Analytical model of the mean velocity distribution in an open channel with double-layered rigid vegetation

2014 ◽  
Vol 69 ◽  
pp. 106-113 ◽  
Author(s):  
Wenxin Huai ◽  
Weijie Wang ◽  
Yang Hu ◽  
Yuhong Zeng ◽  
Zhonghua Yang
Keyword(s):  
Velocity Distribution ◽  
Analytical Model ◽  
Open Channel ◽  
Mean Velocity ◽  
Rigid Vegetation ◽  
The Mean

The Estimation of Discharge in an Experimental Open Channel

2014 ◽  
Vol 905 ◽  
pp. 369-373
Author(s):  
Choo Tai Ho ◽  
Yoon Hyeon Cheol ◽  
Yun Gwan Seon ◽  
Noh Hyun Suk ◽  
Bae Chang Yeon
Keyword(s):  
Unsteady Flow ◽  
River Discharge ◽  
Open Channel ◽  
Flow Conditions ◽  
Mean Velocity ◽  
Velocity Equation ◽  
The Mean ◽  
Loop Form

The estimation of a river discharge by using a mean velocity equation is very convenient and rational. Nevertheless, a research on an equation calculating a mean velocity in a river was not entirely satisfactory after the development of Chezy and Mannings formulas which are uniform equations. In this paper, accordingly, the mean velocity in unsteady flow conditions which are shown loop form properties was estimated by using a new mean velocity formula derived from Chius 2-D velocity formula. The results showed that the proposed method was more accurate in estimating discharge, when compared with the conventional formulas.

On Turbulent Flow Between Parallel Plates

1953 ◽  
Vol 20 (1) ◽  
pp. 109-114
Author(s):  
S. I. Pai
Keyword(s):  
Turbulent Flow ◽  
Velocity Distribution ◽  
Poiseuille Flow ◽  
The Other ◽  
Turbulent Velocity ◽  
Parallel Plates ◽  
Mean Velocity ◽  
Reynolds Equations ◽  
Mean Pressure ◽  
The Mean

Abstract The Reynolds equations of motion of turbulent flow of incompressible fluid have been studied for turbulent flow between parallel plates. The number of these equations is finally reduced to two. One of these consists of mean velocity and correlation between transverse and longitudinal turbulent-velocity fluctuations u 1 ′ u 2 ′ ¯ only. The other consists of the mean pressure and transverse turbulent-velocity intensity. Some conclusions about the mean pressure distribution and turbulent fluctuations are drawn. These equations are applied to two special cases: One is Poiseuille flow in which both plates are at rest and the other is Couette flow in which one plate is at rest and the other is moving with constant velocity. The mean velocity distribution and the correlation u 1 ′ u 2 ′ ¯ can be expressed in a form of polynomial of the co-ordinate in the direction perpendicular to the plates, with the ratio of shearing stress on the plate to that of the corresponding laminar flow of the same maximum velocity as a parameter. These expressions hold true all the way across the plates, i.e., both the turbulent region and viscous layer including the laminar sublayer. These expressions for Poiseuille flow have been checked with experimental data of Laufer fairly well. It also shows that the logarithmic mean velocity distribution is not a rigorous solution of Reynolds equations.

Accuracy Investigation of Active Flow Control Using Synthetic Jets

2015 ◽  
Vol 741 ◽  
pp. 475-480
Author(s):  
Na Gao ◽  
Chen Pu ◽  
Bao Chen
Keyword(s):  
Flow Control ◽  
Velocity Distribution ◽  
Active Flow Control ◽  
Flow Fields ◽  
Synthetic Jet ◽  
Synthetic Jets ◽  
Experimental Results ◽  
Center Line ◽  
Mean Velocity ◽  
The Mean

2nd order implicit format is implemented in the Navier-Stokes code to deal with instantaneous item unsteady flows. Three simulations are made to testify the method on flow control. First, the external flow fields of synthetic jets are simulated, the mean velocity on the center line, the jet width and velocity distribution are compared well with experimental results. Secondly, the flow fields of synthetic jet in a crossflow are simulated, orifice slot, the mean velocity on the center line and velocity distribution are compared well with experimental results. Finally, the flow control experiments on separation of airfoil are simulated, control methods include steady suction and synthetic jets. Both methods show their ability to favorably effect the flow separation, shortening the length of separation bubble and improving the pressure levels in separation areas in different degrees.

