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

Abstract

The 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.
Author Konrad Malik (FPAE / IHE)
Konrad Malik,,
- The Institute of Heat Engineering
, Mateusz Żbikowski (FPAE / IHE)
Mateusz Żbikowski,,
- The Institute of Heat Engineering
, Andrzej Teodorczyk (FPAE / IHE)
Andrzej Teodorczyk,,
- The Institute of Heat Engineering
Journal seriesNuclear Engineering and Technology, ISSN 1738-5733, (A 25 pkt)
Issue year2019
Vol51
No2
Pages424-431
Publication size in sheets21.2
Keywords in EnglishDetonation Cell size model Machine learning Artificial neural network
ASJC Classification2104 Nuclear Energy and Engineering
DOIDOI:10.1016/j.net.2018.11.004.
URL https://doi.org/10.1016/j.net.2018.11.004
Languageen angielski
Score (nominal)25
ScoreMinisterial score = 25.0, 11-07-2019, ArticleFromJournal
Publication indicators Scopus SNIP (Source Normalised Impact per Paper): 2016 = 1.301; WoS Impact Factor: 2017 = 1.655 (2) - 2017=1.472 (5)
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