Data-Free and Data-Driven RANS Predictions with Quantified Uncertainty - Archive ouverte HAL
Article Dans Une Revue Flow, Turbulence and Combustion Année : 2018

Data-Free and Data-Driven RANS Predictions with Quantified Uncertainty

Résumé

For the purpose of estimating the epistemic model-form uncertainty in Reynolds-Averaged Navier-Stokes closures, we propose two transport equations to locally perturb the Reynolds stress tensor of a given baseline eddy-viscosity model. The spatial structure of the perturbations is determined by the proposed transport equations, and thus does not have to be inferred from full-field reference data. Depending on a small number of model parameters and the local flow conditions, a ’return to eddy viscosity’ is described, and the underlying baseline state can be recovered. In order to make predictions with quantified uncertainty, we identify two separate methods, i.e. a data-free and data-driven approach. In the former no reference data is required and computationally inexpensive intervals are computed. When reference data is available, Bayesian inference can be applied to obtained informed distributions of the model parameters and simulation output.
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Dates et versions

hal-02171284 , version 1 (02-07-2019)

Identifiants

Citer

W. N. Edeling, G. Iaccarino, Paola Cinnella. Data-Free and Data-Driven RANS Predictions with Quantified Uncertainty. Flow, Turbulence and Combustion, 2018, 100 (3), pp.593-616. ⟨10.1007/s10494-017-9870-6⟩. ⟨hal-02171284⟩
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