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Record W2034656513 · doi:10.1115/icone10-22538

In-Situ Calibration for Feedwater Flow Measurement

2002· article· en· W2034656513 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue10th International Conference on Nuclear Engineering, Volume 1 · 2002
Typearticle
Languageen
FieldEngineering
TopicFlow Measurement and Analysis
Canadian institutionsGroup for Research in Decision Analysis
Fundersnot available
KeywordsBoiler feedwaterUltrasonic flow meterFlow measurementPipingUltrasonic sensorInstrumentation (computer programming)Flow (mathematics)Flow velocityMarine engineeringCalibrationFlow conditioningEnvironmental scienceEngineeringMechanical engineeringComputer scienceAcousticsMechanicsTurbulencePhysics

Abstract

fetched live from OpenAlex

With the approval by the Nuclear Regulatory Commission (NRC), of the Appendix K power uprates, it has become important to provide an accurate measurement of the feedwater flow. Failure to meet documented requirements can now more easily lead to plant operations above their analyzed safety limits. Thus, the objective of flow instrumentation used in Appendix K uprates, becomes one of providing precise measurements of the feedwater mass flow that will not allow the plant to be overpowered, but will still assure that maximum licensed thermal output is achieved. The NRC has licensed two technologies that meet these standards. Both are based on ultrasonic measurements of the flow. The first of these technologies, which is referred to as transit-time, relies on the measurement of differences in time for multiple ultrasonic beams to pass up and downstream in the fluid stream. These measurements are then coupled with a numerical integration scheme to compensate for distortions in the velocity profile due to upstream flow disturbances. This technology is implemented using a spool piece that is inserted into the feedwater pipe. The second technology relies on the measurement of the velocity of eddies within the fluid using a numerical process called cross-correlation. This technology is implemented by attaching the ultrasonic flow meter to the external surface of the pipe. Because of the ease in installation, for atypical situations, distortions in the velocity profile can be accounted for by attaching a second ultrasonic flow meter to the same pipe or multiple meters to a similar piping configuration, where the flow is fully developed. The additional meter readings are then used for the calibration of the initial set-up. Thus, it becomes possible to provide an in-situ calibration under actual operating conditions that requires no extrapolation of laboratory calibrations to compensate for distortions in the velocity profile. This paper will focus on the cross-correlation method of flow measurement, starting with the theoretical bases for the velocity profile correction factor and its reliance on only the Reynolds number to produce an accurate measurement of the flow, when the flow is fully developed. The method of laboratory calibration and the verification of these calibrations under actual plant operating conditions will be discussed. This will be followed by a discussion of how this technology is being used today to support the Appendix K uprates. Various examples will be presented of piping configurations, where in-situ calibrations have or will be used to provide an accurate measurement of the feedwater flow at a specific location.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.718
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.042
GPT teacher head0.210
Teacher spread0.167 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it