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Data-driven models and digital twins for sustainable combustion technologies.

Published version
Peer-reviewed

Repository DOI


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Authors

Parente, Alessandro 
Swaminathan, Nedunchezhian 

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.

Description

Keywords

Energy sustainability, Machine learning

Journal Title

iScience

Conference Name

Journal ISSN

2589-0042
2589-0042

Volume Title

27

Publisher

Elsevier BV
Sponsorship
Fondation Wiener Anspach (Unknown)
Engineering and Physical Sciences Research Council (EP/S025650/1)