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Record W2142448078 · doi:10.1109/ultsym.2006.567

P3K-4 Utilizing a Ray Technique to Calculate Multiple Reflections and Transmitted Waves from Layered Plates and Examine the Detection of Critical Disbonds, and Lamb Modes Generated

2006· article· en· W2142448078 on OpenAlexafffund
Roman Gr. Maev, Jeffrey M. Sadler

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSuperposition principleLamb wavesAcousticsIsotropyAcoustic wavePhysicsLongitudinal waveVariety (cybernetics)OpticsGeometrical acousticsProcess (computing)Computer scienceWave propagation

Abstract

fetched live from OpenAlex

This paper considers the problem of a single, or multilayer, isotropic plate and the problem of calculating the multiple reflections of acoustic waves within the plate, and the acoustic waves transmitted outside the plate (leaky waves). By using a ray method, and considering the paths traveled by the rays, it has been found that many of the multiple reflections will travel equivalent paths in space and time. Using superposition to combine the equivalent paths reduces the number of rays to track, and allows the process of calculations to be done in a time efficient manner. The use of the a ray model also gives initial results which are independent of properties of the incident acoustic waves. A separate set of calculations can be used expand the rays to a variety of more realistic acoustic waves, allowing a single result to be used for a variety of acoustic sources. The results from this model will be used to examine situations such as the detection of critical disbonds in plates, examination of the lamb modes which are generated, or examination of which situations produce the largest transmitted waves

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.002

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 head0.013
GPT teacher head0.228
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2006
Admission routes2
Has abstractyes

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