Extrinsic Optical Fiber Pressure Sensor Based on F-P Cavity
Bibliographic record
Abstract
Aiming at the problems of the in-situ stress monitoring sensor such as susceptibility to lightning strikes and low accuracy, this paper attempts to design one extrinsic pressure sensor based on the optical fiber sensing technology and F-P interferometer. To this end, it first introduces the structural design of the sensor, which is mainly composed of sensitive diaphragm, single-mode fiber and ceramic ring. The use of silver-plated diaphragms in the air cavity can increase the light reflectivity, thereby improving the monitoring accuracy; the insulating anti-electromagnetic interference material can make the sensor less susceptible to lightning strikes and reduce the risk of damage to the sensor due to lightning strikes. Then, the ANSYS simulation software was used to statically analyze the silverplated diaphragm of the sensor, and obtain the ideal radius and thickness values of the silver-plated diaphragm. Furthermore, to verify the accuracy and lightning protection performance of the extrinsic fiber pressure sensor based on F-P cavity in actual stress measurement, an in-situ stress monitoring experiment platform and a simulated lightning strike experiment platform were built. The pressure and temperature calibration experiments and sensor stress loading simulation showed that the sensor has a cavity length change sensitivity of 1677nm/MPa within 0-10MPa and a stress measurement resolution of 60Pa; compared with the common fiber grating method for in-situ stress measurement, the monitoring accuracy of the designed sensor has improved about 3 times; the lightning strike experiment found by using a lightning surge generator that the designed sensor has a stable operating and anti-lightning performance under the test voltage of 0.5kV. The research findings shed light on the further study of F-P cavity-based extrinsic fiber pressure sensor.
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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".