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.

scholarly journals CURRENTS IN THE SURF ZONE

2000 ◽  
Vol 1 (2) ◽  
pp. 3 ◽  
Author(s):  
D. L. Inman ◽  
W. H. Quinn
Keyword(s):  
Standard Deviation ◽  
Friction Coefficient ◽  
Velocity Distribution ◽  
Surf Zone ◽  
Longshore Current ◽  
Mean Velocity ◽  
Single Observation ◽  
Bottom Currents ◽  
Longshore Currents ◽  
The Mean

Surface and bottom currents in the surf zone were measured at 15 equally spaced points along two straight beaches with approximately parallel bottom contours. The measurements showed that offshore currents predominate over onshore currents at the bottom, while at the surface there is a slight predominance in the onshore direction. With regard to the longshore component, it was found that surface and bottom currents have a similar velocity distribution. The variability of the longshore component as measured by its standard deviation is equal to or larger than the mean longshore velocity. This wide variation in longshore currents indicates the impracticability of estimating the mean velocity from a single observation of longshore current. It was found that the momentum approach to the prediction of longshore currents by Putnam, Munk and Traylor (1949) leads to useful forecasts provided the beach friction coefficient k is permitted to vary with the longshore velocity, V. The indicated relation is k~v^(-3/2).

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.

scholarly journals Catalogue of Clusters that are Members of Superclusters

1983 ◽  
Vol 104 ◽  
pp. 185-186
Author(s):  
M. Kalinkov ◽  
K. Stavrev ◽  
I. Kuneva
Keyword(s):  
Standard Deviation ◽  
Correlation Coefficient ◽  
The Other ◽  
Clusters Of Galaxies ◽  
Number Of Clusters ◽  
Abell Clusters ◽  
The Mean ◽  
Abell Cluster ◽  
Image Position

An attempt is made to establish the membership of Abell clusters in superclusters of galaxies. The relation is used to calibrate the distances to the clusters of galaxies with two redshift estimates. One is m10, the magnitude of the ten-ranked galaxy, and the other is the “mean population,” P, defined by: where p = 40, 65, 105 … galaxies for richness groups 0, 1, 2 …, and r is the apparent radius in degrees given by: The first iteration for redshift, z1, is obtained from m10 alone: The standard deviation for Eq. (1) is 0.105, the number of clusters with known velocities is 342 and the correlation coefficient between observed and fitted values is 0.921. With zi from Eq. (1), we define Cartesian galactic coordinates Xi = Rih−1 cosBi cosLi, Yi = Rih−1 cosBi sinLi, Zi = Rih−1 sinBi for each Abell cluster, i = 1, …, 2712, where Ri is the distance to the cluster (Mpc), and Ho = 100 h km s−1 Mpc−1.

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.

scholarly journals Personality and Motivation: A Correlation study among Sports Person

2019 ◽  
Vol 6 (4) ◽  
pp. 5-13
Author(s):  
M Keerthika ◽  
S Punithavathi
Keyword(s):  
Standard Deviation ◽  
Gender Difference ◽  
Correlation Coefficient ◽  
Significant Relationship ◽  
Correlation Study ◽  
Motivation Factors ◽  
The Mean ◽  
The Individual ◽  
Age Range ◽  
The Relationship

In this competitive world, it is essential to grab the sportive nature of sports persons. For different personality type of the individual the motive to engage in sports also varies from person to person. The aim of the present study is to determine the relationship between personality and motivation among sports persons and to identify the gender difference of personality and motivation factors. The sample of this study was 120 sports persons out of which 60 were males and 60 were females belonging to the age range of 18 -30 years. The mean, standard deviation and Pearson’s correlation coefficient were used for analysing the data. Results indicate that there is no significant relationship between Personality and Motivation type of sports persons.

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.

The Link between Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV) Transformations of NIR Spectra

1994 ◽  
Vol 2 (1) ◽  
pp. 43-47 ◽  
Author(s):  
M.S. Dhanoa ◽  
S.J. Lister ◽  
R. Sanderson ◽  
R.J. Barnes
Keyword(s):  
Particle Size ◽  
Standard Deviation ◽  
Correlation Coefficient ◽  
Size Effects ◽  
Scatter Correction ◽  
Standard Normal Variate ◽  
Multiplicative Scatter Correction ◽  
Standard Normal ◽  
Alternative Approaches ◽  
The Mean

We demonstrate that set-dependent multiplicative scatter correction and set-independent standard normal variate transformations of NIR spectra are linearly related as theoretically expected. It is shown that the mean and standard deviation of the set-mean-spectrum together with the correlation coefficient between each individual spectrum and set-mean-spectrum are required to link these two transformations. It is through these three quantities, that set-dependency is incorporated into spectra derived by application of multiplicative scatter correction. MSC and SNV are two alternative approaches to reduce particle size effects and they are interconvertible.

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