Eddies Stream And Convergence Zones In Turbulent Flows PdfBy Andreas H. In and pdf 29.04.2021 at 03:19 6 min read
File Name: eddies stream and convergence zones in turbulent flows .zip
The low-pressure vortex analysis is performed for the study of dynamical properties of tubular vortices in turbulence.
- Understanding vortex identifiication criteria
- Eddies, streams, and convergence zones in turbulent flows
- Eddy (fluid dynamics)
In fluid dynamics , an eddy is the swirling of a fluid and the reverse current created when the fluid is in a turbulent flow regime.
Manuscript received July 11, ; final manuscript received August 19, ; published online October 30, Editor: David Wisler. Rehill, B. October 30, January ; 1 :
Understanding vortex identifiication criteria
Department of Mechanical Engineering, Shizuoka University. In this study, the direct numerical simulation DNS for homogeneous shear turbulence in the system rotating along the streamwise direction is fulfilled. Due to the rotation effect, the turbulence energy becomes small during the initial short time and the Reynolds shear stress are suppressed more strongly with increasing the system angular velocity. Since the redistribution related to the rotation and pressure-strain terms of the Reynolds normal stress is weakened by the streamwise rotation, the anisotropy of the normal stresses is strengthened. In the strong rotation case the anisotropy of the velocity spectra is strong in the whole wavenumber region in contrast with the isotropy of the non-rotating case in the large one. From the viewpoints of the probability density function PDF for the vorticity vector angle and visualization for the vortex structure, we find that the vortex structures become large and stand in a line by the streamwise rotation axis.
Eddies, streams, and convergence zones in turbulent flows
Robert D. Xiaohua Wu, Ph. Research Council Sourabh V. Eric S. Ronald J. Sandip Ghosal Associate Professor, Dept.
Eddy (fluid dynamics)
In computational fluid dynamics, there is an inevitable trade off between accuracy and computational cost. In this work, a novel multi-fidelity deep generative model is introduced for the surrogate modeling of high-fidelity turbulent flow fields given the solution of a computationally inexpensive but inaccurate low-fidelity solver. The resulting surrogate is able to generate physically accurate turbulent realizations at a computational cost magnitudes lower than that of a high-fidelity simulation. The deep generative model developed is a conditional invertible neural network, built with normalizing flows, with recurrent LSTM connections that allow for stable training of transient systems with high predictive accuracy. The model is trained with a variational loss that combines both data-driven and physics-constrained learning.
Multi-fidelity generative deep learning turbulent flows
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