Accurate determination of fuel properties of complex mixtures over a wide range of pressure and temperature conditions is essential to utilizing alternative fuels. The present work aims to construct cheap-to-compute machine learning (ML) models to act as closure equations for predicting the physical properties of alternative fuels. Those models can be trained using the database from MD simulations and/or experimental measurements in a data-fusion-fidelity approach. Here, Gaussian Process (GP) and probabilistic generative models are adopted. GP is a popular non-parametric Bayesian approach to build surrogate models mainly due to its capacity to handle the aleatory and epistemic uncertainties. Generative models have shown the ability of deep neural networks employed with the same intent. In this work, ML analysis is focused on a particular property, the fuel density, but it can also be extended to other physicochemical properties. This study explores the versatility of the ML models to handle multi-fidelity data. The results show that ML models can predict accurately the fuel properties of a wide range of pressure and temperature conditions.
翻译:精确确定复杂混合物在各种压力和温度条件下的燃料特性,对于使用替代燃料至关重要。目前的工作旨在建立廉价到计算机学习模型,作为预测替代燃料物理特性的封闭方程式。这些模型可以使用数据聚合-不易燃方法的MD模拟和/或实验测量数据库进行培训。在这里,采用了高斯进程(GP)和概率基因化模型。GP是一种流行的非参数贝耶西亚方法,主要用于建立代用模型,因为它有能力处理感应和感应不确定因素。灵敏模型显示了以同样意图使用的深神经网络的能力。在这项工作中,ML分析侧重于特定属性、燃料密度,但也可以扩大到其他物理化学特性。这项研究探讨了ML模型处理多纤维数据时的多功能性。结果显示,ML模型能够准确预测广泛的压力和温度条件下的燃料特性。