An adaptive time-integration scheme for stiff chemistry based on Computational Singular Perturbation and Artificial Neural Networks
KAUST DepartmentClean Combustion Research Center
Computational Reacting Flow Laboratory (CRFL)
Mechanical Engineering Program
Physical Science and Engineering (PSE) Division
Embargo End Date2023-11-29
Permanent link to this recordhttp://hdl.handle.net/10754/673852
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AbstractWe 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.
CitationMalpica 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
SponsorsWe 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.
JournalJournal of Computational Physics