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Updated: Jun 30, 2025

Combustion Characterization and Model Fuel Development for Micro-tubular Flame-assisted Fuel Cells
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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.

Iscience
|March 19, 2024
PubMed
Summary

Data and artificial intelligence are key to advancing sustainable combustion technologies. Developing accurate digital twins for complex industrial systems requires overcoming challenges in data availability and simulation fidelity to meet decarbonization goals.

Keywords:
Energy sustainabilityMachine learning

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Area of Science:

  • Combustion science and engineering
  • Sustainable energy technologies
  • Data-driven modeling and artificial intelligence

Background:

  • Industrial combustion systems are critical for high-density energy but are complex and computationally expensive to simulate.
  • Existing high-fidelity simulations are often impractical for real-world industrial applications due to computational costs.
  • Decarbonization goals necessitate innovative solutions for cleaner energy production.

Purpose of the Study:

  • To highlight the essential role of data in developing sustainable combustion technologies.
  • To explore the potential of data-driven approaches and artificial intelligence (AI) in addressing combustion system complexities.
  • To discuss challenges and propose solutions for creating predictive digital twins of industrial combustion systems.

Main Methods:

  • Leveraging data-driven methodologies and AI to overcome limitations of traditional physics-based simulations.
  • Focusing on the development of digital twins that accurately represent industrial combustion system behavior.
  • Addressing challenges in data availability, data fidelity, and integration with numerical simulations.

Main Results:

  • Data-driven approaches and AI offer viable pathways for modeling complex combustion systems.
  • Renewable synthetic fuels can be effectively integrated using these advanced techniques.
  • The development of continuously updating digital twins is crucial for practical applications.

Conclusions:

  • Data and AI are pivotal for advancing sustainable combustion and achieving decarbonization targets.
  • Overcoming challenges in data and simulation is necessary for the widespread adoption of digital twins in industry.
  • Future research should focus on enhancing data fidelity and integrating AI with physics-based models for robust combustion solutions.