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Record W2771269982 · doi:10.1115/1.4038662

Numerical Study of the Winter-Kennedy Method—A Sensitivity Analysis

2017· article· en· W2771269982 on OpenAlexaff
Binaya Baidar, J Nicolle, Chirag Trivedi, Michel J. Cervantes

Bibliographic record

VenueJournal of Fluids Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicCavitation Phenomena in Pumps
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsMechanicsInletFlow (mathematics)Computational fluid dynamicsSpiral (railway)Discharge coefficientVortexFlow coefficientSensitivity (control systems)Environmental scienceMathematicsPhysicsEngineeringMechanical engineeringMathematical analysisThermodynamicsNozzle

Abstract

fetched live from OpenAlex

The Winter-Kennedy (WK) method is commonly used in relative discharge measurement and to quantify efficiency step-up in hydropower refurbishment projects. The method utilizes the differential pressure between two taps located at a radial section of a spiral case, which is related to the discharge with the help of a coefficient and an exponent. Nearly a century old and widely used, the method has shown some discrepancies when the same coefficient is used after a plant upgrade. The reasons are often attributed to local flow changes. To study the change in flow behavior and its impact on the coefficient, a numerical model of a semi-spiral case (SC) has been developed and the numerical results are compared with experimental results. The simulations of the SC have been performed with different inlet boundary conditions. Comparison between an analytical formulation with the computational fluid dynamics (CFD) results shows that the flow inside an SC is highly three-dimensional (3D). The magnitude of the secondary flow is a function of the inlet boundary conditions. The secondary flow affects the vortex flow distribution and hence the coefficients. For the SC considered in this study, the most stable WK configurations are located toward the bottom from θ=30deg to 45deg after the curve of the SC begins, and on the top between two stay vanes.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score0.461

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 head0.013
GPT teacher head0.266
Teacher spread0.253 · 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

Citations8
Published2017
Admission routes1
Has abstractyes

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