Turbulence Modeling in the Age of Data

TL;DR

This paper reviews recent advances in turbulence modeling that leverage data-driven methods, including physical constraints, statistical inference, and machine learning, to improve accuracy and quantify uncertainties in RANS models.

Contribution

It provides a comprehensive overview of how data and machine learning are integrated with traditional turbulence modeling to enhance predictive capabilities and uncertainty quantification.

Findings

Physical constraints help bound model uncertainties.

Statistical inference improves model coefficient estimation.

Machine learning enhances turbulence model accuracy.

Abstract

Data from experiments and direct simulations of turbulence have historically been used to calibrate simple engineering models such as those based on the Reynolds-averaged Navier--Stokes (RANS) equations. In the past few years, with the availability of large and diverse datasets, researchers have begun to explore methods to systematically inform turbulence models with data, with the goal of quantifying and reducing model uncertainties. This review surveys recent developments in bounding uncertainties in RANS models via physical constraints, in adopting statistical inference to characterize model coefficients and estimate discrepancy, and in using machine learning to improve turbulence models. Key principles, achievements and challenges are discussed. A central perspective advocated in this review is that by exploiting foundational knowledge in turbulence modeling and physical constraints, data-driven approaches can yield useful predictive models.