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Research Papers: Gas Turbines: Combustion, Fuels, and Emissions

Optimization of Reduced Kinetic Models for Reactive Flow Simulations

[+] Author and Article Information
R. Joklik

e-mail: rgjoklik@csefire.com

M. Klassen

Combustion Science & Engineering, Inc.,
8940 Old Annapolis Road,
Suite L,
Columbia, MD 21045

1Corresponding author.

Contributed by the Combustion and Fuels Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received July 8, 2013; final manuscript received August 6, 2013; published online October 21, 2013. Editor: David Wisler.

J. Eng. Gas Turbines Power 136(1), 011503 (Oct 21, 2013) (12 pages) Paper No: GTP-13-1244; doi: 10.1115/1.4025265 History: Received July 08, 2013; Revised August 06, 2013

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 (SA), 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 mechanism 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 50 step semiglobal reduced kinetic model for methane. The 50 step semiglobal 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.

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References

Figures

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Fig. 1

Growth in the size of detailed chemical kinetic models for various fuels (adopted from Law and Lu [11])

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Fig. 2

Schematic of the main components of the rkmGen optimization software

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Fig. 3

Visual representation of the search domain of simulated annealing path for global optimum for ignition delay time generated by rkmGen for one-step ethylene reduced model

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Fig. 4

Ignition delay time predictions for one-step ethylene model compared with detailed mechanism

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Fig. 6

Simultaneous optimization of ignition delay time and lean blow-out time with current 4-step reduced model. Key: symbols, target data (GRI3.0); solid lines, current optimized model; dashed lines, Jones and Lindstedt model [14].

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Fig. 7

Current four-step reduced model predictions for CO and H2 concentrations at blow-out. Key: symbols, target data (GRI3.0); solid lines, current optimized model; dashed lines, Jones and Lindstedt model [14].

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Fig. 8

Current four-step model predictions for blow-out temperature at 600 K and 1 atm compared with the model predictions from GRI3.0. Key: symbols, GRI3.0; solid lines, current four-step optimized model; dashed lines, Jones and Lindstedt model [14].

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Fig. 9

Ignition delay time predictions for off-design conditions using the four-step reduced model. The model predictions are compared with the shock tube experimental data of Petersen et al. [31] for methane/air (equivalence ratio = 0.5) at 0.7 and 20 atm. Design condition is stoichiometric at 1 atm. Key: symbols, experimental data [31]; lines, current four-step model predictions.

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Fig. 10

Current four-step model predictions for laminar flame speed at 300 K and 600 K at 1 atm compared with the model predictions from GRI3.0. Key: symbols, GRI3.0; solid lines, current optimized model; dashed lines, Jones and Lindstedt model [14].

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Fig. 11

Schematic of 50 step reduced kinetic model for a generic hydrocarbon fuel, CxHy

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Fig. 12

GRI3.0 model predictions compared with 50 step methane reduced kinetic model predictions obtained from simultaneous optimization of ignition delay time and lean blow-out time in rkmGen

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Fig. 13

Species time-history profile obtained from 50 step methane semiglobal reduced kinetic model compared with GRI3.0. Key: symbols, GRI3.0; lines, 50 step model.

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Fig. 5

Laminar flame speed data of Nishiie et al. [29] for Jet-A and n-decane compared with 3-step reduced model predictions

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Fig. 14

Axial profiles of different centerline quantities of Sandia Flame D experimental data [33] compared with current model predictions: experiments (symbols), GRI 3.0 (black line), CSE's 50 step mechanism (red line)

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Fig. 15

Radial profiles for velocity, temperature, mixture fraction, and mixture fraction variance at different distances downstream (x) from the exit plane of the jet. Key: same as in Fig. 14.

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Fig. 16

Radial profiles for chemical species at different distances downstream (x) from the exit plane of the jet. Key: same as in Fig. 14.

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Fig. 17

Isotherms for λ as function of probability (u) at different annealing temperatures (Tj) in Eq. (10)

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Fig. 18

Number of iterations needed to reach the final annealing temperature, Tf, as a function of T0/Tf for various quench factors, and constants m and n. k(Tf) is the number of iterations needed to reach the final annealing temperature.

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