Efficient prediction of turbulent flow quantities using a Bayesian hierarchical multifidelity model

TL;DR

This paper introduces a Bayesian hierarchical multifidelity modeling approach to efficiently predict turbulent flow quantities, combining data from multiple fidelity levels to improve accuracy and quantify uncertainty, especially when high-fidelity data is scarce.

Contribution

The paper develops a hierarchical multifidelity model that calibrates parameters within a Bayesian framework, enabling optimal combination of low- and high-fidelity data for turbulence prediction.

Findings

Accurate QoI predictions with limited high-fidelity data

Effective uncertainty quantification and confidence intervals

Successful application to complex turbulent flow cases

Abstract

High-fidelity scale-resolving simulations of turbulent flows quickly become prohibitively expensive, especially at high Reynolds numbers. As a remedy, we may use multifidelity models (MFM) to construct predictive models for flow quantities of interest (QoIs), with the purpose of uncertainty quantification, data fusion and optimization. For numerical simulation of turbulence, there is a hierarchy of methodologies ranked by accuracy and cost, which include several numerical/modeling parameters that control the predictive accuracy and robustness of the resulting outputs. Compatible with these specifications, the present hierarchical MFM strategy allows for simultaneous calibration of the fidelity-specific parameters in a Bayesian framework as developed by Goh et al. 2013. The purpose of the multifidelity model is to provide an improved prediction by combining lower and higher fidelity data in an optimal way for any number of fidelity levels; even providing confidence intervals for the resulting QoI. The capabilities of our multifidelity model are first demonstrated on an illustrative toy problem, and it is then applied to three realistic cases relevant to engineering turbulent flows. The latter include the prediction of friction at different Reynolds numbers in turbulent channel flow, the prediction of aerodynamic coefficients for a range of angles of attack of a standard airfoil, and the uncertainty propagation and sensitivity analysis of the separation bubble in the turbulent flow over periodic hills subject to the geometrical uncertainties. In all cases, based on only a few high-fidelity data samples (typically direct numerical simulations, DNS), the multifidelity model leads to accurate predictions of the QoIs accompanied with an estimate of confidence. The result of the UQ and sensitivity analyses are also found to be accurate compared to the ground truth in each case.