A data-driven approach to viscous fluid mechanics -- the stationary case

TL;DR

This paper proposes a novel data-driven framework for modeling viscous fluid mechanics that bypasses traditional constitutive laws, using experimental data directly within a PDE-based mathematical structure, applicable to various flow regimes.

Contribution

It introduces a data-driven approach to fluid mechanics that constructs solutions without assuming rheological models, extending previous solid-mechanics methods to fluid flows with theoretical guarantees.

Findings

Constructs optimal data-driven solutions free of rheological assumptions.

Establishes $ ext{Gamma}$-convergence for the data-driven fluid functional.

Proves consistency with classical solutions when data form a monotone relation.

Abstract

We introduce a data-driven approach to the modelling and analysis of viscous fluid mechanics. Instead of including constitutive laws for the fluid's viscosity in the mathematical model, we suggest to directly use experimental data. Only a set of differential constraints, derived from first principles, and boundary conditions are kept of the classical PDE model and are combined with a data set. The mathematical framework builds on the recently introduced data-driven approach to solid-mechanics [KO16,CMO18]. We construct optimal data-driven solutions that are material model free in the sense that no assumptions on the rheological behaviour of the fluid are made or extrapolated from the data. The differential constraints of fluid mechanics are recast in the language of constant rank differential operators. Adapting abstract results on lower-semicontinuity and $\mathscr{A}$-quasiconvexity, we show a $\Gamma$-convergence result for the functionals arising in the data-driven fluid mechanical problem. The theory is extended to compact nonlinear perturbations, whence our results apply to both inertialess fluids and flows with finite Reynolds number. Data-driven solutions provide a new relaxed solution concept. We prove that the constructed data-driven solutions are consistent with solutions to the classical PDEs of fluid mechanics if the data sets have the form of a monotone constitutive relation.