  A novel method for analyzing counterflow diffusion flames, inspired by
Zurada's sensitivity approach for neural networks, is proposed to identify
critical species influencing the heat release rate in combustion. By further
analyzing concentration changes of selected key species and radicals, this
method reveals complex interactions among them across regions with temperature.
To illustrate this approach, the study investigates the mechanisms of
auto-ignition of n-heptane and ethanol mixtures in a counterflow configuration
under low strain rates. In mixtures where n-heptane is dominant, the inhibition
of low-temperature chemistry (LTC) by addition of ethanol impacts the heat
release rate in regions where the temperature is higher through the diffusion
of specific radicals such as CH2O, C2H4, C3H6, and H2O2. In mixtures where
ethanol is dominant, the high ethanol fractions in the mixture increase the
heat release rate, primarily due to ethanol decomposition and its subsequent
reactions. This method effectively quantifies and compares the influence of
both chemical kinetics and species diffusion effects, providing detailed
insights into the interactions among species across the reactive field when
analyzing the counterflow configuration of complex fuel mixtures.
