Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Mateusz Zbikowski"'
Publikováno v:
Energy. 116:1479-1483
The aim of this work was to determine the influence of methane addition on the critical layer thickness h* and its relation to detonation cell size ( λ ) for stable detonation propagating in semi-confined, non-obstructed channel filled with uniform,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5ca7e1dc4f19b2a78df9cec84c5d9ff8
https://doi.org/10.1016/j.energy.2016.06.001
https://doi.org/10.1016/j.energy.2016.06.001
Autor:
A. Lelyakin, A. Gavrikov, Alexei Kotchourko, Vladimir Molkov, J. Yanez, Mateusz Zbikowski, A.A. Efimenko, Dmitriy Makarov
Publikováno v:
International Journal of Hydrogen Energy. 36:2613-2619
In order to ensure the public acceptance of the newly introduced technologies, such as e.g., exponentially growing hydrogen utilization, the risk management of them must be brought at the corresponding height. As a part of modern risk assessment proc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8107e3195fe543c4ff72e30a0c9e84a1
https://doi.org/10.1016/j.ijhydene.2010.04.133
https://doi.org/10.1016/j.ijhydene.2010.04.133
Publikováno v:
Journal of Hazardous Materials. 181:949-956
The LES model based on the progress variable equation and the gradient method to simulate propagation of the reaction front within the detonation wave, which was recently verified by the ZND theory, is tested in this study against two large-scale exp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0002699cce0685fb29dbebe66c708841
https://doi.org/10.1016/j.jhazmat.2010.05.105
https://doi.org/10.1016/j.jhazmat.2010.05.105
Publikováno v:
Nuclear Engineering and Technology, Vol 51, Iss 2, Pp 424-431 (2019)
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 co
Externí odkaz:
https://doaj.org/article/68d27f1352b04cdeb7988c98c472f234
Publikováno v:
Energies, Vol 13, Iss 13, p 3381 (2020)
The aim of the study was to develop deep neural network models for laminar burning velocity (LBV) calculations. The present study resulted in models for hydrogen–air and propane–air mixtures. An original data-preparation/data-generation algorithm
Externí odkaz:
https://doaj.org/article/15d6a8c89d97494ab30edb608dd44521