The group members presented at the APS Division of Fluid Dynamics 2020.
At the APS Division of Fluid Dynamics during Nov 22-24, 2020, the invited talks covered topics ranging from “Flow in the Sun” to “Fluid Mechanics of Infectious Disease” to “Kitchen Flows”. All abstract submitters shared results in the virtual format: uploaded on-demand talks, posters, slides, preprints, animations, etc. to connect with their abstract. The scientific program included four award lectures, along with twelve invited lectures, and four minisymposia sessions. Each year the APS Division of Fluid Dynamics awards the Fluid Dynamics Prize, the Francois N. Frenkiel Award, the Andreas Acrivos Dissertation Award, and the Stanley Corrsin Award.
The 39th Annual Gallery of Fluid Motion was held as part of the meeting. A series of high-resolution videos and posters that can be watched or viewed throughout the conference. The posters or videos submitted by the community illustrating the science and also the beauty of fluid motion. Both computational and experimental entries are included.
Six numbers of our group submitted their material online. Dr Xiaoning Wang shared a report named "Kinetic energy transfer in compressible anisotropic homogeneous turbulence". Dr Jian Teng shared a report named "Effect of flow topology on enstrophy production and scalar structures in chemically reacting compressible isotropic turbulence". Dr Qinmin Zheng shared a report named "Transfer of internal energy fluctuations in compressible isotropic turbulence with vibrational nonequilibrium". PHD student Tengfei Luo shared a report named "Expansion-compression motions in the single-mode Rayleigh-Taylor instability". PHD student Zelong Yuan shared a report named "Modeling subgrid-scale stress by deconvolutional neural networks in large eddy simulation of turbulence". PHD student Dehao Xu shared a report named "Effect of isothermal wall condition on the inter-scale kinetic energy transfer in hypersonic boundary layer".
By Zelong Yuan, 2020
- Zelong Yuan, 2020, Modeling subgrid-scale stress by deconvolutional neural networks in large eddy simulation of turbulence