A Methodology for Characterizing Representativeness Uncertainty in Performance Indicator Measurements of Power Generating Systems
Name
vvuq_003_02_021005.pdf
Description
Published version
Size
1.75 MB
Format
Adobe PDF
Checksum (MD5)
6ffd2526a86c6f5a3d03b1bdb9bef938
Author(s)
Otgonbaatar, U.
Baglietto, Emilio
Todreas, N. E.
Lenci, G.
Date Issued
June 1, 2018
Journal
Journal of verification, validation, and uncertainty quantification
Publisher
ASME International
Citation
Otgonbaatar, U. et al. "A Methodology for Characterizing Representativeness Uncertainty in Performance Indicator Measurements of Power Generating Systems." Journal of verification, validation, and uncertainty quantification 3 (2018): 021005 © 2019 The Author(s)
Version
Final published version
Abstract
In this work, a general methodology and innovative framework to characterize and quantify representativeness uncertainty of performance indicator measurements of power generation systems is proposed. The representativeness uncertainty refers to the difference between a measurement value of a performance indicator quantity and its reference true value. It arises from the inherent variability of the quantity being measured. The main objectives of the methodology are to characterize and reduce the representativeness uncertainty by adopting numerical simulation in combination with experimental data and to improve the physical description of the measurement. The methodology is applied to an industrial case study for demonstration. The case study involves a computational fluid dynamics (CFD) simulation of an orifice plate-based mass flow rate measurement, using a commercially available package. Using the insight obtained from the CFD simulation, the representativeness uncertainty in mass flow rate measurement is quantified and the associated random uncertainties are comprehensively accounted for. Both parametric and nonparametric implementations of the methodology are illustrated. The case study also illustrates how the methodology is used to quantitatively test the level of statistical significance of the CFD simulation result after accounting for the relevant uncertainties.
MIT Department
Massachusetts Institute of Technology. Department of Nuclear Science and Engineering
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1115/1.4041687
