Direct numerical simulation (DNS) has become an important tool to predict and understand complex structures and behaviors of turbulent flames over the last two decades, enabled by the rapid growth of supercomputer power and development of more efficient and accurate Navier–Stokes equation solvers . To predict the strongly nonlinear chemical kinetic processes and their interactions with the flow, detailed chemistry is typically employed in DNS while the computational cost is high even after aggressive mechanism reduction . DNS on today’s supercomputer is capable to generate massive datasets, say tens or hundreds of terabytes, even in cleaned forms, such that systematic computational diagnostic tools need to be developed to extract salient information from the massive raw data. Canonical diagnostic methods based on individual scalars, such as temperature or a species concentration and their combinations (e.g., progress variable and mixture fraction) have been widely employed in previous studies. However, the use of such scalars typically requires semi-empirical criteria that need to be adjusted for different flame types and conditions, rendering them difficult to be automated for the processing of large flame data. Tools universally applicable to different flames and suitable for DNS data diagnostics are scarce and need to be developed. To address this need, a method of chemical explosive mode analysis (CEMA) was recently developed to systematically detect critical flame features for general reacting flows, particularly when local ignition, extinction, and premixed flame fronts are involved [3, 4, 5, 6]. CEMA has been demonstrated in elementary reactors, laminar flames and a variety of turbulent flames [3, 4, 5, 6, 7, 8, 9]. It was found that CEMA-based criteria are rather robust and reliable for limit phenomena detection for both premixed and partially premixed flames, and the use of CEMA in computational diagnostics of different types of flames is discussed in the present chapter.
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