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Optimization of Reduced Kinetic Models for Reactive Flow Simulations

[+] Author Affiliations
P. Gokulakrishnan, R. Joklik, D. Viehe, A. Trettel, E. Gonzalez-Juez, M. Klassen

Combustion Science & Engineering, Inc., Columbia, MD

Paper No. GT2013-95215, pp. V01BT04A016; 14 pages
doi:10.1115/GT2013-95215
From:
  • ASME Turbo Expo 2013: Turbine Technical Conference and Exposition
  • Volume 1B: Combustion, Fuels and Emissions
  • San Antonio, Texas, USA, June 3–7, 2013
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-5511-9
  • Copyright © 2013 by ASME

abstract

A robust optimization scheme, known as rkmGen, for reaction rate parameter estimation has been developed for the generation of reduced kinetics models of practical interest for reactive flow simulations. It employs a stochastic optimization algorithm known as Simulated Annealing, and is implemented in C++ and coupled with Cantera, a chemical kinetics software package, to automate the reduced kinetic mechanism generation process. Reaction rate parameters in reduced order models can be estimated by optimizing against target data generated from a detailed model or by experiment. Target data may be of several different kinds: ignition delay time, blow-out time, laminar flame speed, species time-history profiles and species reactivity profiles. The software allows for simultaneous optimization against multiple target data sets over a wide range of temperatures, pressures and equivalence ratios.

In this paper, a detailed description of the optimization strategy used for the reaction parameter estimation is provided. To illustrate the performance of the software for reduced kinetic development, a number of test cases for various fuels were used: one-step, three-step and four-step global reduced kinetic models for ethylene, Jet-A and methane, respectively, and a fifty-step semi-global reduced kinetic model for methane. The fifty-step semi-global reduced kinetic model was implemented in the Star*CCM+ commercial CFD code to simulate Sandia Flame D using laminar flamelet libraries and compared with the experimental data. Simulations were also performed with the GRI3.0 mechanism for comparisons.

Copyright © 2013 by ASME

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