• français
    • English
    English
  • Ouvrir une session
Aide
Voir le document 
  •   Accueil de SAM
  • Dynamique des Fluides (DynFluid)
  • Voir le document
  • Accueil de SAM
  • Dynamique des Fluides (DynFluid)
  • Voir le document
JavaScript is disabled for your browser. Some features of this site may not work without it.

Quantification of model uncertainty in RANS simulations: A review

Article dans une revue avec comité de lecture
Auteur
XIAO, Heng
47147 Virginia Tech [Blacksburg]
CINNELLA, Paola
134975 Laboratoire de Dynamique des Fluides [DynFluid]

URI
http://hdl.handle.net/10985/15519
DOI
10.1016/j.paerosci.2018.10.001
Date
2019
Journal
Progress in Aerospace Sciences

Résumé

In computational fluid dynamics simulations of industrial flows, models based on the Reynolds-averaged Navier–Stokes (RANS) equations are expected to play an important role in decades to come. However, model uncertainties are still a major obstacle for the predictive capability of RANS simulations. This review examines both the parametric and structural uncertainties in turbulence models. We review recent literature on data-free (uncertainty propagation) and data-driven (statistical inference) approaches for quantifying and reducing model uncertainties in RANS simulations. Moreover, the fundamentals of uncertainty propagation and Bayesian inference are introduced in the context of RANS model uncertainty quantification. Finally, the literature on uncertainties in scale-resolving simulations is briefly reviewed with particular emphasis on large eddy simulations.

Fichier(s) constituant cette publication

Nom:
DynFluid_PAS_2019_XIAO.pdf
Taille:
15.68Mo
Format:
PDF
Fin d'embargo:
2020-02-01
Voir/Ouvrir

Cette publication figure dans le(s) laboratoire(s) suivant(s)

  • Dynamique des Fluides (DynFluid)

Documents liés

Visualiser des documents liés par titre, auteur, créateur et sujet.

  • Optimization of cavitating flows simulation with data driven approach: from data assimilation to machine learning 
    Communication avec acte
    ZHANG, Xinlei; GOMEZ, Thomas; XIAO, HENG; ccCOUTIER-DELGOSHA, Olivier (ASME Press, 2018)
    This paper investigates the application of data-driven approach to the optimization of cavitating flow simulations. An evaluation of the performance of commonly used RANS models (k-e, k-w and k-w SST) is presented by ...
  • Sensitivity of Supersonic ORC Turbine Injector Designs to Fluctuating Operating Conditions 
    Communication avec acte
    BUFI, Elio Antonio; CINNELLA, Paola; MERLE, Xavier; CINNELLA, Paola (ASME, 2015)
    The design of an efficient organic rankine cycle (ORC) expander needs to take properly into account strong real gas effects that may occur in given ranges of operating conditions, which can also be highly variable. In this ...
  • Convergence of Fourier-based time methods for turbomachinery wake passing problems 
    Article dans une revue avec comité de lecture
    GOMAR, Adrien; BOUVY, Quentin; SICOT, Frédéric; DUFOUR, Guillaume; CINNELLA, Paola; FRANCOIS, Benjamin (Elsevier, 2014)
    The convergence of Fourier-based time methods applied to turbomachinery flows is assessed. The focus is on the harmonic balance method, which is a timedomain Fourier-based approach standing as an efficient alternative to ...
  • Bayesian estimates of parameter variability in the k − ε turbulence model 
    Article dans une revue avec comité de lecture
    EDELING, Wouter Nico; CINNELLA, Paola; DWIGHT, Richard P.; BIJL, H. (Elsevier, 2014)
    In this paper we are concerned with obtaining estimates for the error in Reynolds-Averaged Navier-Stokes (RANS) simulations based on the Launder-Sharma k−ε turbulence closure model, for a limited class of flows. In particular ...
  • Predictive RANS simulations via Bayesian Model-Scenario Averaging 
    Article dans une revue avec comité de lecture
    CINNELLA, Paola (Elsevier, 2014)
    The turbulence closure model is the dominant source of error in most Reynolds-Averaged Navier–Stokes simulations, yet no reliable estimators for this error component currently exist. Here we develop a stochastic, a posteriori ...

Parcourir

Tout SAMLaboratoiresAuteursDates de publicationCampus/InstitutsCe LaboratoireAuteursDates de publicationCampus/Instituts

Lettre Diffuser la Science

Dernière lettreVoir plus

Statistiques de consultation

Publications les plus consultéesStatistiques par paysAuteurs les plus consultés

ÉCOLE NATIONALE SUPERIEURE D'ARTS ET METIERS

  • Contact
  • Mentions légales

ÉCOLE NATIONALE SUPERIEURE D'ARTS ET METIERS

  • Contact
  • Mentions légales