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Record W2968632776 · doi:10.29008/etc2019-381

Part load resonance risk assessment of francis hydropower units

2019· article· en· W2968632776 on OpenAlexfundno aff
Joao Gomes Perreira, Arthur Favrel, Christophe Nicolet, François Avellan

Bibliographic record

VenueProceedings of ... European Conference on Turbomachinery Fluid Dynamics & Thermodynamics · 2019
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsnot available
FundersFP7 EnergyBundesamt für EnergieBC HydroKommission für Technologie und InnovationEuropean Commission
KeywordsFrancis turbineDraft tubeHydropowerTurbineResonance (particle physics)Marine engineeringHydraulic turbinesPrecessionCavitationRotor (electric)RopeMechanicsEngineeringComputer scienceStructural engineeringPhysicsMechanical engineeringElectrical engineering

Abstract

fetched live from OpenAlex

While operating at part load, Francis turbines feature a precessing cavitation vortex rope in its draft tube. The precession of this vortex in the elbow of the draft tube acts as a pres-sure pulsation source which frequency can match the first hydroacoustic eigenfrequency of the hydraulic system in some cases. Resonance phenomenon can be predicted by using reduced scale physical model tests and numerical simulations, but it remains challenging. This paper proposes a procedure to estimate the risk of part load resonance at the early stage of a hydropower project. The proposed procedure uses the hydroacoustic properties of a given reduced scale physical model and applies them to a large number of turbine de-signs and power plant configurations to assess the risk of resonance for each one of them. Results show that resonance are likely to occur in hydropower plants in a certain range of turbine rated head and rated discharge values. These results can then indicate if more detailed investigations in some specific hydropower projects are necessary.

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 categoriesMeta-epidemiology (narrow)
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 score0.471
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.008
GPT teacher head0.200
Teacher spread0.192 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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