The spectral characterisation of reduced order models in chemical kinetic systems

Mauro Valorani, Riccardo Malpica Galassi, Pietro Paolo Ciottoli, Habib Najm, Samuel Paolucci

Research output: Contribution to journalArticlepeer-review

Abstract

The size and complexity of multi-scale problems such as those arising in chemical kinetics mechanisms has stimulated the search for methods that reduce the number of species and chemical reactions but retain a desired degree of accuracy. The time-scale characterisation of the multi-scale problem can be carried out on the basis of local information such as the Jacobian matrix of the model problem and its related eigen-system evaluated at one point P of the system trajectory. While the original problem is usually described by ordinary differential equations (ODEs), the reduced order model is described by a reduced number of ODEs and a number of algebraic equations (AEs), that might express one or more physical conservation laws (mass, momentum, energy), or the fact that the long-term dynamics evolves within a so-called Slow Invariant Manifold (SIM). To fully exploit the benefits offered by a reduced order model, it is required that the time scale characterisation of the n-dimensional reduced order model returns an answer consistent and coherent with the time-scale characterisation of the N-dimensional original model. This manuscript discusses a procedure for obtaining the time-scale characterisation of the reduced order model in a manner that is consistent with that of the original problem. While a standard time scale characterisation of the (original) N-dimensional original model can be carried out by evaluating the eigen-system of the (N×N) Jacobian matrix of the vector field that defines the system dynamics, the time-scale characterisation of the n-dimensional reduced order model (with n
Original languageEnglish (US)
Pages (from-to)1-32
Number of pages32
JournalCombustion Theory and Modelling
DOIs
StatePublished - Nov 7 2022
Externally publishedYes

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • General Physics and Astronomy
  • Modeling and Simulation
  • General Chemical Engineering
  • General Chemistry
  • Fuel Technology

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