Journal
PROGRESS IN AEROSPACE SCIENCES
Volume 108, Issue -, Pages 1-31Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.paerosci.2018.10.001
Keywords
Model-form uncertainty; Turbulence modeling; Reynolds-averaged Navier-Stokes equations; Bayesian inference; Machine learning
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Funding
- French Agence Nationale de la Recherche (ANR)
- Department of Aerospace and Ocean Engineering at Virginia Tech
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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 BANS simulations. Moreover, the fundamentals of uncertainty propagation and Bayesian inference are introduced in the context of BANS model uncertainty quantification. Finally, the literature on uncertainties in scale-resolving simulations is briefly reviewed with particular emphasis on large eddy simulations.
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