MétaCan
Menu
Back to cohort
Record W4406583681 · doi:10.1115/1.4067647

Parametric Assessment of Size and Peak Pressure Variability for Cavitation Bubbles Induced by Low-Voltage Discharge

2025· article· en· W4406583681 on OpenAlexafffund
Janika Bourgeois, Justine Savard, Jean-David Buron, Sébastien Houde

Bibliographic record

VenueJournal of Fluids Engineering · 2025
Typearticle
Languageen
FieldMaterials Science
TopicUltrasound and Cavitation Phenomena
Canadian institutionsUniversité Laval
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsCavitationParametric statisticsMechanicsMaterials scienceVoltageEnvironmental sciencePhysicsEngineeringElectrical engineeringMathematicsStatistics

Abstract

fetched live from OpenAlex

Abstract Cavitation presents a significant challenge in the operation and longevity of hydraulic machinery. Studying a single cavitation bubble can provide fundamental insights into the phenomenon. One simple and popular method to generate such bubbles is through a low-voltage discharge between two contacting electrodes to create a spark that locally vaporizes water. This study investigates the repeatability of low-voltage discharge in generating consistent bubbles. The electrode length is one parameter that influences the bubble size. However, the excess length (the electrode length after the contact point) is shown to be the primary variable influencing the bubble size rather than the total electrode length. Additionally, even for bubbles of similar size, significant variability in wall pressure peaks was observed for bubbles generated far from the surface. This variability correlates with the time the electrodes require to melt and break. Longer melting times are associated with extended bubble lifetimes and lower pressure peaks.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.257
Teacher spread0.251 · 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 routes2
Has abstractyes

Explore more

Same venueJournal of Fluids EngineeringSame topicUltrasound and Cavitation PhenomenaFrench-language works237,207