Zobrazeno 1 - 10
of 74
pro vyhledávání: '"Donggang Cao"'
Autor:
Donggang Cao, Dan Michaels
Publikováno v:
AIAA Journal. 60:4532-4543
Publikováno v:
Proceedings of the Combustion Institute.
Publikováno v:
Journal of Propulsion and Power. 37:584-594
This study investigated the impact of fuel injection distribution on flame stabilization and heat release in a cavity-stabilized scramjet. Experiments were conducted using a hydrogen air heater tha...
Publikováno v:
2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS).
Autor:
Dan Michaels, Donggang Cao
Publikováno v:
AIAA Journal. 58:2165-2179
This paper presents a numerical investigation of mixing in supersonic flows using the hybrid Reynolds-averaged Navier–Stokes/large-eddy simulation. Three configurations are comparatively studied to...
Autor:
Min Chen, Ivan Petrov, Vitaly Antonenko, Yixue Hao, Wenlai Zhao, Donggang Cao, Ruslan Smeliansky
Publikováno v:
IEEE Wireless Communications. 27:100-106
Caching content on mobile devices reduces not only the transmission of backhaul links but also the latency of content acquisition. However, in the existing caching schemes, the joint effect of the caching-aware indicator of users and content dimensio
Publikováno v:
2021 IEEE International Conference on Joint Cloud Computing (JCC).
Cloud computing has been widely adopted by personal developers and enterprises because of its on-demand and elastic resource usage paradigm. Currently most cloud applications are running on one single cloud. Cloud vendors provide users a bewildering
Publikováno v:
International Journal of Hydrogen Energy. 44:28330-28341
A model scramjet engine in which the 1.0 Ma hydrogen jet mixes and reacts with the 2.0 Ma surrounding airstream is investigated using large eddy simulation. The flame structure is analyzed with a focus on the relationship between premixed/diffusion c
Publikováno v:
Proceedings of the Combustion Institute. 37:3723-3731
An experimental and computational study has been carried out for a supersonic jet flame by using OH chemiluminescence imaging, shadowgraph visualization, temperature measurement by TDLAS, pressure measurement by transducers, and large eddy simulation
Publikováno v:
ICDCS
Hyper-parameter tuning (HPT) is crucial for many machine learning (ML) algorithms. But due to the large searching space, HPT is usually time-consuming and resource-intensive. Nowadays, many researchers use public cloud resources to train machine lear
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0fa74e8fbf535333d54dc70858f9774f
http://arxiv.org/abs/2012.03576
http://arxiv.org/abs/2012.03576