Ignition delay time (IDT) is an important global combustion property that affects the thermal efficiency of the engine and emissions (particularly NO${}_X$). 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 ($E_a$) 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${}_n$ H${}_{2n+2}$ 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.

点火延迟时间 (IDT) 是影响发动机热效率和排放（尤其是 NO${}_X$）。IDT 通常是通过使用 Shock-tube 和 Rapid Compression Machine (RCM) 进行实验来测量的。IDT 的数值计算是一个计算量大且耗时的过程。Arrhenius 类型的经验相关性为获得 IDT 提供了一种廉价的替代方法。然而，这种相关性具有局限性，因为它们通常涉及特定参数并且仅对特定燃料和特定范围的温度/压力条件有效。本研究旨在制定一种数据驱动的科学方法来获得新燃料的 IDT，而无需执行详细的数值计算或依赖临时经验相关性。我们提出了一项严格的、经过充分验证的数据驱动研究，以使用基于回归的聚类算法获得新燃料的 IDT。在我们的模型中，${乙}_{\mathrm{一个}}$) 通过用分子中存在的不同键来表达它。高斯混合模型 (GMM) 用于集群的形成，并对每个集群应用回归以生成模型。所提出的算法用于直链烷烃的点火延迟数据集（C${}_n$H${}_{2n+2}$对于n  = 4 到 16）。所达到的高准确度证明了所提出的算法在 IDT 数据上的适用性。本文中讨论的算法和框架是在 python 中实现的，可在 https://doi.org/10.5281/zenodo.5774617 获得。