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research-article

A METHODOLOGY FOR CHARACTERIZING REPRESENTATIVENESS UNCERTAINTY IN PERFORMANCE INDICATOR MEASUREMENTS OF POWER GENERATING SYSTEMS

[+] Author and Article Information
Uuganbayar Otgonbaatar

ASME membership-N.A, Nuclear Science and Engineering Department, Massachusetts Institute of Technology (Former), 77 Massachusetts avenue, Cambridge, MA, 02142 (Former), Exelon Corporation (Current), 701 9th Street N.W, Washington, DC, 20001
uuganbayar.otgonbaatar@exeloncorp.com

Emilio Baglietto

ASME membership-N.A, Nuclear Science and Engineering Department, Massachusetts Institute of Technology, 77 Massachusetts avenue, Cambridge, MA, 02142
emiliob@mit.edu

Yvan Caffari

ASME membership-N.A, Electricité de France, EDF SA
yvan.caffari@ef.fr

Neil Todreas

ASME membership-Fellow, American Society of Mechanical Engineers, 1983, Nuclear Science and Engineering Department, Massachusetts Institute of Technology, 77 Massachusetts avenue, Cambridge, MA, 02142
todreas@mit.edu

Giancarlo Lenci

ASME membership-N.A, Nuclear Science and Engineering Department, Massachusetts Institute of Technology (Former), 77 Massachusetts avenue, Cambridge, MA, 02142 (Former), Dominion Engineering, Inc (Current), 12100 Sunrise Valley Drive, Suite 220, Reston, VA 20191
glenci@domeng.com

1Corresponding author.

ASME doi:10.1115/1.4041687 History: Received November 14, 2017; Revised October 02, 2018

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 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 non-parametric 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.

Copyright (c) 2018 by ASME
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