Turbulence closure modeling with machine learning: a foundational physics perspective

Abstract Turbulence closure modeling using machine learning (ML) is at an early crossroads. The extraordinary success of ML in a variety of challenging fields had given rise to an expectation of similar transformative advances in the area of turbulence closure modeling. However, by most accounts, the current rate of progress toward accurate and predictive ML-RANS (Reynolds Averaged Navier–Stokes) closure models has been very slow. Upon retrospection, the absence of rapid transformative progress can be attributed to two factors: the underestimation of the intricacies of turbulence modeling and the overestimation of ML’s ability to capture all features without employing targeted strategies. To pave the way for more meaningful ML closures tailored to address the nuances of turbulence, this article seeks to review the foundational flow physics to assess the challenges in the context of data-driven approaches. Revisiting analogies with statistical mechanics and stochastic systems, the key physical complexities and mathematical limitations are explicated. It is noted that the current ML approaches do not systematically address the inherent limitations of a statistical approach or the inadequacies of the mathematical forms of closure expressions. The study underscores the drawbacks of supervised learning-based closures and stresses the importance of a more discerning ML modeling framework. As ML methods evolve (which is happening at a rapid pace) and our understanding of the turbulence phenomenon improves, the inferences expressed here should be suitably modified.