This study delves into the auto-ignition temperature of n-heptane and ethanol mixtures within a counterflow flame configuration under low strain rate, with a particular focus on the impact of ethanol blending on heat release rates. Employing the sensitivity analysis method inspired by Zurada's sensitivity approach for neural network, this study identifies the group of critical species influencing the heat release rate. Further analysis concentration change reveals the intricate interactions among these various radicals across different temperature zones. It is found that, in n-heptane dominant mixtures, inhibition of low-temperature chemistry (LTC) caused by additional ethanol, impacts heat release rate at high temperature zone through diffusion effect of specific radicals such as CH2O, C2H4, C3H6 and H2O2. For ethanol-dominant mixtures, an increase in heat release rate was observed with higher ethanol fraction. Further concentration change analysis elucidated it is primarily attributed to the decomposition of ethanol and its subsequent reactions. This research underscores the significance of incorporating both chemical kinetics and species diffusion effects when analyzing the counterflow configuration of complex fuel mixtures.

翻译：本研究深入探讨了低应变率下逆流火焰构型中正庚烷与乙醇混合物的自点火温度，重点关注乙醇混合对放热速率的影响。采用受Zurada神经网络灵敏度方法启发的灵敏度分析法，识别出影响放热速率的关键物种组。进一步通过浓度变化分析揭示了不同温度区域中各类自由基间的复杂相互作用。研究发现：在正庚烷主导的混合物中，额外乙醇引起的低温化学抑制效应，通过CH2O、C2H4、C3H6和H2O2等特定自由基的扩散作用影响高温区的放热速率；而在乙醇主导的混合物中，随乙醇比例升高观察到放热速率增加，进一步浓度变化分析表明这主要归因于乙醇分解及其后续反应。本研究强调了在分析复杂燃料混合物逆流构型时，必须同时考虑化学动力学与物种扩散效应的重要性。