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Record W4396243808 · doi:10.1063/5.0209167

The Kirkwood–Bethe hypothesis for bubble dynamics, cavitation, and underwater explosions

2024· preprint· en· W4396243808 on OpenAlexaff
Fabian Denner

Bibliographic record

VenuePhysics of Fluids · 2024
Typepreprint
Languageen
FieldMaterials Science
TopicUltrasound and Cavitation Phenomena
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsBubbleCavitationUnderwater explosionPhysicsShock waveUnderwaterShock (circulatory)Statistical physicsMechanicsPlanarFlow (mathematics)Classical mechanicsComputer scienceGeology

Abstract

fetched live from OpenAlex

Pressure-driven bubble dynamics is a major topic of current research in fluid dynamics, driven by innovative medical therapies, sonochemistry, material treatments, and geophysical exploration. First proposed in 1942, the Kirkwood–Bethe hypothesis provides a simple means to close the equations that govern pressure-driven bubble dynamics as well as the resulting flow field and acoustic emissions in spherical symmetry. The models derived from the Kirkwood–Bethe hypothesis can be solved using standard numerical integration methods at a fraction of the computational cost required for fully resolved simulations. Here, the theoretical foundation of the Kirkwood–Bethe hypothesis and contemporary models derived from it are gathered and reviewed, as well as generalized to account for spherically symmetric, cylindrically symmetric, and planar one-dimensional domains. In addition, the underpinning assumptions are clarified and new results that scrutinize the predictive capabilities of the Kirkwood–Bethe hypothesis with respect to the complex acoustic impedance experienced by curved acoustic waves and the formation of shock waves are presented. Although the Kirkwood–Bethe hypothesis is built upon simplifying assumptions and lacks some basic acoustic properties, models derived from it are able to provide accurate predictions under the specific conditions associated with pressure-driven bubble dynamics, cavitation, and underwater explosions.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.713
Threshold uncertainty score0.597

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 head0.029
GPT teacher head0.265
Teacher spread0.236 · 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 designTheoretical or conceptual
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

Citations15
Published2024
Admission routes1
Has abstractyes

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