Data-Driven Constitutive Relation Reveals Scaling Law for Hydrodynamic Transport Coefficients

TL;DR

This paper demonstrates that data-driven models for hydrodynamic transport can be understood as scaling laws, providing a physically justified approach that improves predictions over traditional methods.

Contribution

The paper reveals the equivalence between data-driven constitutive models and non-linear scaling laws, proposing a new model based on this insight for better hydrodynamic predictions.

Findings

Data-driven models are equivalent to non-linear scaling laws.

The scaling law-based model outperforms Chapman-Enskog and moment methods.

Modeling scaling laws simplifies data-driven approaches and enhances physical plausibility.

Abstract

Finding extended hydrodynamics equations valid from the dense gas region to the rarefied gas region remains a great challenge. The key to success is to obtain accurate constitutive relations for stress and heat flux. Data-driven models offer a new phenomenological approach to learning constitutive relations from data. Such models enable complex constitutive relations that extend Newton's law of viscosity and Fourier's law of heat conduction by regression on higher derivatives. However, the choices of derivatives in these models are ad-hoc without a clear physical explanation. We investigated data-driven models theoretically on a linear system. We argue that these models are equivalent to non-linear length scale scaling laws of transport coefficients. The equivalence to scaling laws justified the physical plausibility and revealed the limitation of data-driven models. Our argument also points out that modeling the scaling law could avoid practical difficulties in data-driven models like derivative estimation and variable selection on noisy data. We further proposed a constitutive relation model based on scaling law and tested it on the calculation of Rayleigh scattering spectra. The result shows our data-driven model has a clear advantage over the Chapman-Enskog expansion and moment methods.