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Record W2545845284 · doi:10.1109/ultsym.2010.5935525

Design and experiment of high temperature wedges for shear horizontal plate acoustic waves

2010· article· en· W2545845284 on OpenAlexaff
J.E.B. Oliveira, K.-T. Wu, C.K. Jen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsNational Research Council CanadaMcGill University
Fundersnot available
KeywordsWedge (geometry)Materials scienceBrassUltrasonic sensorShear wavesTransducerAcousticsShear (geology)Lamb wavesAcoustic waveLongitudinal waveElectromagnetic acoustic transducerUltrasonic testingOpticsComposite materialSurface waveWave propagationCopperPhysicsMetallurgy

Abstract

fetched live from OpenAlex

Theoretical and experimental studies of ultrasonic wedges which can generate and receive shear horizontal (SH) plate acoustic waves (PAWs) in a metal plate at temperatures up to 200°C are presented. Brass which has a slow shear (S) wave velocity was chosen as the high temperature wedge material. Mode conversion method is used to convert longitudinal (L) waves generated by a high temperature integrated ultrasonic transducer to S waves in the brass wedge. The calculated L to S wave mode energy conversion efficiency is 79.7% with a conversion angle of 64.1°. The S waves in the wedge glued onto a stainless steel (SS) plate with a wedge angle of 42.9° have been converted to SH PAWs in the SS plate. Analytical analysis of the coupling coefficient from the S waves in the wedge to the SH PAWs in the plate is presented. Measurement results demonstrated that SH PAWs using such wedges may be a practical approach for non-destructive testing and structural health monitoring of metal structures.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.203
Teacher spread0.196 · 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 designBench or experimental
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
Published2010
Admission routes1
Has abstractyes

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