A Methodology for Characterizing Representativeness Uncertainty in Performance Indicator Measurements of Power Generating Systems
| Autor: | Giancarlo Lenci, Emilio Baglietto, Neil E. Todreas, Uuganbayar Otgonbaatar, Yvan Caffari |
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| Rok vydání: | 2018 |
| Předmět: | |
| Zdroj: | Journal of Verification, Validation and Uncertainty Quantification. 3 |
| ISSN: | 2377-2166 2377-2158 |
| DOI: | 10.1115/1.4041687 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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