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Reduced order model of diffusion flames based on multi-scale data from detailed CFD: the impact of preprocessing

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Abstract

Machine learning techniques, such as reduced order models (ROM), have demonstrated low cost when creating models of complex systems while aiming at the same accuracy as high-fidelity models, such as computational fluid dynamics (CFD). However, reduced models must preserve some properties tied to the studied system. For a combustion problem, those are in particular monotonicity, positivity, and boundedness. Here, ROM are created using data from CFD simulations of non-premixed laminar flames with detailed chemistry and transport. The data obtained for variable fuel velocity is reduced using singular value decomposition (SVD), and then a genetic aggregation response surface algorithm is applied to predict the properties fields for an arbitrary fuel inlet velocity. This work analyzes the effect of different data preprocessing approaches on the ROM, i.e., (1) the properties treated as an uncoupled or as a coupled system; (2) normalization of different properties; (3) the logarithm of the chemical species. For all constructed ROM, the energy content of the reduction process and the reconstructed fields of the flame properties evidence the slow convergence of SVD modes for the uncoupled ROM, while a faster one is seen when the logarithm preprocessing is applied. Also, the learning is shown to be achieved with a smaller number of modes for two of the coupled ROM and for the ROM using the logarithm. The reconstruction of the mass fraction fields is characterized by regions of negative values, underscoring that the baseline ROM methodology does not preserve the properties of monotonicity, positivity, and boundedness. The proposed logarithm preprocessing enables to overcome the positivity problem and to accurately reproduce the original data.

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Acknowledgements

For the purpose of Open Access, a CC-BY public copyright license has been applied by the authors to the present document and will be applied to all subsequent versions up to the Author Accepted Manuscript arising from this submission.

Funding

Nicole Lopes Junqueira and Luís Fernando Figueira da Silva received funding from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. Part of this work was deveped while Louise da C. Ramos received funding from the European Union’s Horizon 2020 research and innovation program under Grant Agreement nr 766264.

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Author notes

  1. Louise da Costa Ramos and Luís Fernando Figueira da Silva have contributed equally to this work.

Authors and Affiliations

  1. Department of Mechanical Engineering, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro, 22451-900, Brazil

    Nicole Lopes Junqueira & Luís Fernando Figueira da Silva

  2. Institut Pprime CNRS, ISAE-ENSMA, Université de Poitiers, 86360, Chasseneuil du Poitou, France

    Nicole Lopes Junqueira & Luís Fernando Figueira da Silva

  3. Ansys Fr, 69100, Villeurbanne, France

    Nicole Lopes Junqueira

  4. GIPSA-Lab, Université Grenoble Alpes, Grenoble INP, Grenoble, France

    Louise da Costa Ramos

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  1. Nicole Lopes Junqueira
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all authors contributed to the study conception and design. The first draft of the manuscript was written by NLJ, L da CR and LFF da S and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Nicole Lopes Junqueira.

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Lopes Junqueira, N., da Costa Ramos, L. & Figueira da Silva, L.F. Reduced order model of diffusion flames based on multi-scale data from detailed CFD: the impact of preprocessing. J Braz. Soc. Mech. Sci. Eng. 46, 215 (2024). https://doi.org/10.1007/s40430-024-04749-6

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