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

Prediction of ignition delay times of Jet A-1/hydrogen fuel mixture using machine learning

“…Since accurate numerical simulation of 0-D auto-ignition processes using detailed chemical reaction mechanisms is a quite time-consuming process due to solving many degrees of freedom, it is practically useful to develop an quasi-instant ANN proxy model (or digital-twin) for predicting IDTs over a wide range of temperatures, pressures and other parameters relevant to guide engine designs. Such ANN proxy models have been developed in previous studies for hydrogen [20], biodiesel [21] and n-heptane [22]. In addition, recent studies pay more attention to the ML modeling of IDTs of complex mixture fuels.…”

Effects of High-Concentration CO2 on Ignition Delay Time of 70% n-Heptane/30% Toluene Mixtures

“…Diesel is a complex mixture composing hundreds of different components, which is not feasible to consider the oxidation chemistry of all the compounds [6,7]. Encouragingly, n-heptane is commonly adopted as DSF due to it having a similar cetane number to diesel and the oxidation reaction is well known [8][9][10][11]. Additionally, the aromatic compounds in diesel account to about 30% [12], and the most representative substance with a simple structure is toluene.…”