Waterproof, Anti‐Impacted, and Ultrathin Carbon‐Based Air Pressure Sensors Toward Aerodynamic Tests on High‐Speed Trains
Bibliographic record
Abstract
Air pressure sensors play a crucial role in aerodynamic tests on high‐speed trains, especially when the aerodynamic problems become more significant when the speed of high‐speed trains increases. The air pressure sensors used for aerodynamic testing of high‐speed trains are currently based on microelectromechanical systems (MEMS), which are thick, difficult to adjust linear range, and easily damaged by overloading force and water, thus cannot satisfy all‐weather train aerodynamic monitoring. Herein, a flexible ultrathin air pressure sensor for aerodynamic testing of high‐speed trains is reported; this sensor is based on a sensing material of carbon fiber beams and a sealed microchamber structure. The microchamber structure model allows the sensor to achieve high sensitivity in the target linear range by adjusting the initial internal pressure of the sealed microchamber. Meanwhile, the sealed microchamber structure enables the sensor to be waterproof and anti‐impacted. The sensor can work in water for at least 500 min and remain undamaged after being run over by a car with a weight of approximately 1550 kg. Furthermore, this air pressure sensor has been successfully applied in real‐time train surface pressure monitoring and shows the fantastic perspective for sensors toward aerodynamic tests on the high‐speed trains.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".