Prediction of liquid fuel properties using machine learning models with Gaussian processes and probabilistic conditional generative learning

TL;DR

This paper develops machine learning models, including Gaussian processes and probabilistic generative models, to accurately predict fuel properties like density across various conditions, using data from simulations and experiments.

Contribution

It introduces a data-fusion approach combining Gaussian processes and generative models for predicting fuel properties with uncertainty quantification.

Findings

ML models accurately predict fuel density over diverse conditions

Models effectively handle multi-fidelity data

Gaussian processes quantify uncertainty in predictions

Abstract

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.