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Record W2790323722 · doi:10.1115/1.4039713

Experimental and Numerical Investigations on the Origins of Rotating Stall in a Propeller Turbine Runner Operating in No-Load Conditions

2018· article· en· W2790323722 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Fluids Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicCavitation Phenomena in Pumps
Canadian institutionsUniversité Laval
FundersCompute Canada
KeywordsStall (fluid mechanics)TurbineSurgeMarine engineeringVortexSpinningFrancis turbineCavitationPropellerMechanicsEngineeringMechanical engineeringAerospace engineeringPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

Hydraulic turbines are more frequently used for power regulation and thus spend more time providing spinning reserve for electrical grids. Spinning reserve requires the turbine to operate at its synchronous rotation speed, ready to be linked to the grid in what is termed the speed-no-load (SNL) condition. The turbine's runner flow in SNL is characterized by low discharge and high swirl leading to low-frequency high amplitude pressure fluctuations potentially leading to blade damage and more maintenance downtime. For low-head hydraulic turbines operating at SNL, the large pressure fluctuations in the runner are sometimes attributed to rotating stall. Using embedded pressure transducer measurements, mounted on runner blades of a model propeller turbine, and numerical flow simulations, this paper provides an insight into the inception mechanism associated with rotating stall in SNL conditions. The results offer evidence that the rotating stall is in fact associated with an unstable vorticity distribution not associated with the runner blades themselves.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.600
Threshold uncertainty score0.405

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 head0.013
GPT teacher head0.239
Teacher spread0.226 · 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