Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Camille Barnaud"'
Publikováno v:
Applications in Energy and Combustion Science, Vol 14, Iss , Pp 100126- (2023)
Large eddy simulation of a flameless combustion furnace is discussed with comparison against experimental measurements of the mean thermochemical quantities. The focus is on the introduction in flow simulations of complex chemistry through the traini
Externí odkaz:
https://doaj.org/article/a67404509dc749068198475843cd0e07
Publikováno v:
Proceedings of the Combustion Institute
Proceedings of the Combustion Institute, Elsevier, 2020, ⟨10.1016/j.proci.2020.06.047⟩
Proceedings of the Combustion Institute, Elsevier, 2020, ⟨10.1016/j.proci.2020.06.047⟩
International audience; A novel chemistry reduction strategy based on convolutional neural networks (CNNs) is developed and applied to direct numerical simulation (DNS) of a turbulent non-premixed flame interacting with a cooled wall. The fuel syngas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7527ecad834cb9cc2110363d5c6bfaca
https://doi.org/10.1016/j.proci.2020.06.047
https://doi.org/10.1016/j.proci.2020.06.047
Publikováno v:
Combustion and Flame
Combustion and Flame, Elsevier, 2020, 220, pp.119-129. ⟨10.1016/j.combustflame.2020.06.008⟩
Combustion and Flame, Elsevier, 2020, 220, pp.119-129. ⟨10.1016/j.combustflame.2020.06.008⟩
International audience; A chemistry reduction approach based on machine learning is proposed and applied to direct numerical simulation (DNS) of a turbulent non-premixed syngas oxy-flame interacting with a cooled wall. The training and the subsequent
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6428f31438e763b272428ba81324937e
https://hal-normandie-univ.archives-ouvertes.fr/hal-03041421/file/4-2020_Wan_Barnaud_CnF_220.pdf
https://hal-normandie-univ.archives-ouvertes.fr/hal-03041421/file/4-2020_Wan_Barnaud_CnF_220.pdf
