A Framework for Data-Based Turbulent Combustion Closure: A Posteriori Validation

TL;DR

This paper presents a data-driven framework for turbulent combustion closure modeling using experimental measurements, PCA, KDE, and neural networks, validated through RANS simulations of turbulent jet flames.

Contribution

It introduces a novel data-based approach combining PCA, KDE, and neural networks for closure modeling in turbulent combustion, validated with experimental data.

Findings

Good agreement between simulated and experimental temperature profiles.

Accurate prediction of species mass fractions in turbulent flames.

Framework effectively captures complex turbulent combustion dynamics.

Abstract

In this work, we demonstrate a framework for developing closure models in turbulent combustion using experimental multi-scalar measurements. The framework is based on the construction of conditional means and joint scalar PDFs from experimental data based on the parameterization of composition space using principal component analysis (PCA). The resulting principal components (PCs) act as both conditioning variables and transport variables. Their chemical source terms are constructed starting from instantaneous temperature and species measurements using a variant of the pairwise mixing stirred reactor (PMSR) approach. A multi-dimensional kernel density estimation (KDE) approach is used to construct the joint PDFs in PC space. Convolutions of these joint PDFs with conditional means are used to determine the unconditional means for the closure terms: the mean PCs chemical source terms and the density. These means are parameterized in terms of the mean PCs using artificial neural networks (ANN). The framework is demonstrated a posteriori using the data from the Sandia piloted turbulent jet flames D, E and F by performing RANS calculations. The radial profiles of mean and RMS of temperature and measured species mass fractions agree well with the experimental means for these flames.