An adaptive time-integration scheme for stiff chemistry based on computational singular perturbation and artificial neural networks

DOI10.1016/j.jcp.2021.110875OpenAlexW3215875127MaRDI QIDQ2134789

Mauro Valorani, Pietro Paolo Ciottoli, Hong G. Im, Riccardo Malpica Galassi

Publication date: 3 May 2022

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jcp.2021.110875

zbMATH Keywords

combustionmodel reductionslow invariant manifoldautoignitionmulti-scale dynamical systems

Mathematics Subject Classification ID

Numerical methods for ordinary differential equations (65Lxx) Thermodynamics and heat transfer (80Axx) Asymptotic theory for ordinary differential equations (34Exx)

Uses Software

• Cantera
• PyTorch
• Adam

Cites Work

• Projection-based model reduction: formulations for physics-based machine learning
• Stretching-based diagnostics and reduction of chemical kinetic models with diffusion
• The \(G\)-scheme: a framework for multi-scale adaptive model reduction
• Natural tangent dynamics with recurrent biorthonormalizations: a geometric computational approach to dynamical systems exhibiting slow manifolds and periodic/chaotic limit sets
• A CSP and tabulation-based adaptive chemistry model
• Computational singular perturbation with non-parametric tabulation of slow manifolds for time integration of stiff chemical kinetics
• Explicit time-scale splitting algorithm for stiff problems: Auto-ignition of gaseous mixtures behind a steady shock

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