Data-driven models and digital twins for sustainable combustion technologies
Summary: We highlight the critical role of data in developing sustainable combustion technologies for industries requiring high-density and localized energy sources. Combustion systems are complex and difficult to predict, and high-fidelity simulations are out of reach for practical systems because...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-04-01
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| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004224005704 |
| _version_ | 1827318640458858496 |
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| author | Alessandro Parente Nedunchezhian Swaminathan |
| author_facet | Alessandro Parente Nedunchezhian Swaminathan |
| author_sort | Alessandro Parente |
| collection | DOAJ |
| description | Summary: We highlight the critical role of data in developing sustainable combustion technologies for industries requiring high-density and localized energy sources. Combustion systems are complex and difficult to predict, and high-fidelity simulations are out of reach for practical systems because of computational cost. Data-driven approaches and artificial intelligence offer promising solutions, enabling renewable synthetic fuels to meet decarbonization goals. We discuss open challenges associated with the availability and fidelity of data, physics-based numerical simulations, and machine learning, focusing on developing digital twins capable of mirroring the behavior of industrial combustion systems and continuously updating based on newly available information. |
| first_indexed | 2024-04-25T00:04:18Z |
| format | Article |
| id | doaj.art-f1d8109e80f44b25a19b7f9a28cd9918 |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-04-25T00:04:18Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-f1d8109e80f44b25a19b7f9a28cd99182024-03-14T06:15:49ZengElsevieriScience2589-00422024-04-01274109349Data-driven models and digital twins for sustainable combustion technologiesAlessandro Parente0Nedunchezhian Swaminathan1Aero-Thermo-Mechanics Department, École polytechnique de Bruxelles, Université libre de Bruxelles, Avenue Franklin D. Roosevelt 50, 1050 Brussels, Belgium; WEL Research Institute, Avenue Pasteur 6, 1300 Wavre, Belgium; Brussels Institute for Thermal-fluid systems and clean Energy (BRITE), Université libre de Bruxelles and Vrije Universiteit Brussel, 1050 Ixelles, Belgium; Corresponding authorDepartment of Engineering, Hopkinson Laboratory, Cambridge University, Cambridge CB2 1PZ, UK; Corresponding authorSummary: We highlight the critical role of data in developing sustainable combustion technologies for industries requiring high-density and localized energy sources. Combustion systems are complex and difficult to predict, and high-fidelity simulations are out of reach for practical systems because of computational cost. Data-driven approaches and artificial intelligence offer promising solutions, enabling renewable synthetic fuels to meet decarbonization goals. We discuss open challenges associated with the availability and fidelity of data, physics-based numerical simulations, and machine learning, focusing on developing digital twins capable of mirroring the behavior of industrial combustion systems and continuously updating based on newly available information.http://www.sciencedirect.com/science/article/pii/S2589004224005704Machine learningEnergy sustainability |
| spellingShingle | Alessandro Parente Nedunchezhian Swaminathan Data-driven models and digital twins for sustainable combustion technologies iScience Machine learning Energy sustainability |
| title | Data-driven models and digital twins for sustainable combustion technologies |
| title_full | Data-driven models and digital twins for sustainable combustion technologies |
| title_fullStr | Data-driven models and digital twins for sustainable combustion technologies |
| title_full_unstemmed | Data-driven models and digital twins for sustainable combustion technologies |
| title_short | Data-driven models and digital twins for sustainable combustion technologies |
| title_sort | data driven models and digital twins for sustainable combustion technologies |
| topic | Machine learning Energy sustainability |
| url | http://www.sciencedirect.com/science/article/pii/S2589004224005704 |
| work_keys_str_mv | AT alessandroparente datadrivenmodelsanddigitaltwinsforsustainablecombustiontechnologies AT nedunchezhianswaminathan datadrivenmodelsanddigitaltwinsforsustainablecombustiontechnologies |