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Updated: Jun 30, 2025

Combustion Characterization and Model Fuel Development for Micro-tubular Flame-assisted Fuel Cells
Published on: October 2, 2016
Data-driven models and digital twins for sustainable combustion technologies
Alessandro Parente1,2,3, Nedunchezhian Swaminathan4
1Aero-Thermo-Mechanics Department, École polytechnique de Bruxelles, Université libre de Bruxelles, Avenue Franklin D. Roosevelt 50, 1050 Brussels, Belgium.
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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