Gas detonation cell width prediction model based on support vector regression

Jiyang Yu; Bingxu Hou; Alexander Lelyakin; Zhanjie Xu; Thomas Jordan

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자료유형: 학술저널

저자정보: Jiyang Yu Bingxu Hou Alexander Lelyakin Zhanjie Xu Thomas Jordan

저널정보: 한국원자력학회 Nuclear Engineering and Technology Nuclear Engineering and Technology 제49권 제7호

발행연도: 2017.1

수록면: 1,423 - 1,430 (8page)

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Detonation cell width is an important parameter in hydrogen explosion assessments. The experimental data on gas detonation are statistically analyzed to establish a universal method to numerically predict detonation cell widths. It is commonly understood that detonation cell width, λ, is highly correlated with the characteristic reaction zone width, δ. Classical parametric regression methods were widely applied in earlier research to build an explicit semiempirical correlation for the ratio of λ/δ. The obtained correlations formulate the dependency of the ratio λ/δ on a dimensionless effective chemical activation energy and a dimensionless temperature of the gas mixture. In this paper, support vector regression (SVR), which is based on nonparametric machine learning, is applied to achieve functions with better fitness to experimental data and more accurate predictions. Furthermore, a third parameter, dimensionless pressure, is considered as an additional independent variable. It is found that three-parameter SVR can significantly improve the performance of the fitting function. Meanwhile, SVR also provides better adaptability and the model functions can be easily renewed when experimental database is updated or new regression parameters are considered.

#Detonation Cell Width #Hydrogen Safety #Machine Learning #Support Vector Regression

참고문헌 (20)

참고문헌 신청

A. Gavrikov / 2000 / A model for detonation cell size prediction from chemical kinetics / Combust. Flame 120 : 19 ~ 33

A. Smola / 2004 / A tutorial on support vector regression / Stat. Comput 14 : 199 ~ 222

D. Basak / 2007 / Support vector regression / Neural Inf. Process. Lett. Rev 11 : 203 ~ 224

D.Goodwin / / Cantera: an Object-Oriented Software Toolkit for Chemical Kinetics, Thermodynamics, and Transport Processes (Version 2.2.1)

F. Pedregosa / 2011 / Scikit-learn : machine learning in python / J. Mach. Learn. Res 12 : 2825 ~ 2830

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