• English
    • français
  • Help
  •  | 
  • Contact
  •  | 
  • About
  •  | 
  • Login
  • HAL portal
  •  | 
  • Pages Pro
  • EN
  •  / 
  • FR
View Item 
  •   LillOA Home
  • Liste des unités
  • Laboratoire de Mécanique des Fluides de Lille Kampé de Fériet (LMFL) - UMR 9014
  • View Item
  •   LillOA Home
  • Liste des unités
  • Laboratoire de Mécanique des Fluides de Lille Kampé de Fériet (LMFL) - UMR 9014
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Bayesian optimisation of RANS simulation ...
  • BibTeX
  • CSV
  • Excel
  • RIS

Document type :
Article dans une revue scientifique
DOI :
10.1080/14685248.2019.1622016
Title :
Bayesian optimisation of RANS simulation with ensemble-based variational method in convergent-divergent channel
Author(s) :
Zhang, Xinlei [Auteur]
ONERA [Lille]
Laboratoire de Mécanique des Fluides de Lille – Kampé de Fériet - UMR 9014 [LMFL]
Gomez, Thomas [Auteur]
ONERA [Lille]
Laboratoire de Mécanique des Fluides de Lille – Kampé de Fériet - UMR 9014 [LMFL]
Coutier-Delgosha, Olivier [Auteur]
Laboratoire de Mécanique des Fluides de Lille – Kampé de Fériet - UMR 9014 [LMFL]
Virginia Tech [Blacksburg]
Journal title :
Journal of Turbulence
Pages :
214-239
Publisher :
Taylor & Francis
Publication date :
2019-05-27
ISSN :
1468-5248
Keyword(s) :
RANS
Bayesian optimisation
EnVar
Venturi
Bump
Inlet velocity inference
Model correction inference
HAL domain(s) :
Sciences de l'ingénieur [physics]
French abstract :
This paper investigates the applicability of a hybrid data assimilation approach, namely ensemble-based variational method (EnVar), to optimise Reynolds Averaged Navier-Stokes (RANS) simulations in convergent-divergent ...
Show more >
This paper investigates the applicability of a hybrid data assimilation approach, namely ensemble-based variational method (EnVar), to optimise Reynolds Averaged Navier-Stokes (RANS) simulations in convergent-divergent channel from the perspective of Bayesian inference. Concretely, the ensemble-based variational method is applied to infer the inlet velocity and turbulence model corrections by assimilating Direct Numerical Simulation (DNS) results or limited experimental data. The approach is first adopted to infer the inlet velocity profile for the WallTurb Bump and Venturi geometry. The improvement can be achieved near the inlet region for the bump, but for Venturi in light of the view field limited in adverse pressure gradient region, the observation space is not sensitive to the perturbation of inlet condition. In a second step, the model corrections in k − ω SST model are investigated by assimilating the limited sparse experimental data. With the inferred model corrections, the predictions on both velocity and turbulent kinetic energy (TKE) get improved. The results indicate that the ensemble-based variational method is efficient in inferring unknown quantities of both low dimension (D=20) and high dimension (D=2400) with small ensemble size robustly and non-intrusively. This approach could prove very useful for Bayesian inference or optimisation in CFD problems.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
  • Laboratoire de Mécanique des Fluides de Lille Kampé de Fériet (LMFL) - UMR 9014
Source :
Harvested from HAL
Files
Thumbnail
  • https://hal.archives-ouvertes.fr/hal-02166205/document
  • Open access
  • Access the document
Thumbnail
  • https://hal.archives-ouvertes.fr/hal-02166205/document
  • Open access
  • Access the document
Thumbnail
  • https://sam.ensam.eu/bitstream/10985/15477/3/LMFL_JOT_2019_BAYEUL-LAINE.pdf
  • Open access
  • Access the document
Thumbnail
  • https://sam.ensam.eu/bitstream/10985/15477/3/LMFL_JOT_2019_BAYEUL-LAINE.pdf
  • Open access
  • Access the document
Thumbnail
  • https://hal.archives-ouvertes.fr/hal-02166205/document
  • Open access
  • Access the document
Thumbnail
  • https://sam.ensam.eu/bitstream/10985/15477/3/LMFL_JOT_2019_BAYEUL-LAINE.pdf
  • Open access
  • Access the document
Thumbnail
  • https://sam.ensam.eu/bitstream/10985/15477/3/LMFL_JOT_2019_BAYEUL-LAINE.pdf
  • Open access
  • Access the document
Thumbnail
  • https://sam.ensam.eu/bitstream/10985/15477/3/LMFL_JOT_2019_BAYEUL-LAINE.pdf
  • Open access
  • Access the document
Thumbnail
  • https://sam.ensam.eu/bitstream/10985/15477/3/LMFL_JOT_2019_BAYEUL-LAINE.pdf
  • Open access
  • Access the document
Université de Lille

Mentions légales
Université de Lille © 2017