Numerical Study Of Heat Transfer Mechanism In Turbulent Supercritical Co2 Channel Flow

Direct numerical simulation (DNS) of supercritical CO2 turbulent channel flow has been performed to investigate the heat transfer mechanism of supercritical fluid. In the present DNS, full compressible Navier-Stokes equations and Peng-Robison state equation are solved. Due to effects of the mean density variation in the wall normal direction, mean velocity in the cooling region becomes high compared with that in the heating region. The mean width between high-and low-speed streaks near the wall decreases in the cooling region, which means that turbulence in the cooling region is enhanced and lots of fine scale eddies are created due to the local high Reynolds number effects. From the turbulent kinetic energy budget, it is found that compressibility effects related with pressure fluctuation and dilatation of velocity fluctuation can be ignored even for supercritical condition. However, the effect of density fluctuation on turbulent kinetic energy cannot be ignored. In the cooling region, low kinematic viscosity and high thermal conductivity in the low speed streaks modify fine scale structure and turbulent transport of temperature, which results in high Nusselt number in the cooling condition of the supercritical CO2.

Numerical Study of Heat Transfer Mechanism in Turbulent Supercritical CO2 Channel Flow

Direct numerical simulation (DNS) of supercritical CO2 turbulent channel flow has been performed to investigate the heat transfer mechanism of supercritical fluid. In the present DNS, full compressible Navier-Stokes equations and Peng-Robison state equation are solved. Due to effects of the mean density variation in the wall normal direction, mean velocity in the cooling region becomes high compared with that in the heating region. The mean width between high-and low-speed streaks near the wall decreases in the cooling region, which means that turbulence in the cooling region is enhanced and lots of fine scale eddies are created due to the local high Reynolds number effects. From the turbulent kinetic energy budget, it is found that compressibility effects related with pressure fluctuation and dilatation of velocity fluctuation can be ignored even for supercritical condition. However, the effect of density fluctuation on turbulent kinetic energy cannot be ignored. In the cooling region, low kinematic viscosity and high thermal conductivity in the low speed streaks modify fine scale structure and turbulent transport of temperature, which results in high Nusselt number in the cooling condition of the supercritical CO2.

DNS and LES of turbulent channel flow with hydrophobic surface

Hydrophobic surface benefits for drag reduction. Min and Kim[1] do the first Direct Numerical Simulation on drag reduction in turbulent channel flow. And Fukagata and Kasagi[2] make some theoretical analysis based on Dean[3]'s formula and some observations in the DNS results. Using their theory, they conclude that drag reduction is possible in large Reynolds number. Both Direct Numerical Simulation (DNS) and Large Eddy Simulation (LES) are performed in our research. How the LES behaving in the turbulent channel flow with hydrophobic surface is examined. Original Smagorinsky model and its Dynamical model are used in LES. The slip velocities predicted by LES using Dynamical model are in good agreement with DNS as shown in the Figure. Although the percentage of drag reduction predicted by LES shows some discrepancies, it is in the error limit for industrial flow. First order and second order moments of LES are also examined and compared with DNS's results. The first-order moments is calculated well by LES. But there are some discrepancies of second-order moments between LES and DNS. [GRAPHICS]

Mean-speed measurements in two-dimensional, incompressible, fully-developed turbulent channel flow

Mean velocity profiles were measured in the 5” x 60” wind channel of the turbulence laboratory at the GALCIT, by the use of a hot-wire anemometer. The repeatability of results was established, and the accuracy of the instrumentation estimated. Scatter of experimental results is a little, if any, beyond this limit, although some effects might be expected to arise from variations in atmospheric humidity, no account of this factor having been taken in the present work. Also, slight unsteadiness in flow conditions will be responsible for some scatter.

Irregularities of a hot-wire in close proximity to a solid boundary at low speeds were observed, as have already been found by others.

That Kármán’s logarithmic law holds reasonably well over the main part of a fully developed turbulent flow was checked, the equation u/u_t = 6.0 + 6.25 log₁₀ y^ut/v being obtained, and, as has been previously the case, the experimental points do not quite form one straight line in the region where viscosity effects are small. The values of the constants for this law for the best over-all agreement were determined and compared with those obtained by others.

The range of Reynolds numbers used (based on half-width of channel) was from 20,000 to 60,000.

The mechanism of energy loss and transition in a flow with submerged vegetation

The mechanism of energy balance in an open-channel flow with submerged vegetation was investigated. The energy borrowed from the local flow, energy spending caused by vegetation drag and flow resistance, and energy transition along the water depth were calculated on the basis of the computational results of velocity and Reynolds stress. Further analysis showed that the energy spending in a cross-section was a maximum around the top of the vegetation, and its value decreased progressively until reaching zero at the flume bed or water surface. The energy borrowed from the local flow in the vegetated region could not provide for spending; therefore, surplus borrowed energy in the non-vegetated region was transmitted to the vegetated region. In addition, the total energy transition in the cross-section was zero; therefore, the total energy borrowed from the flow balanced the energy loss in the whole cross-section. At the same time, we found that there were three effects of vegetation on the flow: turbulence restriction due to vegetation, turbulence source due to vegetation and energy transference due to vegetation, where the second effect was the strongest one. Crown Copyright (C) 2010 Published by Elsevier Ltd. All rights reserved.