Accurate Measurements of a Wavelength Drift in High-Temperature Silica-Fiber Bragg Gratings
Bibliographic record
Abstract
Fiber Bragg gratings (FBG) are extensively used to perform high-temperature measurements in harsh environments, however the drift of the characteristic Bragg wavelength affects their long-term stability resulting in an erroneous temperature measurement. Herein we report the most precise and accurate measurements of wavelength drifts available up to date on high-temperature FBGs. The measurements were performed with a set of packaged π-phase-shifted FBGs for high wavelength resolution, in caesium and sodium pressure-controlled heat pipes for stable temperature environment and with a tunable laser for stable wavelength measurements with a 0.1 pm resolution. Using this dataset we outline the experimental caveats that can lead to inconsistent results and confusion in measuring wavelength drifts, namely: influence of packaging; interchangeability of FBGs produced under identical conditions; birefringence of π-phase-shifted FBGs; initial transient behaviour of FBGs at constant temperature and dependence on the previous thermal history of FBGs. In addition, we observe that the wavelength stability of π-phase-shifted gratings at lower temperature is significantly improved upon by annealing at higher temperature. The lowest value of the wavelength drift we obtain is +0.014 pm·h−1 at 600 °C (corresponding to +0.001 °C·h−1) after annealing for 400 h at 1000 °C, the longest annealing time we have tried. The annealing time required to achieve the small drift rate is FBG-specific.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".