Validation strategy of reduced-order two-fluid flow models based on a hierarchy of direct numerical simulations

TL;DR

This paper evaluates reduced-order two-fluid flow models against high-fidelity DNS data in an air-assisted water atomization process, highlighting their strengths and limitations for industrial applications.

Contribution

It introduces a validation framework comparing two-fluid models with DNS data for a relevant atomization test case, aiding the development of more accurate reduced-order models.

Findings

Reduced-order models capture averaged quantities well.

Quantitative discrepancies highlight areas for model refinement.

Framework facilitates future model validation against DNS data.

Abstract

Whereas direct numerical simulation (DNS) have reached a high level of description in the field of atomization processes, they are not yet able to cope with industrial needs since they lack resolution and are too costly. Predictive simulations relying on reduced order modeling have become mandatory for applications ranging from cryotechnic to aeronautic combustion chamber liquid injection. Two-fluid models provide a good basis in order to conduct such simulations, even if recent advances allow to refine subscale modeling using geometrical variables in order to reach a unified model including separate phases and disperse phase descriptions based on high order moment methods. The simulation of such models has to rely on dedicated numerical methods and still lacks assessment of its predictive capabilities. The present paper constitutes a building block of the investigation of a hierarchy of test-cases designed to be amenable to DNS while close enough to industrial configurations, for which we propose a comparison of two-fluid compressible simulations with DNS data-bases. We focus in the present contribution on an air-assisted water atomization using a planar liquid sheet injector. Qualitative and quantitative comparisons with incompressible DNS allow us to identify and analyze strength and weaknesses of the reduced-order modeling and numerical approach in this specific configuration and set a framework for more refined models since they already provide a very interesting level of comparison on averaged quantities.