Generalizing progress variable definition in CFD simulation of combustion systems using tabulated chemistry models

Abstract

In the Computational Fluid Dynamics (CFD) simulation of advanced combustion systems, the chemical kinetics must be examined in detail to predict the emissions and performance characteristics accurately. Nevertheless, the combustion simulation with detailed chemical kinetics is complicated because of the number of equations and a broad timescale spectrum. The Flamelet-Generated Manifold (FGM) is one of the examples of tabulation methods that has received much attention in recent years due to its fast and accurate prediction of combustion characteristics. The Progress Variable (PV) definition in FGM and other PV-based tabulated approaches is often selected randomly or depending on the user's experience. When complicated combustion systems are involved, such choices can become extremely difficult. In the current work, a generic approach for formulating a global PV is developed and tested in various operating conditions relevant to combustion engines. The method is based on a genetic algorithm optimization to maximize the monotonicity of PV, ensuring that for each value of PV, the dependent thermophysical properties have unique values. The FGM model's ability to reproduce the detailed kinetics evolution of the essential combustion and emission parameters of a non-premixed diffusion flame in Spray A configuration is evaluated in both one-dimensional counterflow and CFD simulation. It is concluded that with the use of the current approach, important combustion characteristics can be predicted much better compared to non-optimized PV while eliminating the manual selection of PV definition by the user. Since the algorithm needs to be executed before the chemistry tabulation in the pre-processing step, it does not increase the runtime of the FGM simulation. The algorithm only needs a few minutes to be finished on a standard desktop. The improvement in the results and the distribution of the values of important species in the computational domain is examined.