Ultrasonic Testing of Railroad Rails: Cold Temperature Effects and Considerations
Bibliographic record
Abstract
The research presented in this article investigated the effect of low temperatures on acoustic properties in coupling fluid and rail steel. The study focused on the effect of low-temperature conditions on ultrasonic attenuation and velocity. The work introduces practical considerations for improving the quality of ultrasonic testing (UT) performed in cold weather. The study investigated common coupling fluids used in rail detector cars equipped with liquid-filled tires that house ultrasonic transducers. Velocity measurements of longitudinal waves propagating through the fluid and reflecting from a steel disc target were conducted. Steel properties were studied by fabricating two specimens from the head and Web of two different 136RE rail sections. Velocity of longitudinal waves and mode-converted shear waves as well as attenuation measurements were conducted in rail specimens with side drilled holes (SDHs) at different depths. The tests were performed in an ultrasonic immersion tank integrated with a heat exchanger and chiller bath to obtain the targeted test temperatures ranging from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$- 50~^{\circ }$ </tex-math></inline-formula>C to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${+} 20~^{\circ }$ </tex-math></inline-formula>C. The coupling fluid test results showed a linear increase in the ultrasonic velocity as the temperature decreased with a rate that ranged from −2.70 m/s/°C to −1.83 m/s/°C for the tested fluids. The test results also showed increased velocity in rail steel with decreasing temperatures with an average rate of −0.65 m/s/°C for longitudinal waves and an average rate of −0.33 m/s/°C for shear waves. These results indicate that temperature-dependent velocities must be used to obtain the desired refraction angle and adjustments to amplitude-based acceptance criteria may be needed to ensure uniform acceptance/rejection capabilities across all potential inspection temperatures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".