Issue |
E3S Web Conf.
Volume 345, 2022
XXV Biennial Symposium on Measuring Techniques in Turbomachinery (MTT 2020)
|
|
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Article Number | 02001 | |
Number of page(s) | 10 | |
Section | Methods | |
DOI | https://doi.org/10.1051/e3sconf/202234502001 | |
Published online | 29 March 2022 |
A probabilistic uncertainty evaluation method for turbomachinery probe measurements
1 ETH, Turbomachinery Laboratory, Zurich, Switzerland
2 AUTh, Department of Mechanical Engineering, Laboratory of Fluid Mechanics and Turbomachinery, Thessaloniki, Greece
* Thomas Behr: thomas.behr@ch.abb.com
† Anestis I. Kalfas: akalfas@auth.gr
‡ Reza S. Abhari: abhari@lec.mavt.ethz.ch
The paper presents a probabilistic uncertainty evaluation method described in the Guide to the Expression of Uncertainty in Measurement (GUM) [1] and its application to the field of Turbomachinery probe measurement techniques. All sources of uncertainties contributing to a measurement result are expressed in terms of probability distributions. Consequently, the overall standard uncertainty of the result can be calculated using the Gaussian error propagation formula. The result of the uncertainty evaluation yields the most probable value of the measurand and describes its distribution in terms of standard or extended uncertainties. A pneumatic five-hole-probe measurement technique has been chosen to show the principle of the probabilistic uncertainty evaluation method. The complete signal chain including the probe calibration, the modeling and the application in the turbine has been included in the analysis. The overall uncertainties of the measured flow angles and flow total and static pressures are presented as a function of the flow Mach number. In addition, the contribution of the individual sources of uncertainty to the overall standard uncertainty is shown. Based on this break down of uncertainties optimization options of the measurement chain are suggested.
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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