Incorporating Prior Knowledge for Quantifying and Reducing Model-Form Uncertainty in RANS Simulations

TL;DR

This paper presents a Bayesian framework that effectively incorporates prior physics knowledge and observational data to quantify and reduce model-form uncertainties in RANS simulations, enhancing decision-making in turbulent flow modeling.

Contribution

It introduces an open-box Bayesian approach that leverages empirical and physics-based priors, improving uncertainty quantification over traditional black-box methods.

Findings

Physics-based priors improve uncertainty estimates with limited data

The framework effectively integrates empirical knowledge from multiple sources

Incorporating prior knowledge enhances model reliability in RANS simulations

Abstract

Simulations based on Reynolds-Averaged Navier--Stokes (RANS) models have been used to support high-consequence decisions related to turbulent flows. Apart from the deterministic model predictions, the decision makers are often equally concerned about the predictions confidence. Among the uncertainties in RANS simulations, the model-form uncertainty is an important or even a dominant source. Therefore, quantifying and reducing the model-form uncertainties in RANS simulations are of critical importance to make risk-informed decisions. Researchers in statistics communities have made efforts on this issue by considering numerical models as black boxes. However, this physics-neutral approach is not a most efficient use of data, and is not practical for most engineering problems. Recently, we proposed an open-box, Bayesian framework for quantifying and reducing model-form uncertainties in RANS simulations by incorporating observation data and physics-prior knowledge. It can incorporate the information from the vast body of existing empirical knowledge with mathematical rigor, which enables a more efficient usage of data. In this work, we examine the merits of incorporating various types of prior knowledge in the uncertainties quantification and reduction in RANS simulations. The result demonstrates that informative physics-based prior plays an important role in improving the quantification of model-form uncertainties, particularly when the observation data are limited. Moreover, it suggests that the proposed Bayesian framework is an effective way to incorporate empirical knowledge from various sources.