Fibre optic sensors for high temperatures and fire scenarios
Bibliographic record
Abstract
In both industrial applications and fire scenarios, sensing may be required at very high temperatures. Such sensing could be used to monitor and control equipment in industrial situations or to provide an emergency management system in a structural fire. Conventional fibre optic sensors (FOS), however, are limited to relatively low temperatures. Thus, this paper discusses the development of technology for fibre optic sensing at high temperatures.FOS with a special coating for resisting high temperatures is being employed to measure temperatures and strains on fibre reinforced polymer (FRP) tensile coupons. These sensors are attached to the coupons and tested in a special high temperature material testing facility at Queen?s University. This test facility can perform well-controlled tests up to 600°C with a load capacity of 600 kN. The stimulated Brillioun scattering method (SBS) is used to interpret the measurements. Using Optical Frequency Domain Reflectometry (OFDR) on data collected from carbon coated fibres, a relationship between strains and the optical wavelength shifts is discovered. In addition to FOS, the particle image velocimetry (PIV) method is used to determine the state of strain and deformation in the coupons.To illustrate the potential application in a structure, two full-scale T-beams (4 m span) are constructed with FOS attached to the internal longitudinal reinforcement. These T-beams are strengthened with external FRP, and fire protection for the FRP is provided by sprayed insulation. These beams are then exposed to a standard ASTM fire while under sustained loading.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".