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Review
. 2024 Feb 27;27(4):109349.
doi: 10.1016/j.isci.2024.109349. eCollection 2024 Apr 19.

Data-driven models and digital twins for sustainable combustion technologies

Alessandro Parente  1   2   3 , Nedunchezhian Swaminathan  4
Affiliations
Review

Data-driven models and digital twins for sustainable combustion technologies

Alessandro Parente et al. iScience. .

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.

Keywords: Energy sustainability; Machine learning.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
A schematic of a digital twin (DT) with data flow: cyber-physical infrastructure can drive decarbonization in hard-to-abate industries creating a link between physical assets and virtual models to assess reliably and affordably the impact of renewable synthetic fuels on the combustion processes, the associated pollutants emission, the overall process efficiency, and the effect on the characteristics of the products
Figure 2
Figure 2
Adaptive simulation framework On-the-fly classification for dynamic, turbulent combustion simulations, adapting the chemical mechanism, the number of chemical species, the combustion closure, and the numerical settings to the local flow conditions.
Figure 3
Figure 3
A digital twins development framework based on heterogeneous data streams, feature extraction/dimensionality reduction, and ML algorithms
Figure 4
Figure 4
Schematic of the digital twin generation/adaptation process DTs can be updated using new data to modify the mapping between the feature and state spaces and between the input conditions and reactor network parameters. A new training process shall be carried out for new data significantly exceeding the original DOE.
See this image and copyright information in PMC

References

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