Reconstruction of numerical inlet boundary conditions using machine learning: Application to the swirling flow inside a conical diffuser-Reference-Cited by-同舟云学术

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Reconstruction of numerical inlet boundary conditions using machine learning: Application to the swirling flow inside a conical diffuser

Published:2021-08 Issue:8 Volume:33 Page:085132
ISSN:1070-6631
Container-title:Physics of Fluids
language:en
Short-container-title:Physics of Fluids

Author:

Véras Pedro¹^ORCID,Balarac Guillaume¹²^ORCID,Métais Olivier¹,Georges Didier³^ORCID,Bombenger Antoine⁴,Ségoufin Claire⁴

Affiliation:

1. Université Grenoble Alpes, CNRS, Grenoble-INP, LEGI, 38000 Grenoble, France

2. Institut Universitaire de France (IUF), 75000 Paris, France

3. Université Grenoble Alpes, CNRS, Grenoble-INP, GIPSA-lab, 38000 Grenoble, France

4. GE Renewable Energy, 82 Avenue Léon Blum, 38100 Grenoble, France

Funder

Hydro'like Industrial Chair

Publisher

AIP Publishing

Subject

Condensed Matter Physics,Fluid Flow and Transfer Processes,Mechanics of Materials,Computational Mechanics,Mechanical Engineering

Link

https://aip.scitation.org/doi/pdf/10.1063/5.0058642

Reference74 articles.

1. A priori and a posteriori tests of inflow conditions for large-eddy simulation

2. Inlet conditions for large eddy simulation: A review

3. Inflow Turbulence Generation Methods

4. Overview of Turbulent Inflow Boundary Conditions for Large-Eddy Simulations

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4. The Influence of Supply Channel Design on the Gas-Dynamic Structure of Air Flow in a Vertical Conical Diffuser;Applied Sciences;2023-11-08

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