NDT of pipes and plates at high temperatures
Bibliographic record
Abstract
NDT and structural health monitoring (SHM) of metal pipes and plates at temperatures up to 490°C using flexible ultrasonic transducers (FUTs) are presented. These FUTs consist of a typically 75-μm thick titanium or stainless steel membrane coated with a 70 μm piezoelectric bismuth-titanate composite (BIT-c) or lead-zirconate titanate composite (PZT-c) film, and a thin top electrode. The BIT-c and PZT-c films are made with a sol-gel spray technique. The main merit of these FUTs is that they can be fabricated economically and installed on-site for NDT and SHM purposes. On-site installation using glues or brazing materials which serve as a high temperature ultrasonic couplant between the FUT and the object for inspection, will be demonstrated. One of such FUTs with a center frequency of around 12.1 MHz has been glued onto a steel pipe of 101 mm in diameter and 4.5 mm in wall thickness and operated at 300°C. The estimated pipe thickness measurement accuracy at 300°C is 8 μm. Another FUT was brazed onto a steel pipe of 25 mm in diameter and 3.5 mm in wall thickness and operated up to 490°C. Furthermore, UTs with a center frequency of about 7 MHz were glued onto the end and side edges of steel plates with a length of 406.4 mm and a width of 50.8 mm to generate and receive, predominantly shear-horizontal (SH) plate acoustic waves (PAWs) at temperatures up to 300°C. Two line defects, with 1 mm in width, 1 mm in depth, and 25.4 mm and 50.8 mm in length, at a distance of about 146.3 mm and 233.5 mm respectively away from the FUT location, could be detected by the SH PAWs. FUT arrays have been used to demonstrate the capability of monitoring crack propagation in a steel plate.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".