The use of ignition delay time in genetic algorithms optimisation of chemical kinetics reaction mechanisms

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

doi:10.1016/j.engappai.2005.02.006

Cited by 14 publications

(7 citation statements)

References 8 publications

(13 reference statements)

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“…As indicated in the previous study, 33 if an optimized chemical mechanism is achieved based on only one category of experimental data, e.g., ignition delay times or major species concentrations, the predictions using the optimized mechanism usually cannot well agree with the other category of measurements. Therefore, wide sources of experimental data should be considered in the optimization of the chemical mechanism in order to achieve a satisfactory mechanism for the application in wide operating conditions.…”

Section: Computational Approachmentioning

confidence: 97%

“…Although a singleobjective function can be constructed by including all the experimental information into a mathematical expression, the relationships among the different objectives are hidden, 44 and it works worse when a large noise exists between the objectives. 33 To solve the problem, a multiobjective GA should be considered.…”

Section: Computational Approachmentioning

confidence: 99%

“…Therefore, wide sources of experimental data should be considered in the optimization of the chemical mechanism in order to achieve a satisfactory mechanism for the application in wide operating conditions. Although a single-objective function can be constructed by including all the experimental information into a mathematical expression, the relationships among the different objectives are hidden, and it works worse when a large noise exists between the objectives . To solve the problem, a multiobjective GA should be considered.…”

Section: Computational Approachmentioning

confidence: 99%

“…However, only considering the product concentrations in the mechanism optimization is not sufficient. 29,33 The purpose of this study is to provide an automatic and efficient method for the construction and optimization of skeletal mechanisms based on the decoupling methodology. 10,19 By integrating the multiobjective GA using the nondominated sorting genetic algorithm II (NSGA-II) code 34 and the decoupling methodology, a skeletal PRF mechanism is developed.…”

Section: Introductionmentioning

confidence: 99%

“…Two objective functions including the O 2 and CO 2 concentrations were considered in the optimization process, and a set of optimized mechanisms were gained. However, only considering the product concentrations in the mechanism optimization is not sufficient. , …”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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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Section: Computational Approachmentioning

confidence: 97%

Section: Computational Approachmentioning

confidence: 99%

Section: Computational Approachmentioning

confidence: 99%