Detonation cell size model based on deep neural network for hydrogen, methane and propane mixtures with air and oxygen
Konrad Malik , Mateusz Żbikowski , Andrzej Teodorczyk
AbstractThe aim of the present study was to develop model for detonation cell sizes prediction based on a deep artificial neural network of hydrogen, methane and propane mixtures with air and oxygen. The discussion about the currently available algorithms compared existing solutions and resulted in a conclusion that there is a need for a new model, free from uncertainty of the effective activation energy and the reaction 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 of detonation parameters, as well as with data collected from the literature. Additionally, separate models for individual mixtures were created and compared with the main model. The comparison showed no drawbacks caused by fitting one model to many mixtures. Moreover, it was demonstrated that the model may be easily extended by including more independent variables. As an example, dependency on pressure was examined. The preparation of experimental data for deep neural network training was described in detail to allow reproducing the results obtained and extending the model to different mixtures and initial conditions. The source code of ready to use models is also provided.
|Journal series||Nuclear Engineering and Technology, ISSN 1738-5733|
|Publication size in sheets||21.2|
|Keywords in English||Detonation Cell size model Machine learning Artificial neural network|
|Score||= 100.0, 28-01-2020, ArticleFromJournal|
|Publication indicators||= 1; : 2016 = 1.301; : 2018 = 1.546 (2) - 2018=1.708 (5)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.