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Record W2898516024 · doi:10.1115/fedsm2018-83178

Design of a Thermal Mass Flow Meter for Measurements Within the Rotor Rim Ducts of a Hydroelectric Generator

2018· article· en· W2898516024 on OpenAlexaff
Kevin Venne, Laurent Mydlarski, Federico Torriano, Mathieu Kirouac, Jean-Philippe Charest-Fournier, C. Hudon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversité de SherbrookeMcGill UniversityHydro-Québec
Fundersnot available
KeywordsRotor (electric)Thermal mass flow meterAnemometerComputational fluid dynamicsHydroelectricityStatorMechanicsMass flowFlow measurementMarine engineeringFlow (mathematics)Mass flow meterGenerator (circuit theory)MetreMechanical engineeringAirflowEngineeringTurbulencePhysicsElectrical engineeringPower (physics)

Abstract

fetched live from OpenAlex

To ensure the proper operation of hydroelectric generators, their cooling must be well understood. However, the airflow within such machines is difficult to characterize, and although Computational Fluid Dynamics (CFD) can be a reliable engineering tool, its application to the field of hydroelectric generators is quite recent and has certain limitations which are, in part, due to geometrical and flow complexities, including the coexistence of moving (rotor) and stationary (stator) components. For this reason, experimental measurements are required to validate CFD simulations of such complex flows. Of particular interest is the quantification of the flow within the rotor rim ducts, since it is directly responsible for cooling the poles (one of the most critical components of a hydroelectric generator). Thus, to measure the flow therein, an anemometer was designed. The anemometer had to be accurate, durable, cost-effective, easy to install, and able to withstand the extreme conditions found in hydroelectric generators (temperatures of 45°C, centrifugal forces of 300 g, etc.). In this paper, a thermal mass flow meter and a method for validating its performance, using hot-wire anemometry and a static model of a rotor rim, are described. Preliminary tests demonstrate that the thermal mass flow meter is capable of i) measuring the mass flow rate in the rotor rim ducts with an accuracy of approximately 10%, ii) fitting inside small rectangular ducts (12.2 mm by 51 mm), and iii) resisting forces up to 300 g.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.818
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.036
GPT teacher head0.220
Teacher spread0.184 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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