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UID:submissions.pasc-conference.org_PASC23_sess143_msa224@linklings.com
SUMMARY:Hierarchical Bayesian Multifidelity Models for Predictions in Turb
 ulent Flows
DESCRIPTION:Minisymposium\n\nSaleh Rezaeiravesh (University of Manchester)
 ; Timofey Mukha (KTH Royal Institute of Technology); and Philipp Schlatter
  (Friedrich-Alexander-Universität Erlangen-Nürnberg, KTH Royal Institute o
 f Technology)\n\nConducting high-fidelity studies in fluid mechanics can b
 e prohibitively expensive, particularly at high Reynolds numbers. Thus, it
  is necessary to develop accurate yet cost-effective models for outer-loop
  problems involving turbulent flows. One way is multifidelity models (MFMs
 ) which aim at accurately predicting quantities of interest (QoIs) and the
 ir stochastic moments by combining the data obtained from different fideli
 ties. When constructing MFMs, a balance is sought between a few expensive 
 (but accurate) simulations and many more inexpensive (but potentially less
  accurate) simulations. In our multifidelity modeling approach, the calibr
 ation parameters as well as the hyperparameters appearing in the Gaussian 
 processes are simultaneously estimated within a Bayesian framework. GP pro
 vides a natural way for incorporating observational uncertainty in the dat
 a. The Bayesian inference is done using a Markov Chain Monte Carlo (MCMC) 
 approach. The efficiency of the HC-MFM is evaluated for various problems i
 nvolving turbulent flows. We first predict the lift coefficient of a wing.
  The angle of attack (AoA) is the design parameter and experiments, detach
 ed-eddy simulations (DES) and 2D RANS are used as data. We will also study
  the periodic hill case to assess the effect of geometry, and provide comp
 arison to more classical co-Krigin approaches.\n\nDomain: Engineering\n\nS
 ession Chair: Philipp Schlatter (Friedrich-Alexander-Universität Erlangen-
 Nürnberg, KTH Royal Institute of Technology)
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