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Record W3156302993 · doi:10.3390/metrology1010001

Accurate Measurements of a Wavelength Drift in High-Temperature Silica-Fiber Bragg Gratings

2021· article· en· W3156302993 on OpenAlexaff
Sergey Dedyulin, Elena Timakova, Dan Grobnic, Cyril Hnatovsky, A. D. W. Todd, Stephen J. Mihailov

Bibliographic record

VenueMetrology · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceFiber Bragg gratingWavelengthOpticsTemperature measurementAnnealing (glass)BirefringenceLaserOptoelectronics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.765

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.236
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2021
Admission routes1
Has abstractyes

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