969 resultados para Flow Channel


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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.

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The velocity distribution between two sidewalls is; M-shaped for the MHD channel flows with rectangular cross section and thin conducting walls in a strong transverse magnetic field. Assume that the dimensionless numbers R(m) much less than 1, M, N much greater than 1, and sigma

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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.

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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]

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In a slow flow, on a smooth uniform substratum, a limited bed allows the existence of currents slow enough for benthic invertebrates. These conditions rarely occur naturally. The investigations carried out in this work aimed, on an intermediary scale, to define the influence of irregularities in the substratum on flow near the bottom. The substrata used were made of glass marbles. The investigations were carried out in a transparent channel of 70 cm in length and a rectangular section 10 x 5 cm. The data was analysed to study the general evolution of flow in terms of average speeds and the appearance of the turbulence near the bottom.

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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/ut = 6.0 + 6.25 log10 yut/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.

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The viscosity-temperature relation is determined for the water models SPC/E, TIP4P, TIP4P/Ew, and TIP4P/2005 by considering Poiseuille flow inside a nano-channel using molecular dynamics. The viscosity is determined by fitting the resulting velocity profile (away from the walls) to the continuum solution for a Newtonian fluid and then compared to experimental values. The results show that the TIP4P/2005 model gives the best prediction of the viscosity for the complete range of temperatures for liquid water, and thus it is the preferred water model of these considered here for simulations where the magnitude of viscosity is crucial. On the other hand, with the TIP4P model, the viscosity is severely underpredicted, and overall the model performed worst, whereas the SPC/E and TIP4P/Ew models perform moderately.