Bayesian Predictions of Reynolds-Averaged Navier–Stokes Uncertainties Using Maximum a Posteriori Estimates
Article dans une revue avec comité de lecture
Computational fluid dynamics analyses of high-Reynolds-number flows mostly rely on the Reynolds-averaged Navier–Stokes equations. The associated closure models are based on multiple simplifying assumptions and involve numerous empirical closure coefficients, which are calibrated on a set of simple reference flows. Predicting new flows using a single closure model with nominal values for the closure coefficients may lead to biased predictions. Bayesian model-scenario averaging is a statistical technique providing an optimal way to combine the predictions of several competing models calibrated on various sets of data (scenarios). The method allows a stochastic estimate of a quantity of interest in an unmeasured prediction scenario to be obtain by 1) propagating posterior probability distributions of the parameters obtained for multiple calibration scenarios, and 2) computing a weighted posterior predictive distribution. Although step 2 has a negligible computational cost, step 1 requires a large number of samples of the solver. To enable the application of the proposed approach to computationally expensive flow configurations, a modified formulation is used where a maximum posterior probability approximation is used to drastically reduce the computational burden. The predictive capability of the proposed simplified approach is assessed for two-dimensional separated and three-dimensional compressible flows.
Showing items related by title, author, creator and subject.
Estimation of Model Error Using Bayesian Model-Scenario Averaging with Maximum a Posterori-Estimates Ouvrage scientifiqueSCHMELZER, Martin; DWIGHT, Richard P.; EDELING, Wouter Nico; CINNELLA, Paola (Springer International Publishing, 2019-07)
Article dans une revue avec comité de lectureEDELING, 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 ...
Article dans une revue avec comité de lectureEDELING, Wouter Nico; DWIGHT, Richard P.; CINNELLA, Paola (Elsevier, 2016)The Simplex-Stochastic Collocation (SSC) method is a robust tool used to propagate uncertain input distributions through a computer code. However, it becomes prohibitively expensive for problems with dimensions higher than ...
Article dans une revue avec comité de lectureEDELING, Wouter Nico; IACCARINO, Gianluca; CINNELLA, Paola (Springer Verlag (Germany), 2017)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 ...
Data-Driven Deterministic Symbolic Regression of Nonlinear Stress-Strain Relation for RANS Turbulence Modelling Communication avec acteSCHMELZER, Martin; DWIGHT, Richard P.; CINNELLA, Paola (American Institute of Aeronautics and Astronautics, 2018)This work presents developments towards a deterministic symbolic regression method to derive algebraic Reynolds-stress models for the Reynolds-Averaged Navier-Stokes (RANS) equations. The models are written as tensor ...