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    An adaptive time-integration scheme for stiff chemistry based on Computational Singular Perturbation and Artificial Neural Networks

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    Name:
    Adaptive_1-s2.0-S0021999121007701-main (1).pdf
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    4.552Mb
    Format:
    PDF
    Description:
    Accepted manuscript
    Embargo End Date:
    2023-11-29
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    Type
    Article
    Authors
    Malpica Galassi, Riccardo cc
    Ciottoli, Pietro P. cc
    Valorani, Mauro
    Im, Hong G. cc
    KAUST Department
    Clean Combustion Research Center
    Computational Reacting Flow Laboratory (CRFL)
    Mechanical Engineering Program
    Physical Science and Engineering (PSE) Division
    Date
    2021-11-30
    Online Publication Date
    2021-11-29
    Print Publication Date
    2022-02
    Embargo End Date
    2023-11-29
    Submitted Date
    2021-07-27
    Permanent link to this record
    http://hdl.handle.net/10754/673852
    
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    Abstract
    We leverage the computational singular perturbation (CSP) theory to develop an adaptive time-integration scheme for stiff chemistry based on a local, projection-based, reduced order model (ROM) freed of the fast time-scales. Its construction is such that artificial neural networks (ANN) can be plugged-in as cheap surrogates of the local projection basis, which is a state function, to alleviate the computational cost, without sacrificing the geometrical and physical foundation of the method. In fact, the solver relies on the synthetic basis in place of the more expensive on-the-fly calculated basis, i.e. the eigenvectors of the Jacobian matrix of the chemical source term, to define the local slow invariant manifold (SIM) and the projection matrix, then integrates explicitly the projected, i.e., non-stiff, chemical source term.
    Citation
    Malpica Galassi, R., Ciottoli, P. P., Valorani, M., & Im, H. G. (2021). An adaptive time-integration scheme for stiff chemistry based on Computational Singular Perturbation and Artificial Neural Networks. Journal of Computational Physics, 110875. doi:10.1016/j.jcp.2021.110875
    Sponsors
    We acknowledge the fruitful discussions and the technical support kindly offered by Dr. Shivam Barwey and Professor Venkat Raman at the University of Michigan, Ann Arbor, MI, USA, and Dr. Mattia Soldan at KAUST, Saudi Arabia. R. Malpica Galassi acknowledges the financial support of the Fédération Wallonie-Bruxelles (FWB), Cellule Europe.
    Publisher
    Elsevier BV
    Journal
    Journal of Computational Physics
    DOI
    10.1016/j.jcp.2021.110875
    Additional Links
    https://linkinghub.elsevier.com/retrieve/pii/S0021999121007701
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.jcp.2021.110875
    Scopus Count
    Collections
    Articles; Physical Science and Engineering (PSE) Division; Mechanical Engineering Program; Clean Combustion Research Center

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