Dr. Tarek Echekki is a Professor at the Department of Mechanical and Aerospace Engineering at North Carolina State University (NC State) since 2002. He received his Ph.D. in Mechanical Engineering from Stanford University in 1993. Subsequently, he held different research positions at the French Petroleum Institute, Sandia National Laboratories and the University of California at Berkeley. Prof. Echekki’s research interests are in combustion theory and turbulent combustion modeling. His most recent focus is on the development of a multiscale and data-based modeling framework to overcome challenges in turbulent combustion closure and to accelerate the simulation of turbulent reacting flows. The multiscale frameworks are based on coupling the coarse large-eddy simulation (LES) approach for momentum transport with the fine-grained, low-dimensional, stochastic model, one-dimensional turbulence (ODT) to capture subgrid physics, including –chemistry and turbulence-radiation interactions. The data-based framework relies on detailed numerical data (e.g. from DNS) or multiscalar point/line experimental measurements to construct RANS/LES models using principal components as conditioning variables.

Prof. Echekki is a Fellow of the American Society of Mechanical Engineers and an Associate Fellow of the American Institute of Aeronautics and Astronautics. He is the co-editor, with Prof. Epaminondas Mastorakos (University of Cambridge), of “Turbulent Combustion Modeling – Advances, New Trends and Perspectives” (Springer, 2011).

Abstract

A New Framework for Experiment-Based Turbulent Combustion Modeling

We present and demonstrate a novel 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 the composition space using principal component analysis (PCA). The resulting principal components (PCs) act as both conditioning and transported 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 and the Sydney piloted flames with inhomogeneous inlets. Strategies to further develop and expand this framework and introduce variants for establishing closure will be presented.