Large-eddy simulation of moderately dense evaporating sprays with particle-informed super-resolution

TL;DR

This paper introduces a particle-informed super-resolution method to enhance LES of dense evaporating sprays, improving evaporation rate accuracy and generalization over traditional models.

Contribution

It presents a novel super-resolution approach that reconstructs high-resolution flow fields for better evaporation modeling in dense spray simulations.

Findings

PISR-LES closely replicates CP-DNS evaporation rates.

Significant reduction in fuel mass fraction discrepancies.

Model generalizes well to unseen conditions.

Abstract

In large-eddy simulation (LES) of dense sprays or sprays with pronounced clustering, evaporation rates can be inaccurate when the mesh is too coarse to provide realistic boundary conditions for the widely employed single droplet evaporation model. This is especially relevant to liquid spray combustion in practical applications. Deep learning-based super-resolution (SR) has recently emerged as a promising method for LES subgrid-scale modeling, capable of enhancing flow field resolution. This technique appears well-suited to reconstruct the local gas fields within the inter-droplet space that can be used to correct the evaporation rates. However, it has not yet been applied for this purpose. This paper presents an innovative SR approach $-$ particle-informed super-resolution (PISR) $-$ that approximates high-resolution flow fields for improved evaporation computation. It is validated with a priori, a posteriori and generalization tests on moderately dense sprays. The results show that PISR-LES can closely replicate the evaporation rates computed in a carrier-phase direct numerical simulation (CP-DNS), significantly reducing the discrepancy in the fuel mass fraction field between LES and CP-DNS. Furthermore, the PISR model exhibits robust generalization to cases unseen in training when varying air temperature, droplet diameter, and turbulent Reynolds number.