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Research Papers

A Methodology for Characterizing Representativeness Uncertainty in Performance Indicator Measurements of Power Generating Systems

[+] Author and Article Information
U. Otgonbaatar

Mem. ASME
Nuclear Science and Engineering Department,
Massachusetts Institute of Technology (Former),
77 Massachusetts Avenue,
Cambridge, MA 02142
e-mail: uuganbayar.otgonbaatar@exeloncorp.com

E. Baglietto

Mem. ASME
Nuclear Science and Engineering Department,
Massachusetts Institute of Technology,
77 Massachusetts Avenue,
Cambridge, MA 02142
e-mail: emiliob@mit.edu

Y. Caffari

Fellow ASME
Electricité de France,
EDF SA, Paris 75008, France
e-mail: yvan.caffari@edf.fr

N. E. Todreas

Fellow ASME
Nuclear Science and Engineering Department,
Massachusetts Institute of Technology,
77 Massachusetts Avenue,
Cambridge, MA 02142
e-mail: todreas@mit.edu

G. Lenci

Mem. ASME
Nuclear Science and Engineering Department,
Massachusetts Institute of Technology,
77 Massachusetts Avenue,
Cambridge, MA 02142
e-mail: glenci@domeng.com

1Present address: Exelon Corporation, 701 9th Street N.W, Washington, DC 20001.

2Present address: Dominion Engineering, Inc., 12100 Sunrise Valley Drive, Suite, 220, Reston, VA 20191.

Manuscript received November 14, 2017; final manuscript received October 2, 2018; published online November 5, 2018. Assoc. Editor: Yassin A. Hassan.

J. Verif. Valid. Uncert 3(2), 021005 (Nov 05, 2018) (10 pages) Paper No: VVUQ-17-1035; doi: 10.1115/1.4041687 History: Received November 14, 2017; Revised October 02, 2018

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.

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Figures

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Fig. 1

Schematic for the true value, reference true value, and measurement value. The dimension of the vertical axis is the unit in which the performance indicator is measured (e.g., kg/s for mass flow rate measurement).

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Fig. 2

Schematic for the measurement, simulated measurement, and the reference true value for both experiment and simulation

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Fig. 3

Schematic of the methodology framework to quantify representativeness uncertainty using a simulation. The Gaussian shape for random uncertainty distribution is chosen only for illustration purpose.

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Fig. 4

Illustration of two cases of the corrected measurement value Mc: (a) shows Mc sufficiently separated from M and will likely pass the test for the null hypothesis H0 and (b) shows a corrected measurement value that is not sufficiently separated from the original measurement value M due to large modeling and/or validation uncertainty and hence will likely fail the null hypothesis H0

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Fig. 9

Combined discretization and sampling uncertainties. The histograms show the bootstrap resampled results. The blue histogram is barely visible since the sampling uncertainty is negligibly small in this simulation.

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Fig. 10

Summary of unsteady RANS simulation results benchmarked against ISO standard

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Fig. 5

Geometry of the piping configuration of the case study

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Fig. 6

Input uncertainty calculation using a combination of sensitivity coefficient

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Fig. 7

Histogram in green shows a resampled bootstrap distribution constructed from the grid convergence data

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Fig. 8

Result for the pressure drop across an orifice plate of a RANS simulation using the cubic k–ε turbulence model. The histogram represents the distribution of time-dependent simulation result plotted as a function of time. The horizontal line is the time-average of the simulation result. The iteration uncertainty is included in each time-step of the simulation and quantified as part of the sampling uncertainty.

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Fig. 11

Representativeness uncertainty is the difference between the reference true value and the measurement value given by the orifice plate method

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Fig. 12

CFD model for the orifice plate flow measurement method in the experimental configuration. Input, discretization, and sampling uncertainties are each quantified using the bootstrap method in Sec. 4.2.

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Fig. 13

Combined methodology schematic using the nonparametric bootstrap method

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