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Record W7095176457

Canadian machinery vibration association 1 OPERATIONAL MODAL ANALYSIS OF A STRUCTURE SUBJECTED TO A TURBULENT FLOW

2015· article· en· W7095176457 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCavitation Phenomena in Pumps
Canadian institutionsnot available
Fundersnot available
KeywordsVibrationModal analysisResonance (particle physics)Added massTurbulenceOperational Modal AnalysisTurbineTurbine bladeModal
DOInot available

Abstract

fetched live from OpenAlex

The mechanical systems are subjected to various excitations during their operation. According to the type of excitation, the vibratory response differs too. Generally the excitations such as the repetitive shocks and random excitations excite the resonance frequencies of the system. In the special case of the hydraulic turbines, the blades operating under water are subjected to turbulent flows and mechanical constraints generating vibrations may be amplified when the resonance occurs. This fact may harm the state of the turbine blades subjected to the rupture due to the appearance of cracks and the slackening of the welding. This is reflected on the safety operation of the turbine, and affects notably its energetic efficiency. One can easily understand the importance of the knowledge of the structural resonance frequencies of the machine in order to specify a safety range of operating conditions. It is well known that the resonance frequencies of underwater structures are not the same when these structures vibrate in air because of the effects of the added mass and added damping. The first effect is the result of the presence of water itself or fluid in general, the second is caused by the fluid flows. However, if the identification of the parameters of the structures in air is well controlled nowadays, the identification of the

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 categoriesnone
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 score0.236
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.202
Teacher spread0.196 · 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.

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

Citations0
Published2015
Admission routes1
Has abstractyes

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