Applicability of high dimensional model representation correlations for ignition delay times of n-heptane/air mixtures

Wang LIU, Jiabo ZHANG, Zhen HUANG, Dong HAN

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Front. Energy ›› 2019, Vol. 13 ›› Issue (2) : 367-376. DOI: 10.1007/s11708-018-0584-9
RESEARCH ARTICLE
RESEARCH ARTICLE

Applicability of high dimensional model representation correlations for ignition delay times of n-heptane/air mixtures

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Abstract

It is difficult to predict the ignition delay times for fuels with the two-stage ignition tendency because of the existence of the nonlinear negative temperature coefficient (NTC) phenomenon at low temperature regimes. In this paper, the random sampling-high dimensional model representation (RS-HDMR) methods were employed to predict the ignition delay times of n-heptane/air mixtures, which exhibits the NTC phenomenon, over a range of initial conditions. A detailed n-heptane chemical mechanism was used to calculate the fuel ignition delay times in the adiabatic constant-pressure system, and two HDMR correlations, the global correlation and the stepwise correlations, were then constructed. Besides, the ignition delay times predicted by both types of correlations were validated against those calculated using the detailed chemical mechanism. The results showed that both correlations had a satisfactory prediction accuracy in general for the ignition delay times of the n-heptane/air mixtures and the stepwise correlations exhibited a better performance than the global correlation in each subdomain. Therefore, it is concluded that HDMR correlations are capable of predicting the ignition delay times for fuels with two-stage ignition behaviors at low-to-intermediate temperature conditions.

Keywords

ignition delay / random sampling / high dimensional model representation / n-heptane / fuel kinetics

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Wang LIU, Jiabo ZHANG, Zhen HUANG, Dong HAN. Applicability of high dimensional model representation correlations for ignition delay times of n-heptane/air mixtures. Front. Energy, 2019, 13(2): 367‒376 https://doi.org/10.1007/s11708-018-0584-9

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 51776124).

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2018 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature
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