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Hydraulic turbine start-up: a fluid-structure simulation methodology

2019· article· en· W2928904599 on OpenAlexaffabout
Matthieu Melot, Maxime Coulaud, J Chamberland-Lauzon, B Nennemann, Claire Deschênes

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2019
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsUniversité LavalTransCanada (Canada)
Fundersnot available
KeywordsHydraulic fluidFluid couplingMechanical engineeringFrancis turbineFocus (optics)Fluid dynamicsComputationCoupling (piping)Flow (mathematics)TurbineHydraulic machineryWork (physics)Hydraulic turbinesEngineeringComputer scienceMarine engineeringMechanicsPhysics

Abstract

fetched live from OpenAlex

In the present work, a one-way fluid-structure coupling approach is proposed with a focus on the fluid modelling. Main engineering quantities are compared against the thorough laboratory measurements coming from the BulbT project which is led by the Consortium on Hydraulic Machines and the LAMH (Hydraulic Machine Laboratory of Laval University). Good agreement is obtained with main engineering quantities as well as pressure sensors and strain gauges on the runner blades. Moreover, pressure sensor signals on the runner blades and the hydraulic computations results allow to give a description of the flow topology in the runner during the start-up and speed-no-load operating points.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score1.000

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 communication0.0000.001
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 head0.022
GPT teacher head0.226
Teacher spread0.204 · 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 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

Citations9
Published2019
Admission routes2
Has abstractyes

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