Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Zhideng Zhou"'
Publikováno v:
AIP Advances, Vol 12, Iss 12, Pp 125304-125304-9 (2022)
A super-resolution reconstruction model for the subgrid scale (SGS) turbulent flow field in large-eddy simulation (LES) is proposed, and it is called the meta-learning deep convolutional neural network (MLDCNN). Direct numerical simulation (DNS) data
Externí odkaz:
https://doaj.org/article/178abc78e88240ac9d236023ae53e093
Publikováno v:
Theoretical and Applied Mechanics Letters, Vol 12, Iss 1, Pp 100318- (2022)
Flow around a real-life underwater vehicle often happens at a high Reynolds number with flow structures at different scales from the boundary layer around a blade to that around the hull. This poses a great challenge for large-eddy simulation of an u
Externí odkaz:
https://doaj.org/article/c0967fceaacf4489a98e9d090980603a
Publikováno v:
AIP Advances, Vol 12, Iss 1, Pp 015002-015002-7 (2022)
The dispersion of cough-generated droplets from a person going up- or downstairs was investigated through a laboratory experiment in a water tunnel. This experiment was carried out with a manikin mounted at inclination angles facing the incoming flow
Externí odkaz:
https://doaj.org/article/cf20de8e478d49389c7d0bb661acf33d
Publikováno v:
European Journal of Mechanics - B/Fluids. 94:350-365
In this work, we propose a single formula for the law of the wall, which is dubbed as the logarithmic-exponential (LOG-EXP) formula, for predicting the mean velocity profile in different regions near the wall. And then a feedforward neural network (F
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::30e6c3d34268a05c0dd4927853647f4d
https://doi.org/10.1016/j.euromechflu.2022.03.013
https://doi.org/10.1016/j.euromechflu.2022.03.013
In this work, we carried out roughness-resolved direct numerical simulations of cube-roughened turbulent channel flows to investigate the effects of aligned and staggered arrangements, element spacings, and element orientations on the statistics of r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::59289c5a5d9fc47027a5bb778b21e36a
https://doi.org/10.21203/rs.3.rs-2016750/v1
https://doi.org/10.21203/rs.3.rs-2016750/v1
Publikováno v:
Sustainability; Volume 15; Issue 6; Pages: 5139
In this study, large-eddy simulation was employed to investigate the influence of the forest canopy on wind turbine wakes. Nine forest case studies were carried out with different vertical distributions of leaf area density (LAD) and values of leaf a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e5d9f66995c54ddb2b0ffa8789032388
https://doi.org/10.3390/su15065139
https://doi.org/10.3390/su15065139
Publikováno v:
AIP Advances
AIP Advances, Vol 12, Iss 1, Pp 015002-015002-7 (2022)
AIP Advances, Vol 12, Iss 1, Pp 015002-015002-7 (2022)
The dispersion of cough-generated droplets from a person going up- or downstairs was investigated through a laboratory experiment in a water tunnel. This experiment was carried out with a manikin mounted at inclination angles facing the incoming flow
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1eb43627e05de4c42a758a29fe098f93
https://pubmed.ncbi.nlm.nih.gov/35003882
https://pubmed.ncbi.nlm.nih.gov/35003882
Publikováno v:
Ocean Engineering. 266:113107
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cd2052d8bed394d5f38f7f4194155d4b
https://doi.org/10.1016/j.oceaneng.2022.113107
https://doi.org/10.1016/j.oceaneng.2022.113107
Publikováno v:
Physical Review Fluids. 6
A data-driven wall model for turbulent flows over periodic hills is developed using a feedforward neural network and wall-resolved large-eddy simulation data. The developed wall model employs wall-normal distance, near-wall velocities, and pressure g
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e8fb06b406648f9fa4041fdcee35f7a7
https://doi.org/10.1103/physrevfluids.6.054610
https://doi.org/10.1103/physrevfluids.6.054610
Publikováno v:
Computers & Fluids. 239:105382
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::60b86b819ce6112026313039432ab3f9
https://doi.org/10.1016/j.compfluid.2022.105382
https://doi.org/10.1016/j.compfluid.2022.105382