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Novel techniques for the analysis of dynamic pressure in penstocks

2021· article· en· W3169146648 on OpenAlexaff
Fabien Chevillotte, Goran Pavić, Guillaume Dubois, Gilles Proulx, Martin Gagnon

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2021
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsPenstockStrain gaugePressure measurementDynamic pressureLine (geometry)Pressure sensorHydropowerElectrical conduitVibrating wireAcousticsPower (physics)EngineeringStructural engineeringElectrical engineeringMechanical engineeringMechanicsPhysicsMathematics

Abstract

fetched live from OpenAlex

Abstract This paper focuses on the analysis of dynamic pressure fluctuations in hydro-electric power plants. Pressure fluctuations are usually measured with remote sensors and the dynamic behaviour of the remote tubing line modifies the readings of dynamic pressure and thus introduces a bias. A method to characterize the remote tubing line and to correct the actual pressure is proposed. The corrected measurements are compared with those obtained using intrusive flush-mounted sensors as well as non-intrusive sensors (PVDF wires and strain gauges). Then, the technique to separate the forward and backward components of the obtained pressure pulsations is outlined. It is further shown how the pulsations and the hoop stresses can be reconstructed along the penstock. Finally, measurements carried out in a hydropower plant are presented to demonstrate the proposed techniques applicability for the analysis of dynamic pressure in a penstock. The results show that a pressure profile can be mapped along the penstock length which allows a comparison with the direct pressure sensor measurements.

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.

How this classification was reachedexpand

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.667
Threshold uncertainty score0.169

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)0.0000.000

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 head0.008
GPT teacher head0.190
Teacher spread0.182 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2021
Admission routes1
Has abstractyes

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