Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/28158
Title: | Towards predicting liquid fuel physicochemical properties using molecular dynamics guided machine learning models |
Authors: | Freitas, RSM Lima, ÁPF Chen, C Rochinha, FA Mira, D Jiang, X |
Keywords: | fuel properties;molecular dynamics;deep learning;machine learning models |
Issue Date: | 5-Aug-2022 |
Publisher: | Elsevier |
Citation: | Freitas, R.S.M. et al. (2022) 'Towards predicting liquid fuel physicochemical properties using molecular dynamics guided machine learning models', Fuel, 329, 125415, pp. 1 - 14. doi: 10.1016/j.fuel.2022.125415. |
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 two particular properties, the fuel density and diffusion, 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. |
Description: | Data availability: Data will be made available on request. |
URI: | https://bura.brunel.ac.uk/handle/2438/28158 |
DOI: | https://doi.org/10.1016/j.fuel.2022.125415 |
ISSN: | 0016-2361 |
Other Identifiers: | ORCID iD: Rodolfo S.M. Freitas https://orcid.org/0000-0001-6036-8534 ORCID iD: Ágatha P.F. Lima https://orcid.org/0000-0002-2155-6185 ORCID iD: Cheng Chen https://orcid.org/0000-0001-7292-9490 ORCID iD: Fernando A. Rochinha https://orcid.org/0000-0001-8035-9651 ORCID iD: Daniel Mira https://orcid.org/0000-0001-9901-7942 ORCID iD: Xi Jiang https://orcid.org/0000-0003-2408-8812 125415 |
Appears in Collections: | Dept of Mechanical and Aerospace Engineering Research Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
FullText.pdf | Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/). | 4 MB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License