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Record W4417359560 · doi:10.15625/0866-7136/23571

Finite element analysis of guided wave dispersion in pipes with multichannel acquisition

2025· article· en· W4417359560 on OpenAlexaff
Ductho Le, Haidang Phan, Hoai Thu Nguyen

Bibliographic record

VenueVietnam Journal of Mechanics · 2025
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsUniversity of Alberta
FundersVietnam Academy of Science and Technology
KeywordsAttenuationFinite element methodGuided wave testingMirroringDispersion (optics)SIGNAL (programming language)Signal processingSensitivity (control systems)

Abstract

fetched live from OpenAlex

Ultrasonic guided waves have become a key tool in nondestructive evaluation of pipelines, as they can travel long distances with low attenuation while maintaining high sensitivity to defects. Accurate modeling of their dispersion characteristics is essential for inspection design and signal interpretation. This study presents a finite element (FE) framework that advances beyond conventional eigenvalue-based analyses by directly simulating a realistic multichannel acquisition process. A pitch-catch configuration, consisting of a ring actuator and a linear receiver array, is modeled, and dispersion spectra are reconstructed through two-dimensional Fourier transforms—closely mirroring experimental practice. The reconstructed spectra show excellent agreement with analytical solutions, thereby validating the approach. By bridging numerical modeling and experimental acquisition, this FE framework delivers realistic datasets that facilitate advanced signal processing, imaging algorithms, and pipeline inspection strategies.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.771
Threshold uncertainty score0.325

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.009
GPT teacher head0.220
Teacher spread0.211 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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