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Record W4413030512 · doi:10.1017/jfm.2025.10398

Cavitation bubble-induced hydrodynamic loading and interfacial deformation in an air pocket

2025· article· en· W4413030512 on OpenAlexaff
Jihoo Moon, C.D. Jang, Ehsan Mahravan, Daegyoum Kim

Bibliographic record

VenueJournal of Fluid Mechanics · 2025
Typearticle
Languageen
FieldMaterials Science
TopicUltrasound and Cavitation Phenomena
Canadian institutionsKootenay Association for Science & Technology
FundersNational Research Foundation of Korea
KeywordsCavitationBubbleMechanicsAir bubbleMaterials scienceDeformation (meteorology)PhysicsComposite material

Abstract

fetched live from OpenAlex

The presence of trapped air on a solid surface can alter the direction of the liquid jets induced by cavitation bubbles, which prevents or reduces erosion. In this study, we numerically investigate mutual interaction between air trapped in a pocket on a wall and a nearby bubble in water, as well as the resultant hydrodynamic loading. Both the depth and radius of the cylindrical pocket are similar to the maximum bubble radius. The pressure imposed on the inner wall of the air pocket is assessed for various values of the air pocket size and the stand-off parameter. The deformation of the air pocket and the bubble is analysed in each of three sequential stages. During the bubble expansion stage, a shock wave reflects at the water–air interface of the pocket, and the wall inside the compressed pocket is protected from the shock wave. As the bubble jet induced during bubble contraction tends to move away from the air pocket, other liquid jets formed at the water–air interface, namely central and lateral pocket jets, can directly collide with the inner wall of the pocket after the bubble collapses. These collisions exert significant pressure on the wall under certain conditions. The formation of the central pocket jet originates from the strong fluctuation of the water–air interface by the expanding and contracting bubble. The development of the lateral pocket is related to changes in the potential energy of the air under its second contraction.

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 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 score0.207
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.010
GPT teacher head0.266
Teacher spread0.255 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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