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Record W2558155902 · doi:10.1088/1755-1315/49/8/082024

Influence of the thrust bearing on the natural frequencies of a 72-MW hydropower rotor

2016· article· en· W2558155902 on OpenAlexaff
Samuel Cupillard, J.-O. Aidanpää

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2016
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsThrust bearingThrustVibrationBearing (navigation)Structural engineeringRotor (electric)Natural frequencyStiffnessTurbineEngineeringRADIUSFinite element methodPropellerGeologyMarine engineeringMechanical engineeringPhysicsAcousticsComputer science

Abstract

fetched live from OpenAlex

The thrust bearing is an essential element of a hydropower machine. Not only does it carry the total axial load but it also introduces stiffness and damping properties in the system. The focus of this study is on the influence of the thrust bearing on the lateral vibrations of the shaft of a 72-MW propeller turbine. The thrust bearing has a non-conventional design with a large radius and two rows of thrust pads. A numerical model is developed to estimate natural frequencies. Numerical results are analyzed and related to experimental measurements of a runaway test. The results show the need to include the thrust bearing in the model. In fact, the vibration modes are substantially increased towards higher frequencies with the added properties from the thrust bearing. The second mode of vibration has been identified in the experimental measurements. Its frequency and mode shape compare well with numerical results.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.006
GPT teacher head0.171
Teacher spread0.165 · 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 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

Citations3
Published2016
Admission routes1
Has abstractyes

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