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Record W3154840924 · doi:10.35848/1347-4065/abf74f

Beam-steering ultrasonic guided waves in a bone-mimicking plate by time-delaying the excitation of the elements in a multi-element array: a numerical study

2021· article· en· W3154840924 on OpenAlexaff
Hoai Thu Nguyen, Vu‐Hieu Nguyen, Quyen T.-L. Bui, Kim‐Cuong T. Nguyen, Haidang Phan, Lawrence H. Le

Bibliographic record

VenueJapanese Journal of Applied Physics · 2021
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBeam steeringExcitationUltrasonic sensorAcousticsFinite element methodBeam (structure)IsotropyLamb wavesDispersion (optics)PhysicsExcited stateOpticsMaterials scienceMechanicsWave propagationAtomic physics

Abstract

fetched live from OpenAlex

Abstract We present a numerical simulation of the beam-steering of ultrasonic guided waves in an isotropic and viscoelastic solid plate, which mimics bovine cortex. The excitation was modeled by a group of five finite-size emitters, each exercised a normal force to the bone plate. Beam steering was achieved by delaying the emitters’ firing. The simulation technique was implemented by a semi-analytical finite element scheme to compute the wave fields. At small steering angles, the simulated time-offset signals show mainly two groups of arrivals. The first group is the fast-traveling and high-frequency bulk waves and the second one is slow-traveling and low-frequency guided waves. The fast-traveling waves gradually diminish with increasing steering angles, in agreement with the excitation function of the source influence theory. The frequency-phase velocity dispersion maps also illustrate the phenomenon. The study has demonstrated that the lowest order Lamb asymmetrical mode, A 0 , which is useful for bone characterization, can best be excited when the cortical bone thickness is thin, the beam angle is large, and the excited frequency is low.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score0.425

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 communication0.0000.000
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 head0.014
GPT teacher head0.238
Teacher spread0.224 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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