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- 저자 : Konrad Malik (검색결과 1 건)
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Konrad Malik,Mateusz Zbikowski,Andrzej Teodorczyk 한국원자력학회 2019 Nuclear Engineering and Technology Vol.51 No.2
The aim of the present study was to develop model for detonation cell sizes prediction based on a deepartificial neural network of hydrogen, methane and propane mixtures with air and oxygen. The discussionabout the currently available algorithms compared existing solutions and resulted in a conclusionthat there is a need for a new model, free from uncertainty of the effective activation energy and thereaction length definitions. The model offers a better and more feasible alternative to the existing ones. Resulting predictions were validated against experimental data obtained during the investigation ofdetonation parameters, as well as with data collected from the literature. Additionally, separate modelsfor individual mixtures were created and compared with the main model. The comparison showed nodrawbacks caused by fitting one model to many mixtures. Moreover, it was demonstrated that the modelmay be easily extended by including more independent variables. As an example, dependency onpressure was examined. The preparation of experimental data for deep neural network training wasdescribed in detail to allow reproducing the results obtained and extending the model to differentmixtures and initial conditions. The source code of ready to use models is also provided
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