Assessment of deconvolution-based flamelet methods for progress variable rate modeling

TL;DR

This paper introduces a deconvolution-based method to improve progress variable rate modeling in turbulent reacting flows, demonstrating good predictive accuracy without significant bias in flamelet models.

Contribution

It proposes a novel deconvolution approach to model progress variable variance, enhancing flamelet model predictions in Large Eddy Simulations.

Findings

Deconvolution does not cause significant bias in predictions.

The approach yields quantitatively accurate progress variable rates.

Assessment conducted using direct numerical simulation data.

Abstract

A novel approach for modeling the progress variable reaction rate in Large Eddy Simulations of turbulent and reacting flows is proposed. This is done in the context of two popular flamelet models which require the progress variable variance as input. The approach is based on using a recently proposed deconvolution method for modeling the variance. The deconvolution-modeled variance, is used as an input in the flamelet models for modeling the filtered progress variable rate. The assessment of the proposed approach is conducted a priori using direct numerical simulation data of turbulent premixed flames. For the conditions tested in this study, deconvolution does not introduce a significant bias in the flamelet models' predictions, while a quantitatively good prediction of the progress variable rate is obtained for both flamelet models considered.