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DC Field | Value | Language |
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dc.contributor.author | Freitas, RSM | - |
dc.contributor.author | Lima, ÁPF | - |
dc.contributor.author | Chen, C | - |
dc.contributor.author | Rochinha, FA | - |
dc.contributor.author | Mira, D | - |
dc.contributor.author | Jiang, X | - |
dc.date.accessioned | 2024-02-01T15:58:37Z | - |
dc.date.available | 2024-02-01T15:58:37Z | - |
dc.date.issued | 2022-08-05 | - |
dc.identifier | ORCID iD: Rodolfo S.M. Freitas https://orcid.org/0000-0001-6036-8534 | - |
dc.identifier | ORCID iD: Ágatha P.F. Lima https://orcid.org/0000-0002-2155-6185 | - |
dc.identifier | ORCID iD: Cheng Chen https://orcid.org/0000-0001-7292-9490 | - |
dc.identifier | ORCID iD: Fernando A. Rochinha https://orcid.org/0000-0001-8035-9651 | - |
dc.identifier | ORCID iD: Daniel Mira https://orcid.org/0000-0001-9901-7942 | - |
dc.identifier | ORCID iD: Xi Jiang https://orcid.org/0000-0003-2408-8812 | - |
dc.identifier | 125415 | - |
dc.identifier.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. | en_US |
dc.identifier.issn | 0016-2361 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/28158 | - |
dc.description | Data availability: Data will be made available on request. | en_US |
dc.description.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. | en_US |
dc.description.sponsorship | The research leading to these results had received funding from the Brazilian National Agency of Petroleum, Natural Gas and Biofuels (ANP) through Programa de Recursos Humanos (PRH) under the PRH 8 - Mechanical Engineering for the Efficient Use of Biofuels, grant agreement numbers F0A5.EDDE.B5C0.3BCB and 2B61.4F5C.A83B.A713. | en_US |
dc.format.extent | 1 - 14 | - |
dc.format.medium | Print-Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | 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/). | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | fuel properties | en_US |
dc.subject | molecular dynamics | en_US |
dc.subject | deep learning | en_US |
dc.subject | machine learning models | en_US |
dc.title | Towards predicting liquid fuel physicochemical properties using molecular dynamics guided machine learning models | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1016/j.fuel.2022.125415 | - |
dc.relation.isPartOf | Fuel | - |
pubs.publication-status | Published | - |
pubs.volume | 329 | - |
dc.identifier.eissn | 1873-7153 | - |
dc.rights.holder | The Author(s) | - |
Appears in Collections: | Dept of Mechanical and Aerospace Engineering Research Papers |
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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 |
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