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Record W2550889248 · doi:10.1121/1.4970764

Numerical investigation of the nonlinear dynamics of interacting microbubbles

2016· article· en· W2550889248 on OpenAlexaff
Amin Jafari Sojahrood, Hossein Haghi, Raffi Karshafian, Michael C. Kolios

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2016
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsNonlinear systemResonance (particle physics)Dynamics (music)PhysicsBifurcationPopulationMicrobubblesHarmonicWork (physics)Nonlinear resonanceMechanicsAtomic physicsThermodynamicsAcousticsQuantum mechanics

Abstract

fetched live from OpenAlex

The successful application of MBs requires detailed understanding of the nonlinear behavior of microbubbles (MBs), especially in polydisperse clouds, where the dynamic of every individual MB affects the other MBs. However, there is not enough information on how the nonlinear oscillations of one MB influences the other and vice versa. In this work the dynamics of 2 and 3 interacting MBs of initial radii of 1μm<R0<4 μm are studied by investigating the pressure dependent resonance curves and the bifurcation diagrams of the MBs (1 MHz<f<15 MHz, 1 kPa<p<1 MPa) for cases of no interaction and interaction with varying MB-MB distances. Results show that, for small enough distances, the pressure dependent resonance frequencies (fr) and the pressure threshold for harmonic and SH fr decreases. The larger MB may force new peaks in the resonance curves of the smaller MB at fr, harmonic fr and SH fr of the larger MB. The larger MB can control the dynamics of the smaller MB and force the smaller MB to exhibit the same nonlinear behavior (e.g., ½, 1/3, 1/4 SH) as of the larger MB. The dynamics of a cloud of interacting MBs maybe controlled by controlling the dynamics of the larger MB in the population.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.782
Threshold uncertainty score0.298

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 studies0.0000.001
Scholarly communication0.0000.000
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 head0.009
GPT teacher head0.225
Teacher spread0.217 · 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 designSimulation or modeling
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

Citations5
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicParticle Dynamics in Fluid FlowsFrench-language works237,207