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Record W2216011415 · doi:10.1115/power2015-49124

PIV Characterization of the Air Flow in a Scale Model of a Hydrogenerator

2015· article· en· W2216011415 on OpenAlexafffundabout
Emmanuel Bach, Laurent Mydlarski, Federico Torriano, Jean-Philippe Charest-Fournier, Hubert Sirois, Jean-François Morissette, C. Hudon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsMcGill UniversityHydro-Québec
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsComputational fluid dynamicsParticle image velocimetryGenerator (circuit theory)EnclosureFlow (mathematics)HydroelectricityCalibrationThermalMarine engineeringMechanical engineeringMechanicsComputer sciencePower (physics)EngineeringElectrical engineeringMeteorologyPhysicsTurbulence

Abstract

fetched live from OpenAlex

In hydroelectric power plants, generators are essential components and, like all machines, generate heat due to losses. The most common way to evacuate this heat is by circulating a cooling fluid (generally air) through the generator’s components. Due to the geometrical complexity, it is quite challenging to simulate the flow to predict the cooling in a generator, and in-situ measurements are costly and difficult to perform due to the limited access. For this reason, a 1:4 scale model of a hydroelectric generator was built at the research institute of Hydro-Québec (IREQ). In this paper, particle image velocimetry (PIV) measurements of the flow in the opening of the generator scale model pit, in the space between the enclosure wall and the cooler exit, at the cooler exit, in the covers, in the air gap and in the interpole region are presented. Experimental aspects pertaining to the seeding of the flow, calibration targets, and experimental method are also discussed. Furthermore, a comparison of the experimental data with CFD (Computational Fluid Dynamics) simulation results using ANSYS-CFX is given.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.139

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 head0.025
GPT teacher head0.215
Teacher spread0.190 · 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 designSimulation or modeling
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

Citations6
Published2015
Admission routes3
Has abstractyes

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