Modeling and Analysis of Fiber Optic Bragg Grating Shape Sensors
Bibliographic record
Abstract
The present work aims at modeling of fiber Bragg gratings (FBG) used as shape sensors. FBGs, which are commonly used for sensing physical parameters (e.g., strain, temperature, pressure), can be effectively used for shape detection in flexible bodies. They can be embedded in flexible structures for in-situ measurement of curvature. In order to design the embedded sensors and identify the spectral response of FBG, the effects of different geometrical and structural parameters on the optical response of the fiber should be investigated. In this paper, an opto-mechanical model is developed to assess the shape detection with FBGs. In the proposed model, non-symmetric coating of optical fibers with metallic materials is investigated. The model is a combination of structural and optical analyses; the structural analysis is used to find the change in optical properties of the sensor due to photo-elastic effect and the optical analysis is conducted to find the spectral response of FBG. It is shown that the non-symmetric coating can increase the sensitivity of the sensor. While the bare sensor shows little sensitivity to the curvature, the sensitivity increases with non-symmetric coating.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".