A quantitative comparison of phase-averaged models for bubbly, cavitating flows

TL;DR

This paper compares deterministic and stochastic phase-averaged models for simulating dilute cavitation bubble flows, analyzing their computational performance, accuracy, and convergence in a case study involving acoustically excited bubbles.

Contribution

It provides a detailed comparison of two phase-averaged modeling approaches for cavitating flows, including computational costs and guidelines for their application.

Findings

Ensemble- and volume-averaged simulations produce closely matching observables.

Both methods exhibit first-order convergence under grid refinement.

Stochastic closure costs dominate computational expense, influenced by spatial resolution and bubble void fraction.

Abstract

We compare the computational performance of two modeling approaches for the flow of dilute cavitation bubbles in a liquid. The first approach is a deterministic model, for which bubbles are represented in a Lagrangian framework as advected features, each sampled from a distribution of equilibrium bubble sizes. The dynamic coupling to the liquid phase is modeled through local volume averaging. The second approach is stochastic; ensemble-phase averaging is used to derive mixture-averaged equations and field equations for the associated bubble properties are evolved in an Eulerian reference frame. For polydisperse mixtures, the probability density function of the equilibrium bubble radii is discretized and bubble properties are solved for each representative bin. In both cases, the equations are closed by solving Rayleigh-Plesset-like equations for the bubble dynamics as forced by the local or mixture-averaged pressure, respectively. An acoustically excited dilute bubble screen is used as a case study for comparisons. We show that observables of ensemble- and volume-averaged simulations match closely and that their convergence is first order under grid refinement. Guidelines are established for phase-averaged simulations by comparing the computational costs of methods. The primary costs are shown to be associated with stochastic closure; polydisperse ensemble-averaging requires many samples of the underlying PDF and volume-averaging requires repeated, randomized simulations to accurately represent a homogeneous bubble population. The relative sensitivities of these costs to spatial resolution and bubble void fraction are presented.