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Acoustic-structure interaction in disk-disk configurations in water for high head pump-turbines

2025· article· en· W4409233907 on OpenAlexaff
R. Jean, O Braun, Christine Monette, B Nennemann, Frédérick P. Gosselin

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2025
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsAndritz (Canada)Polytechnique Montréal
Fundersnot available
KeywordsHead (geology)AcousticsMaterials sciencePhysicsGeology

Abstract

fetched live from OpenAlex

Abstract Francis high-head pumps or pump-turbine runners often have speed and dimension ratios that make acoustic and structural natural frequencies, as well as rotor-stator excitation frequencies, approach each other. When compressibility effect becomes significant, it is not sufficient to only consider the incompressible added mass, which shifts the natural frequencies of the structure. The strong vibroacoustic coupling makes it difficult to distinguish the nature of each mode and the associated effect of the forced response amplification. The project’s main aim is to study a disk-disk system behavior in an acoustic cavity filled with water when the acoustic and structural frequencies are close, as a simplified model of a pump-turbine runner in its casing. It is demonstrated here that the compressibility significantly affects vibration modes of the system, particularly when individual acoustic and structural modes have similar frequencies and spatial coherence, resulting in an increase in energy transmission and a decrease in total natural frequency. Additionally, the system’s rigidity is affected by the cover’s stiffness; when the vibration frequencies of the cover and runner are similar, excitation transmission is divided between them.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.700
Threshold uncertainty score0.361

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 communication0.0000.000
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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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 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
Published2025
Admission routes1
Has abstractyes

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