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Record W1969931902 · doi:10.1115/fedsm2013-16473

Inlet Velocity Profile Optimization of the Turbine 99 Draft Tube

2013· article· en· W1969931902 on OpenAlexafffund
Sergio Galván, Marcelo Reggio, François Guibault

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCavitation Phenomena in Pumps
Canadian institutionsPolytechnique Montréal
FundersPolytechnique Montréal
KeywordsDraft tubeInletDiffuser (optics)TurbineFlow (mathematics)Marine engineeringFrancis turbineMechanicsHullFlow velocityComputational fluid dynamicsOperating pointTube (container)Volumetric flow rateMechanical engineeringEngineeringPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

In recent years, several investigations on hydraulic turbine draft tube performance have shown that the hydrodynamic field at the runner’s outlet is a direct outcome of the runner design and the operating point. This has shown the dependence of the diffuser efficiency on the flow rate and the inlet swirling flow intensity, mostly on turbines that present low head (high specific velocity) and operate away from their best efficiency point. The numerical optimization of the inlet velocity profile is presented as an attempt to control these two inlet flow characteristics. The goal is the improvement of the flow through the draft tube to allow for better turbine performance. This methodology is based on the automatic coupling of several commercial softwares and is used to manipulate the analytical representation of the swirling flow, which has led to the minimization of hydraulic losses. Also, a qualitative and quantitative analysis of the draft tube flow field provoked by a redesigned inlet velocity profiles, has helped to understand how it is possible to suppress or at least mitigate undesirable draft tube flow characteristics.

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 categoriesInsufficient payload (model declined to judge)
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 score0.164
Threshold uncertainty score0.999

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)0.0020.000

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 head0.006
GPT teacher head0.187
Teacher spread0.181 · 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.

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
Published2013
Admission routes2
Has abstractyes

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