An Informed Operator Based Genetic Algorithm for Tuning the Reaction Rate Parameters of Chemical Kinetics Mechanisms

Elliott, L.; Ingham, D.B.; Kyne, A. G.; Mera, N. S.; Pourkashanian, Mohamed; Wilson, C. W.

doi:10.1007/978-3-540-24855-2_107

Cited by 6 publications

(8 citation statements)

References 5 publications

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“… and Elliott et al . − The overall methodology is similar to that described in the previous section. The differences lie in how the parent and children chromosomes are defined.…”

Section: Optimization Modelsmentioning

confidence: 99%

“…In determining “the best” subset of species and their associated reactions the performance is based on an “objective function”, which measures how well the new reduced mechanisms predict a set of species' profiles simulated by the full mechanism. The second step of the reduction process involved taking the optimal skeletal mechanism generated in step 1 and applying a real encoded genetic algorithm − in order to find “the best” set of the Arrhenius reaction rate coefficients that provide the best fit to an experimental set of species' profiles. Here the “best set” of reaction rate coefficients is judged using an objective function that examines how effectively the reaction mechanisms predict an experimentally observed set of species' profiles.…”

Section: Introductionmentioning

confidence: 99%

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Reaction Mechanism Reduction and Optimization Using Genetic Algorithms

Elliott

Ingham

Kyne

et al. 2005

Ind. Eng. Chem. Res.

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This study describes the development of a new binary encoded genetic algorithm to determine a subset of species and their associated reactions that best represent the full starting point reaction mechanism in modeling a low-pressure stoichiometric 12.5% CH 4 /25% O 2 /62.5% Ar, burner-stabilized premixed flame. The number of species in the subset chosen is kept fixed and is specified at the start of the procedure. The genetic algorithm chooses better and better mechanisms on the basis of an objective function which measures how well the new reduced mechanisms predict a set of species' profiles simulated by the full mechanism. To verify the validity of our approach, a full enumeration was performed on a reduced problem, and it was reassuring to find that the genetic algorithm was able to find the optimum solution to this reduced problem in very few generations. A second step was to take the reduced reaction mechanism and to use a second real encoded genetic algorithm to determine the optimal reaction rate parameters that best model an experimental set of premixed flame species' profiles. This second step not only improved the reduced mechanism's ability to model the experimental profiles but also provided a remarkable improvement over the mechanism developed from step 1 in modeling combustion processes outside those used during the mechanism's development.

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“… and Elliott et al . − The overall methodology is similar to that described in the previous section. The differences lie in how the parent and children chromosomes are defined.…”

Section: Optimization Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reaction Mechanism Reduction and Optimization Using Genetic Algorithms

Elliott

Ingham

Kyne

et al. 2005

Ind. Eng. Chem. Res.

Self Cite

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show abstract

“…However, there are usually uncertainties in the experimental data, and even errors exist. 21 As indicated by Elliott, 31 if the experimental data for the mechanism optimization are incompatible, the subitems representing different experimental data in the objective function would conflict with each other, resulting in a defective-optimized reaction mechanism. Therefore, the multiobjective GA should be considered for the mechanism optimization.…”

Section: Introductionmentioning

confidence: 99%

Efficient Approach for the Optimization of Skeletal Chemical Mechanisms with Multiobjective Genetic Algorithm

Niu

Jia

et al. 2018

Energy Fuels

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For low-temperature combustion (LTC) engines, the ignition and combustion processes are dominantly controlled by the chemical kinetics of fuels. In order to simulate the working process of LTC engines using the multidimensional model, the skeletal or reduced oxidation mechanisms of fuels are urgently required. In this study, a new approach was proposed for the construction and optimization of the skeletal chemical mechanism of primary reference fuel (PRF) by coupling the decoupling methodology with the multiobjective genetic algorithm (GA). An initial skeletal chemical mechanism for PRF was first proposed according to the decoupling methodology. Then, the nondominated sorting genetic algorithm II (NSGA-II) code was employed to optimize the rate constants of the large-molecule reactions in the skeletal PRF mechanism. In NSGA-II, two objective functions based on the experimental data including ignition delay times in shock tubes (ST) and major species profiles in jet-stirred reactors (JSR) were introduced. Compared with the initial mechanism, the performance of the optimized mechanism is improved considerably. Furthermore, the optimized mechanism was used to predict the laminar flame speed, and the major species evolution in premixed laminar flames, as well as the in-cylinder pressure, heat release rate, and emissions in a homogeneous charge compression ignition (HCCI) engine. Good agreements between the predicted and measured results indicate that the integration of the decoupling methodology with genetic algorithm is an efficient approach for the construction of skeletal mechanisms. Furthermore, the influences of the selection and weight factor of the experimental data for mechanism optimization were discussed. The results indicate that the available experimental data on the ignition delay times under extensive operating conditions should be considered in the optimization process. Heavier weight factor should be assigned to the ignition delay times in the negative temperature coefficient (NTC) and low-temperature zones to improve the performance of the optimized mechanism.

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“…A common assumption is that higher fidelity models are generally more accurate at the expense of a higher computational cost. The complexity and level of details of a physical system comes in many forms, from variable mathematical models [7], variable parametric formulations [4,[8][9][10], variable operating conditions [11] to variable residual tolerance levels [12].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Optimization with Dynamic Fidelity Computational Models

Lim

Ong

Jin

et al. 2008

Lecture Notes in Computer Science

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Abstract. In optimization, it is now a common practice to use lower fidelity computational models in place of the original model when dealing with problems with computationally expensive objective functions. In this paper, we present a study on evolutionary optimization with dynamic fidelity computational models capable of acclimatizing to localized complexity, for enhancing design search efficiency. In particular, we propose an evolutionary framework for model fidelity control that decides, at runtime, the appropriate fidelity level of the computational model, which is deemed to be computationally less expensive, to be used in place of the exact analysis code as the search progresses. Empirical study on an aerodynamic airfoil design problem based on a Memetic Algorithm with Dynamic Fidelity Model (MA-DFM) demonstrates that improved quality solution and efficiency are obtained over existing evolutionary schemes.

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An Informed Operator Based Genetic Algorithm for Tuning the Reaction Rate Parameters of Chemical Kinetics Mechanisms

Cited by 6 publications

References 5 publications

Reaction Mechanism Reduction and Optimization Using Genetic Algorithms

Reaction Mechanism Reduction and Optimization Using Genetic Algorithms

Efficient Approach for the Optimization of Skeletal Chemical Mechanisms with Multiobjective Genetic Algorithm

Evolutionary Optimization with Dynamic Fidelity Computational Models

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