Handbook, Uncertainty in Gas Turbine Measurements

Abernathy, R. B.; Powell, B. D.; Colbert, D L; Sanders, Daniel G.; Thompson, Jeffrey P.

doi:10.21236/ad0755356

Cited by 69 publications

(34 citation statements)

References 4 publications

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“…Moreover, tests were conducted by gradually decreasing the heat flux down to zero. Instruments were calibrated and the uncertainty of measured parameters evaluated according to the procedure suggested by Abernethy and Thompson [23] with results summarized in Table 2.…”

Section: Methodsmentioning

confidence: 99%

Modeling and experimental study of nucleate boiling on a vertical array of horizontal plain tubes

Ribatski

Jabardo

Silva

2008

Experimental Thermal and Fluid Science

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Section: Methodsmentioning

confidence: 99%

Modeling and experimental study of nucleate boiling on a vertical array of horizontal plain tubes

Ribatski

Jabardo

Silva

2008

Experimental Thermal and Fluid Science

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“…Temperature measurements were calibrated and the temperature uncertainty was evaluated according to the procedure suggested by Abernethy and Thompson [34]. Accounting for all instrument errors, uncertainties for the calculated parameter were estimated using the method of sequential perturbation according to Moffat [35].…”

Section: Validation Of Data Concerning the Two-phase Flow Characterizmentioning

confidence: 99%

Two-Phase Flow Characteristics During Convective Boiling of Halocarbon Refrigerants Inside Horizontal Small-Diameter Tubes

Sempértegui-Tapia

Alves

Ribatski

2013

Heat Transfer Engineering

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Two-phase flow observations were performed for The tests were run for mass velocities ranging from 100 to 600 kg/m 2 -s and saturation temperatures of 22, 31, and 41 • C. Additionally, an objective method to characterize two-phase flow patterns was developed. This method is based on simultaneous processing of signals from the following devices: a pair of diodes laser-sensors with a transparent tube between them within which the two-phase flow occurs, a micro-piezoelectric pressure transducer to determine the variation in the local pressure, and a microthermocouple within the fluid. The method was developed based on the k-means clustering algorithm, which consists of the gradual agglomeration of data of similar average characteristics. Simultaneous images of two-phase flow were obtained through a high-speed camera (10,000 frames/s) and used to identify the following flow patterns: bubbly, elongated bubbles, churn, and annular flows. The maps obtained by the objective method were compared against flow pattern results segregated based on flow visualization and a reasonable agreement was obtained between them. The vapor quality for the transition between churn and annular flow pattern decreases with decreasing the tube diameter, whereas the vapor quality for the transition between elongated bubbles and churn flow decreases with increasing tube diameter. Effects of saturation temperature and mass velocity were also verified. Additionally, elongated bubble velocities, frequencies, and lengths were determined based on the analysis of high-speed videos and the processing of signals of the diode/laser-sensor. The elongated bubble velocity was correlated as a linear function of the two-phase superficial velocity. A new image treatment method was developed to automatically identify the entrainment frequencies, which were segregated according to two groups: low and high frequency. The former group was characterized by frequencies lower than 20 Hz and the later by 50-500 Hz frequency ranges.

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“…The precision of a sensor can be given with a precision index [2], which is in essence a sample standard deviation obtained from the repeated measurements and indicates the maximum allowable error limit of the sensor. For clarity and practical significance, we define two kinds of sensor precisions as follows.…”

Section: Compatibility Of the Sensor Precisions In A Crmentioning

confidence: 99%

“…Modern power plants are monitored by hundreds of sensors and ultimate basis for the operating state assessments and the control decisions are those measurement values collected continuously during power plant operations. In spite of various rudimentary sensor value validation techniques routinely practiced in industries [1], in reality, final measurement values quite often include errors and pick up noises [2]- [4] or sensors themselves may even fail. The control actions based on such invalid or failed sensor values can lead to serious consequences such as total plant shutdowns and/or equipment damages, resulting in significant economic losses or even putting the human operators' safeties in jeopardy.…”

mentioning

confidence: 99%

Sensor Value Validation Based on Implicit Sensor Redundancy for Reliable Operation of Power Plants

Lee

Park

2005

IEEE Trans. On Energy Conversion

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This paper presents an innovative sensor value validation technique that can systematically explore the embedded sensor redundancies in power plants and utilize them in validating sensor values. The proposed technique uses the relationships existing among the sensor values, which is called causal relations (CRs) in this paper. A data structure called a sensor redundancy graph (SRG) is formulated to explore systematically the sensor redundancies existing throughout the plants. In the SRG, CRs are represented as nodes and two CRs having a common sensor are connected with an edge. The criteria are also provided for testing whether a given sensor relationship expressed as a CR holds with the actual measurement values. If the relationship holds, the necessary condition for the validities of the sensors participated in the CR is satisfied. A technique that can examine the compatibility of the precisions among the sensors in a CR for testing the usability of the given CR is also developed based on sensor precision indices and statistical inference. The proposed technique is applied to flow meter data provided from an actual super critical steam power plant and its application potential is demonstrated.Index Terms-Casual relation, sensor precision, sensor redundancy, sensor redundancy graph, sensor value validation, validity level.

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Handbook, Uncertainty in Gas Turbine Measurements

Cited by 69 publications

References 4 publications

Modeling and experimental study of nucleate boiling on a vertical array of horizontal plain tubes

Modeling and experimental study of nucleate boiling on a vertical array of horizontal plain tubes

Two-Phase Flow Characteristics During Convective Boiling of Halocarbon Refrigerants Inside Horizontal Small-Diameter Tubes

Sensor Value Validation Based on Implicit Sensor Redundancy for Reliable Operation of Power Plants

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