Dataset: Particle Image Velocimetry (PIV) Data

Name: Particle image velocimetry (PIV) data for 6 representative cases of quagga mussel filter feeding activity collected during a benthic deployment from the R/V Neeskay on Aug. 2- Aug. 9, 2017.
Published: 2018-03-06
Keywords: biota, oceans

Cite This Dataset

Spatial Extent: N:43.09503 E:-87.77119 S:43.09503 W:-87.77119

Temporal Extent: 2017-08-02 - 2017-08-09

Project:

Collaborative Research: Regulation of plankton and nutrient dynamics by hydrodynamics and profundal filter feeders (Filter Feeders Physics and Phosphorus)

Principal Investigator:

Harvey Bootsma (University of Wisconsin, UW-Milwaukee)

Co-Principal Investigator:

Qian Liao (University of Wisconsin, UW-Milwaukee)

BCO-DMO Data Manager:

Megan Switzer (Woods Hole Oceanographic Institution, WHOI BCO-DMO)

Version:

Version Date:

2018-03-06

Restricted:

Validated:

Yes

Data URL:

https://www.bco-dmo.org/dataset/729461/data

Particle image velocimetry (PIV) data for 6 representative cases of quagga mussel filter feeding activity collected during a benthic deployment from the R/V Neeskay on Aug. 2- Aug. 9, 2017.

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Description

Instantaneous flow velocity distribution on a 2D (x-z) plane, where x and z denote the horizontal and vertical directions, respectively. The velocity field was measured by an underwater PIV system deployed in Lake Michigan to investigate turbulent flows and mixing above the lake bottom, as a result of interaction between lake currents and filter feeding activities of quagga mussels.

Please see supporting document Bio-mixing-PIV.pdf for a description of the data collection and preliminary results.

This dataset contains 6 of the 64 total image sets collected during the deployment. The 6 image sets are labeled as Cases A-F. The chosen sets represent a variety of hydrodynamic conditions. Sets A,B, and C represent the flow fields when the mussels were not actively feeding. Sets D,E, and F represent the flow fields when the mussels were actively feeding.

The datasets for each case are served with BCO-DMO as plain text files with fields for Case (A-F), Time (s), x coordinate (cm), z coordinate (z), u(m/s), and v (m/s). Each file is approximately 32.1 MB.

Links to the MatLab (2015b) version of these data are listed below. Each file is approximately 3.5 MB. Velocity measurements are listed in a 2D matrix format. Also listed are MatLab programs to read the data files and plot flow profiles.

Case A
Case B
Case C
Case D
Case E
Case F
MatLab file descriptions

Plot profiles
Plot velocity fields
Convert to CSV

Acquisition Description

Excerpt from supporting document Bio-mixing-PIV.pdf:

"An underwater PIV system had been developed (Liao et al., 2009, 2014; Wang et al., 2013) and it was upgraded for this project to measure 2D velocity field immediately above the bottom of Lake Michigan at a mid-depth (latitude = 43.09503 and longitude = -87.77119, water depth = 55 meters) location, which is full covered by quagga mussels. The autonomous PIV (cf. Fig 1) is self-contained, including a laser unit that generated a light sheet by scanning the laser beam, a digital camera unit that captures images of particles illuminated by the laser “sheet”. Laser and camera are synchronized by a signal generator and images are captured by a single board computer that runs a Linux Arm OS. The system was programed to turn on 17 times per day and captures 6,000 image pairs in 10 minutes for each duty cycle. The battery capacity (15 AH 12V) allows 7 days of operation with this duty cycle configuration. The system was deployed untethered between Aug 2 and Aug 9 2017 for seven days. Image acquired covered an area of 8 × 9 cm2 (width × height). PIV interrogation resolved a 2D velocity field on a 41 × 35 mesh with a spatial resolution of 1.7 mm."

Processing Description

PIV interrogation analysis was conducted using a house-developed Matlab+C code. The interrogation algorithm followed that by Liao and Cowen (2005). PIV interrogation resolved the 2D velocity field on a 41 × 35 mesh with a spatial resolution of 1.7 mm. The interrogation sub-window was 64 × 64 pixels (3.4 × 3.4 mm), with a 50% overlap. PIV results were post-processed, including a local median filter (5 by 5 mesh grids) to remove strayed vectors and replaced with the local median value. Dissipation rate was estimated with a “direct” method based on PIV measurements (Wang and Liao, 2016)

References:

Liao, Q., Cowen, E. A. (2005). An efficient anti-aliasing spectral continuous window shifting technique for PIV. Experiments in Fluids, 38(2), 197-208. doi: 10.1007/s00348-004-0899-7

Wang B., and Liao, Q. (2016) Field observations of turbulent dissipation rate profiles immediately below the air-water interface. Journal of Geophysical Research: Ocean. 121(6), 4377-4391. doi: 10.1002/2015JC011512

Note: Files processed using MatLab 2015b.

More Information about this dataset

Funding Sources

Funding Source	Award Number
NSF Division of Ocean Sciences	OCE-1658390

Deployments

Deployment	Synonyms	Start Date	Platform	Investigator
Neeskay_Cruises_2017	Station AT55 Neeskay Cruise 1	2017-08-01	R/V Neeskay	Harvey Bootsma (Chief Scientist)	data info

Instruments

Particle Image Velocimetry (PIV) system

Supplied Name:

Supplied Description:

Instrument Type

Generic Name: Particle Image Velocimetry (PIV) system

Generic Description:

Measures 2D velocity flow fields, usually by scanning particles with a laser beam and capturing images of the illuminated particles.

Parameters

Supplied Name	Supplied description	Supplied Units	Standard Name
Time	500 measurements were made in 1 second increments	seconds	unknown
x	horizontal coordinate	cm	unknown
z	vertical coordinate	cm	unknown
u	instantaneous horizontal velocity field	cm/s	unknown
w	instantaneous vertical velocity field	cm/s	unknown

Database

Geospatial Access

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Dataset: Particle Image Velocimetry (PIV) Data