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Integrating properties and conditions to predict spray performance of alternative aviation fuel by ANN model
Alternative aviation fuel has been confirmed benefits for GHGs reduction and energy saving. Alternative fuel use should meet drop-in fuel requirement, and one of the important factors to ensure combustion completeness is to achieve spray requirement in the whole envelop of flight. Alternative fuels...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10634133/ https://www.ncbi.nlm.nih.gov/pubmed/37941033 http://dx.doi.org/10.1186/s13068-023-02408-x |
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author | Liu, Ziyu Tang, Zimu Yang, Xiaoyi |
author_facet | Liu, Ziyu Tang, Zimu Yang, Xiaoyi |
author_sort | Liu, Ziyu |
collection | PubMed |
description | Alternative aviation fuel has been confirmed benefits for GHGs reduction and energy saving. Alternative fuel use should meet drop-in fuel requirement, and one of the important factors to ensure combustion completeness is to achieve spray requirement in the whole envelop of flight. Alternative fuels are characterized different fuel properties at low temperature comparison with traditional jet fuel. For understanding fuel properties and spray-related processes under different conditions, alternative aviation fuel, including Fischer Tropsch (FT), cellulose hydrotreating jet fuel (CHJ) and traditional jet fuel (RP-3), were investigated spray performance. According to empirical equation deduced from experiment data (283 K-343 K), deviations to RP-3 enhanced significantly on surface tension and viscosity at low temperature aera (243 K-273 K). As the complex and discontinuous interaction between nozzle structure and fuel properties with temperature, and thus it is difficult to obtain appropriate empirical equation or simulation results at low temperature. Moreover, non-drop-in fuel like pure FT fuel cannot comply with the same spray mechanism as drop-in fuel. The artificial neural network (ANN) approaches have been involved to solve the complex relationship of properties with spray performance. ANN-spray model coupling with ANN-mass flow can predict not only cone angle and liquid length but also SMD and velocity in liquid zone and droplet zone with above 0.99 total correlation coefficient. Coupling simulation results of mass flow and spray performance, FT and CHJ as well as blend fuels present more obvious difference to RP-3 in droplet size distribution and velocity distribution at low temperature. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13068-023-02408-x. |
format | Online Article Text |
id | pubmed-10634133 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-106341332023-11-10 Integrating properties and conditions to predict spray performance of alternative aviation fuel by ANN model Liu, Ziyu Tang, Zimu Yang, Xiaoyi Biotechnol Biofuels Bioprod Research Alternative aviation fuel has been confirmed benefits for GHGs reduction and energy saving. Alternative fuel use should meet drop-in fuel requirement, and one of the important factors to ensure combustion completeness is to achieve spray requirement in the whole envelop of flight. Alternative fuels are characterized different fuel properties at low temperature comparison with traditional jet fuel. For understanding fuel properties and spray-related processes under different conditions, alternative aviation fuel, including Fischer Tropsch (FT), cellulose hydrotreating jet fuel (CHJ) and traditional jet fuel (RP-3), were investigated spray performance. According to empirical equation deduced from experiment data (283 K-343 K), deviations to RP-3 enhanced significantly on surface tension and viscosity at low temperature aera (243 K-273 K). As the complex and discontinuous interaction between nozzle structure and fuel properties with temperature, and thus it is difficult to obtain appropriate empirical equation or simulation results at low temperature. Moreover, non-drop-in fuel like pure FT fuel cannot comply with the same spray mechanism as drop-in fuel. The artificial neural network (ANN) approaches have been involved to solve the complex relationship of properties with spray performance. ANN-spray model coupling with ANN-mass flow can predict not only cone angle and liquid length but also SMD and velocity in liquid zone and droplet zone with above 0.99 total correlation coefficient. Coupling simulation results of mass flow and spray performance, FT and CHJ as well as blend fuels present more obvious difference to RP-3 in droplet size distribution and velocity distribution at low temperature. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13068-023-02408-x. BioMed Central 2023-11-08 /pmc/articles/PMC10634133/ /pubmed/37941033 http://dx.doi.org/10.1186/s13068-023-02408-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Liu, Ziyu Tang, Zimu Yang, Xiaoyi Integrating properties and conditions to predict spray performance of alternative aviation fuel by ANN model |
title | Integrating properties and conditions to predict spray performance of alternative aviation fuel by ANN model |
title_full | Integrating properties and conditions to predict spray performance of alternative aviation fuel by ANN model |
title_fullStr | Integrating properties and conditions to predict spray performance of alternative aviation fuel by ANN model |
title_full_unstemmed | Integrating properties and conditions to predict spray performance of alternative aviation fuel by ANN model |
title_short | Integrating properties and conditions to predict spray performance of alternative aviation fuel by ANN model |
title_sort | integrating properties and conditions to predict spray performance of alternative aviation fuel by ann model |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10634133/ https://www.ncbi.nlm.nih.gov/pubmed/37941033 http://dx.doi.org/10.1186/s13068-023-02408-x |
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