Deriving thin-film averaged equations using computer algebra

TL;DR

This paper demonstrates how computer algebra, specifically SymPy in Python, can be used to derive complex thin-film reduced-order models, simplifying the process and enabling efficient numerical analysis of multiscale fluid flows.

Contribution

It introduces a symbolic computation approach to derive thin-film averaged equations, including second-order terms and multi-phase effects, which were previously complex to obtain manually.

Findings

Successfully derived the core-annular WRIBL model using SymPy.

Numerically solved the derived model to analyze Rayleigh-Plateau instability.

Showed that open-source computer algebra simplifies complex model derivations.

Abstract

We demonstrate the use of computer algebra for facilitating the derivation of thin film reduced-order models. We focus on the weighted residual integral boundary layer (WRIBL) method, which has proven to be a very effective technique for developing reduced-order models by averaging the Navier-Stokes equations over the thin-gap direction. In particular, we use SymPy (the symbolic computing library in Python) to derive the core-annular WRIBL model of Dietze and Ruyer-Quil (J. Fluid Mech. 762, 60, 2015); the derivation is especially involved due to the inclusion of second-order terms, the presence of two hydrodynamically active phases, the enforcement of interfacial boundary conditions, and the cylindrical geometry. We show, using excerpts of code, how each step of the derivation can be broken down into substeps that are amenable to symbolic computation. To illustrate the application of the derived model, we solve it numerically using scientific computing libraries in Python, and briefly explore the dynamics of the Rayleigh-Plateau instability. The use of open-source computer algebra, in the manner described here, greatly eases the derivation of averaged models, thereby facilitating their use for the study of multiscale flows, as well as for computationally-efficient prediction and optimization.