On the mechanism of wall turbulence

1982 ◽  
Vol 119 ◽  
pp. 173-217 ◽  
Author(s):  
A. E. Perry ◽  
M. S. Chong
Keyword(s):  
Velocity Distribution ◽  
Turbulence Intensity ◽  
Scaling Laws ◽  
Heated Surface ◽  
Broad Band ◽  
Wall Turbulence ◽  
Mean Velocity ◽  
Quantitative Measurements ◽  
Temperature Distributions ◽  
The Mean

In this paper an attempt is made to formulate a model for the mechanism of wall turbulence that links recent flow-visualization observations with the various quantitative measurements and scaling laws established from anemometry studies. Various mechanisms are proposed, all of which use the concept of the horse-shoe, hairpin or ‘A’ vortex. It is shown that these models give a connection between the mean-velocity distribution, the broad-band turbulence-intensity distributions and the turbulence spectra. Temperature distributions above a heated surface are also considered. Although this aspect of the work is not yet complete, the analysis for this shows promise.

VELOCITY DISTRIBUTION IN THE DUKE–RUBINSTEIN MODEL

2002 ◽  
Vol 13 (06) ◽  
pp. 829-835
Author(s):  
P. PAŚCIAK ◽  
M. J. KRAWCZYK ◽  
K. KUŁAKOWSKI
Keyword(s):  
Velocity Distribution ◽  
Electric Fields ◽  
Gel Electrophoresis ◽  
Dna Molecules ◽  
Mean Velocity ◽  
The Mean ◽  
High Fields ◽  
High Electric Fields

The Duke–Rubinstein model of gel electrophoresis is applied to calculate the velocity of DNA molecules. We have found that the velocity distribution becomes flat at high electric fields. Simultaneously, the percentage of immobile molecules increases. Effectively, the mean velocity starts to decrease at high fields. The field value, where the mean velocity is maximal, decreases with the molecule length. The results are compared with those from similar calculations obtained by Heukelum and Beljaars within the cage model.

A Comparison of Techniques for Particle Rebound Measurement in Gas Turbine Applications

2015 ◽  
Author(s):  
Jeffrey P. Bons ◽  
Rory Blunt ◽  
Steven Whitaker
Keyword(s):  
Standard Deviation ◽  
Velocity Distribution ◽  
Gas Turbine ◽  
Correlation Coefficient ◽  
Strong Dependence ◽  
Individual Particle ◽  
Aluminum Surface ◽  
Trajectory Data ◽  
Mean Velocity ◽  
The Mean

The rebound characteristics of 100–500μm quartz particles from an aluminum surface were imaged using the particle shadow velocimetry (PSV) technique. Particle trajectory data were acquired over a range of impact velocity (30–90 m/s) and impact angle (20°–90°) typical for gas turbine applications. The data were then analyzed to obtain coefficients of restitution (CoR) using four different techniques: (1) individual particle rebound velocity divided by the same particle’s inbound velocity (2) individual particle rebound velocity divided by inbound velocity taken from the mean of the inbound distribution of velocities from all particles (3) rebound velocity distribution divided by inbound velocity distribution related using distribution statistics and (4) the same process as (3) with additional precision provided by the correlation coefficient between the two distributions. It was found that the mean and standard deviation of the CoR prediction showed strong dependence on the standard deviation of the inbound velocity distribution. The two methods that employed statistical algorithms to account for the distribution shape [methods (3) and (4)] actually overpredicted mean CoR by up to 6% and CoR standard deviation by up to 100% relative to method (1). The error between the methods is shown to be a strong (and linear) function of correlation coefficient, which is typically 0.2–0.6 for experimental CoR data. Non-Gaussianity of the distributions only accounts for up to 1% of the error in mean CoR, and this largely from the non-zero skewness of the inbound velocity distribution. Particle rebound data acquired using field average techniques that do not provide an estimate of correlation coefficient are most accurately evaluated using method (2). Method (3) can be used with confidence if the standard deviation of the inbound velocity distribution is less than 10% of the mean velocity, or if a linear correction based on an assumed correlation coefficient is applied.

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