Validation and parameterization of a novel physics-constrained neural dynamics model applied to turbulent fluid flow

• Published in: Physics of fluids (1994) Vol. 34; no. 11
• Main Authors: Shankar, Varun, Portwood, Gavin D., Mohan, Arvind T., Mitra, Peetak P., Krishnamurthy, Dilip, Rackauckas, Christopher, Wilson, Lucas A., Schmidt, David P., Viswanathan, Venkatasubramanian
• Format: Journal Article
• Language: English
• Published: Melville American Institute of Physics 01.11.2022; American Institute of Physics (AIP)
• Subjects: artificial intelligence; artificial neural networks; CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; Computational fluid dynamics; Constraints; Differential equations; Fluid flow; fluid flows; fluid systems; Incompressibility; Isotropic turbulence; Machine learning; Mathematical models; Navier Stokes equations; numerical methods; Ordinary differential equations; Parameterization; Physics; Quality assessment; Reduced order models; Representations; Reynolds number; theoretical computer science; Time dependence; Turbulent flow; turbulent flows
• ISSN: 1070-6631; 1089-7666
• DOI: 10.1063/5.0122115

In fluid physics, data-driven models to enhance or accelerate time to solution are becoming increasingly popular for many application domains, such as alternatives to turbulence closures, system surrogates, or for new physics discovery. In the context of reduced order models of high-dimensional time-dependent fluid systems, machine learning methods grant the benefit of automated learning from data, but the burden of a model lies on its reduced-order representation of both the fluid state and physical dynamics. In this work, we build a physics-constrained, data-driven reduced order model for Navier–Stokes equations to approximate spatiotemporal fluid dynamics in the canonical case of isotropic turbulence in a triply periodic box. The model design choices mimic numerical and physical constraints by, for example, implicitly enforcing the incompressibility constraint and utilizing continuous neural ordinary differential equations for tracking the evolution of the governing differential equation. We demonstrate this technique on a three-dimensional, moderate Reynolds number turbulent fluid flow. In assessing the statistical quality and characteristics of the machine-learned model through rigorous diagnostic tests, we find that our model is capable of reconstructing the dynamics of the flow over large integral timescales, favoring accuracy at the larger length scales. More significantly, comprehensive diagnostics suggest that physically interpretable model parameters, corresponding to the representations of the fluid state and dynamics, have attributable and quantifiable impact on the quality of the model predictions and computational complexity.