Multi-timescale and correlated dynamic adaptive chemistry modeling of ignition and flame propagation using a real jet fuel surrogate model

Abstract

A new correlated dynamic adaptive chemistry (CO-DAC) method is developed and integrated with the hybrid multi-timescale (HMTS) method for computationally efficient modeling of ignition and unsteady flame propagation of real jet fuel surrogate mixtures with a detailed and comprehensively reduced kinetic mechanism. A concept of correlated dynamic adaptive chemistry (CO-DAC) method in both time and space coordinates is proposed by using a few key phase parameters which govern the low, intermediate, and high temperature chemistry, respectively. Correlated reduced mechanisms in time and space are generated dynamically on the fly from the detailed kinetic mechanism by specifying thresholds of phase parameters of correlation and using the multi-generation path flux analysis (PFA) method. The advantages of the CO-DAC methods are that it not only provides the flexibility and accuracy of kinetic model and chemistry integration but also avoids redundant model reduction in time and space when the chemistry is frequently correlated in phase space. To further increase the computational efficiency in chemistry integration, the hybrid multi-timescale (HMTS) method is integrated with the CO-DAC method to solve the stiff ordinary differential equations (ODEs) of the reduced chemistry generated on the fly by CO-DAC. The present algorithm is compared and validated against the conventional VODE solver, DAC and HMTS/DAC methods for simulating ignition and unsteady flame propagation of real jet fuel surrogate mixtures consisting of four component fuels, n-dodecane, iso-octane, n-propyl benzene, and 1,3,5-trimethyl benzene. The results show the present HMTS/CO-DAC algorithm is not only computationally efficient but also robust and accurate. Moreover, it is shown that compared to the DAC and HMTS/DAC methods, the computation time of model reduction in CO-DAC is almost negligible even for a large kinetic mechanism involving hundreds of species. In addition, the results show that computation efficiency of CO-DAC increases from homogeneous ignition to one-dimensional flame propagation for both the first and second generation PFA reduction. Therefore, the present HMTS/CO-DAC method can enable high-order model reduction and achieve higher computation efficiency for multi-dimensional numerical modeling.