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Record W4380853192 · doi:10.21105/joss.05435

APECSS: A software library for cavitation bubbledynamics and acoustic emissions

2023· article· en· W4380853192 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Journal of Open Source Software · 2023
Typearticle
Languageen
FieldMaterials Science
TopicUltrasound and Cavitation Phenomena
Canadian institutionsPolytechnique Montréal
FundersDeutsche Forschungsgemeinschaft
KeywordsBubbleCavitationAcousticsShock (circulatory)Shock waveCompressibilityMechanicsAcoustic attenuationAttenuationPhysicsAerospace engineeringComputer scienceEngineeringOptics

Abstract

fetched live from OpenAlex

The dynamics of cavitation bubbles and the acoustic emissions they produce are important in a broad range of engineering applications and natural phenomena, either because the strong energy focusing of the bubble collapse is to be avoided, as it may cause damage to surfaces, or to be exploited, such as in emerging medical applications.APECSS (Acoustic Pulse Emitted by Cavitation in Spherical Symmetry) is a software library to simulate the dynamic behavior and acoustic emissions of cavitation bubbles using an efficient state-of-the-art numerical framework.APECSS supports different Rayleigh-Plesset models for bubble dynamics in incompressible and compressible media with Newtonian or viscoelastic rheology, considering clean or coated bubbles.Acoustic emissions may be modeled under different modeling assumptions using a tailored Lagrangian wave tracking method, including the formation and attenuation of shock waves.APECSS can be extended easily to include custom functionality and may be incorporated into other software frameworks.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score0.375

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 communication0.0000.001
Open science0.0010.000
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 head0.021
GPT teacher head0.284
Teacher spread0.262 · 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