2010
Cavazzini, Giovanna; Pavesi, Giorgio; Ardizzon, Guido
Validation of an analysis method for particle image velocimetry of turbulent unsteady flows in turbomachinery Journal Article
In: Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, vol. 224, iss. 5, pp. 679-689, 2010, ISSN: 09576509.
Abstract | Links | BibTeX | Tags: density function, goodness-of-fit test, particle image velocimetry, probability, probability density function, statistical analysis, Turbomachinery, unsteady flow fields
@article{pop00032,
title = {Validation of an analysis method for particle image velocimetry of turbulent unsteady flows in turbomachinery},
author = {Giovanna Cavazzini and Giorgio Pavesi and Guido Ardizzon},
url = {http://pia.sagepub.com/content/224/5/679.abstract http://journals.sagepub.com/doi/10.1243/09576509JPE889},
doi = {10.1243/09576509JPE889},
issn = {09576509},
year = {2010},
date = {2010-01-01},
urldate = {2010-01-01},
journal = {Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy},
volume = {224},
issue = {5},
pages = {679-689},
publisher = {pia.sagepub.com},
abstract = {This paper presents a post-processing analysis that combines several statistical tools to validate experimental results in a particle image velocimetry analysis on unsteady flows in the turbomachinery field. The method was developed in three steps: verification of the number of acquired images, verification ofthe meaningfulness ofthe velocity averages, and identification of the zones characterized by the greatest error on the average estimation. The statistical approach allows one to identify the possible error sources, discriminating between experimental problems due to the test rig, the turbulent nature of the flow, and structures unidentifiable by a single frequency.},
keywords = {density function, goodness-of-fit test, particle image velocimetry, probability, probability density function, statistical analysis, Turbomachinery, unsteady flow fields},
pubstate = {published},
tppubtype = {article}
}
This paper presents a post-processing analysis that combines several statistical tools to validate experimental results in a particle image velocimetry analysis on unsteady flows in the turbomachinery field. The method was developed in three steps: verification of the number of acquired images, verification ofthe meaningfulness ofthe velocity averages, and identification of the zones characterized by the greatest error on the average estimation. The statistical approach allows one to identify the possible error sources, discriminating between experimental problems due to the test rig, the turbulent nature of the flow, and structures unidentifiable by a single frequency.
