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Record W4293116014 · doi:10.11159/htff22.133

Numerical Study of Cavitation Bubble Collapse under VariousConditions

2022· article· en· W4293116014 on OpenAlexvenueno aff
Van‐Tu Nguyen, Thanh-Hoang Phan, Donghyun Kim, Warn-Gyu Park

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2022
Typearticle
Languageen
FieldMaterials Science
TopicUltrasound and Cavitation Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsCavitationBubbleMechanicsMaterials scienceGeologyPhysics

Abstract

fetched live from OpenAlex

Cavitation bubble plays an important role in applications in diverse fields of science and technology such as naval structure engineering, biosciences, and biomedical technology. The cavitation bubble collapses violently, and in different conditions, the collapse of the cavitation bubble produces high-speed jets of liquid bubbles moving in different directions This in turn generates high local energy and high-pressure waves and high temperature Multiple events of cavitation bubble collapse that produce high pressure over time can cause detrimental effects on the mechanical components. This in turn generates high local energy and impacts the surface with high-pressure waves that can erode the metals Conversely, this energy was observed as useful for the hydrodynamic cavitation process in cleaning technology or in industrial applications such as wastewater treatment and biofuel production Bubble collapse leads to the re-entrant jet formation, concentrated pressures, shear, and lift forces on the dirt particle or biomass, and high impulsive loads on a layer of materials. In the other approaches, cavitation bubbles can be intentionally generated by using acoustic waves or laser technologies to take advantage of local high-energy and microjets for application to biosciences, and biomedical technology such as needlefree injection devices, tissue engineering, and lithotripsy

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.524

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 head0.008
GPT teacher head0.214
Teacher spread0.206 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Mechanical, Chemical, and Material EngineeringSame topicUltrasound and Cavitation PhenomenaFrench-language works237,207