A data-driven framework to predict ignition delays of straight-chain alkanes

Ignition delay time (IDT) is an important global combustion property that affects the thermal efficiency of the engine and emissions (particularly NO ). IDT is generally measured by performing experiments using Shock-tube and Rapid Compression Machine (RCM). The numerical calculation of IDT is a computationally expensive and time-consuming process. Arrhenius type empirical correlations offer an inexpensive alternative to obtain IDT. However, such correlations have limitations as these typically involve ad-hoc parameters and are valid only for a specific fuel and particular range of temperature/pressure conditions. This study aims to formulate a data-driven scientific way to obtain IDT for new fuels without performing detailed numerical calculations or relying on ad-hoc empirical correlations. We propose a rigorous, well-validated data-driven study to obtain IDT for new fuels using a regression-based clustering algorithm. In our model, we bring in the fuel structure through the overall activation energy ( ) by expressing it in terms of the different bonds present in the molecule. Gaussian Mixture Model (GMM) is used for the formation of clusters, and regression is applied over each cluster to generate models. The proposed algorithm is used on the ignition delay dataset of straight-chain alkanes (C H for n = 4 to 16). The high level of accuracy achieved demonstrates the applicability of the proposed algorithm over IDT data. The algorithm and framework discussed in this article are implemented in python and made available at https://doi.org/10.5281/zenodo.5774617.