PIV measurements in near-wake turbulent regions

2018 ◽  
Vol 32 (12n13) ◽  
pp. 1840026 ◽  
Author(s):  
Wei-Cheng Chen ◽  
Keh-Chin Chang
Keyword(s):  
Particle Image Velocimetry ◽  
Particle Image ◽  
Interrogation Window ◽  
Gray Level ◽  
Flow Properties ◽  
Near Wake ◽  
Piv Measurements ◽  
Optical Diagnostic ◽  
Piv Measurement ◽  
Image Velocimetry

Particle image velocimetry (PIV) is a non-intrusive optical diagnostic and must be made of the seedings (foreign particles) instead of the fluid itself. However, reliable PIV measurement of turbulence requires sufficient numbers of seeding falling in each interrogation window of image. A gray-level criterion is developed in this work to check the attainment of statistically stationary status of turbulent flow properties. It is suggested that the gray level of no less than 0.66 is used as the threshold for reliable PIV measurements in the present near-wake turbulent regions.

Experimental Measurements in Near-Wall Regions by Particle Image Velocimetry (PIV)

2014 ◽  
Author(s):  
Puxuan Li ◽  
Steve J. Eckels ◽  
Garrett W. Mann ◽  
Ning Zhang
Keyword(s):  
Particle Image Velocimetry ◽  
Particle Image ◽  
Particle Density ◽  
Piv Measurements ◽  
Experimental Fluid Mechanics ◽  
Micro Piv ◽  
Piv Measurement ◽  
Image Velocimetry ◽  
Eddy Structures ◽  
Near Wall

The current study investigates the flow field near a surface with a micro-PIV system using a square tube to enhance optical access. Measurements of velocity fields and eddy structures near the wall of tubes are important to the design of in-tube surface geometries. In experimental fluid mechanics, particle image velocimetry (PIV) is now a common way to measure velocity. However, PIV measurements near walls require efforts to deal with low particle density, high shear gradient and wall reflection. The current paper discusses a PIV measurement technique utilized to observe flow dynamics in near-wall regions. PIV uncertainty analysis is discussed in this study. The experimental results are compared with previous results for validation.

Time-Resolved Particle Image Velocimetry (PIV) Measurements in a Radial Diffuser Pump

2009 ◽  
Author(s):  
Jianjun Feng ◽  
Friedrich-Karl Benra ◽  
Hans Josef Dohmen
Keyword(s):  
Particle Image Velocimetry ◽  
Particle Image ◽  
Time Resolved ◽  
Piv Measurements ◽  
Part Load ◽  
Channel Characteristic ◽  
Image Velocimetry ◽  
Pump Stage ◽  
Low Specific Speed ◽  
Load Conditions

The truly time-variant unsteady flow in a low specific speed radial diffuser pump stage has been investigated by time-resolved Particle Image Velocimetry (PIV) measurements. The measurements are conducted at the midspan of the blades for the design condition and also for some severe part-load conditions. The instantaneous flow fields among different impeller channels are analyzed and compared in detail, and more attention has been paid to flow separations at part-load conditions. The analysis of the measured results shows that the flow separations at two adjacent impeller channels are quite different at some part-load conditions. The separations generally exhibit a two-channel characteristic.

A Method of Measuring Turbulent Flow Structures With Particle Image Velocimetry and Incorporating Into Boundary Conditions of Large Eddy Simulations

2018 ◽  
Vol 140 (7) ◽  
Author(s):  
Puxuan Li ◽  
Steve J. Eckels ◽  
Garrett W. Mann ◽  
Ning Zhang
Keyword(s):  
Particle Image Velocimetry ◽  
Large Eddy Simulation ◽  
Boundary Conditions ◽  
Particle Image ◽  
Advantages And Disadvantages ◽  
Eddy Simulation ◽  
Piv Measurement ◽  
Image Velocimetry ◽  
Large Eddy ◽  
Inlet Conditions

The setup of inlet conditions for a large eddy simulation (LES) is a complex and important problem. Normally, there are two methods to generate the inlet conditions for LES, i.e., synthesized turbulence methods and precursor simulation methods. This study presents a new method for determining inlet boundary conditions of LES using particle image velocimetry (PIV). LES shows sensitivity to inlet boundary conditions in the developing region, and this effect can even extend into the fully developed region of the flow. Two kinds of boundary conditions generated from PIV data, i.e., steady spatial distributed inlet (SSDI) and unsteady spatial distributed inlet (USDI), are studied. PIV provides valuable field measurement, but special care is needed to estimate turbulent kinetic energy and turbulent dissipation rate for SSDI. Correlation coefficients are used to analyze the autocorrelation of the PIV data. Different boundary conditions have different influences on LES, and their advantages and disadvantages for turbulence prediction and static pressure prediction are discussed in the paper. Two kinds of LES with different subgrid turbulence models are evaluated: namely dynamic Smagorinsky–Lilly model (Lilly model) and wall modeled large eddy simulation (WMLES model). The performances of these models for flow prediction in a square duct are presented. Furthermore, the LES results are compared with PIV measurement results and Reynolds-stress model (RSM) results at a downstream location for validation.

scholarly journals Data assimilation method to de-noise and de-filter particle image velocimetry data

2019 ◽  
Vol 877 ◽  
pp. 196-213 ◽  
Author(s):  
Jurriaan J. J. Gillissen ◽  
Roland Bouffanais ◽  
Dick K. P. Yue
Keyword(s):  
Particle Image Velocimetry ◽  
Data Assimilation ◽  
Particle Image ◽  
Particle Image Velocimetry Data ◽  
Reconstruction Error ◽  
Integration Time ◽  
Piv Measurement ◽  
Image Velocimetry ◽  
The Difference ◽  
Filter Particle

We present a variational data assimilation method in order to improve the accuracy of velocity fields $\tilde{\boldsymbol{v}}$, that are measured using particle image velocimetry (PIV). The method minimises the space–time integral of the difference between the reconstruction $\boldsymbol{u}$ and $\tilde{\boldsymbol{v}}$, under the constraint, that $\boldsymbol{u}$ satisfies conservation of mass and momentum. We apply the method to synthetic velocimetry data, in a two-dimensional turbulent flow, where realistic PIV noise is generated by computationally mimicking the PIV measurement process. The method performs optimally when the assimilation integration time is of the order of the flow correlation time. We interpret these results by comparing them to one-dimensional diffusion and advection problems, for which we derive analytical expressions for the reconstruction error.

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