flow inside a conical diffuser is investigated. Despite its relatively simple geometry, it constitutes a very challenging test case for numerical simulations due to incomplete experimental data and to the delicate balance between core flow recirculation and boundary layer separation. Simulations are performed using both Reynolds averaged Navier–Stokes (RANS) and large-eddy simulations (LES) turbulence methods. The mean velocity and turbulence kinetic energy profiles obtained with the machine learning approach in RANS are found to be in very good agreement with the experimental measurements and the numerical predictions are greatly improved as compared to the previous results using basic inlet boundary conditions. They are indeed comparable to the best previous RANS using empirical ad hoc inlet conditions to accurately simulate the downstream flow. In LES, in addition to the mean velocity profiles, the machine learning approach also allows us to properly reconstruct the fluctuating part of the turbulent field. In particular, the methodology allows us to circumvent the lack of turbulent correlations associated with classical inlet synthetic turbulence.
- Published
- 2021
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