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Flow field investigation in a bulb turbine diffuser

2017· article· en· W2604604593 on OpenAlexaff
Michaël Pereira, Pierre Duquesne, V Aeschlimann, Claire Deschênes

Bibliographic record

VenueJournal of Physics Conference Series · 2017
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversité Laval
FundersVolvo Research and Educational Foundations
KeywordsBackflowDiffuser (optics)MechanicsInletFlow (mathematics)TurbineVolumetric flow rateMaterials scienceDrop (telecommunication)Internal flowFlow separationOpticsEngineeringMechanical engineeringPhysicsTurbulence

Abstract

fetched live from OpenAlex

An important drop in turbine performances has been measured in a bulb turbine model operated at overload. Previous investigations have correlated the performance drop with diffuser losses, and particularly to the flow separation zone at the diffuser wall. The flow has been investigated in the transition part of the diffuser using two LDV measurement sections. The transition part is a diffuser section that transforms from a circular to a rectangular section. The two measurement sections are at the inlet and outlet of the diffuser transition part. The turbine has been operated at three operating points, which are representative of different flow patterns at the diffuser exit at overload. In addition to the average velocity field, the analysis is conducted based on a backflow occurrence function and on the swirl level. Results reveal a counter-rotating zone in the diffuser, which intensifies with the guide vanes opening. The guide vanes opening induces a modification of the flow phenomena: from a central backflow recirculation zone at the lowest flowrate to a backflow zone induced by flow separation at the wall at the highest flowrate.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.215
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2017
Admission routes1
Has abstractyes

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