Article ; Online: Data-driven models and digital twins for sustainable combustion technologies.
iScience
2024 Volume 27, Issue 4, Page(s) 109349
Abstract: 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 ...
Abstract | 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. |
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Language | English |
Publishing date | 2024-02-27 |
Publishing country | United States |
Document type | Journal Article ; Review |
ISSN | 2589-0042 |
ISSN (online) | 2589-0042 |
DOI | 10.1016/j.isci.2024.109349 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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