Novel Experimental and Simulation Investigation of Transducer Coupling and Specimen Geometry Effects in Low-Frequency Ultrasonic Testing
Bibliographic record
Abstract
Conventional characterization of ultrasonic testing (UT) transducers primarily focuses on determining centre frequency and usable bandwidth. However, the relative amplitude distribution across different frequency components—particularly in low-frequency transducers used for civil engineering applications—remains largely overlooked. This paper introduces a comprehensive methodology to assess the influence of transducer coupling and specimen geometry on ultrasonic pulse velocity signals. The novel approach combines high-frequency laser Doppler vibrometry, real-time photoelastic imaging, and computer simulations using commercial semi-analytical wave-propagation software. The methodology is applied to the characterization of a 250 kHz UT transducer, with particular emphasis on how coupling with a solid test medium alters its frequency response. A glass specimen with an acoustic impedance comparable to that of concrete is used to simulate practical testing conditions. Vibration patterns recorded at the distal end of the specimen are analysed through computer simulations and validated experimentally using a novel photoelastic system capable of capturing wave–specimen interactions at ultrasonic frequencies in real time. The findings offer valuable insights into frequency-dependent signal behaviour and transducer–medium interactions, providing practical guidance for the design and optimization of UT inspections in concrete and other highly attenuative materials commonly encountered in civil engineering.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".