Optical Fiber Sensors in Extreme Temperature and Radiation Environments: A Review
Bibliographic record
Abstract
This paper presents a comprehensive review of optical fiber sensors (OFSs), including FBG, distributed optical fiber sensor and Fabry-Perot interferometer, and their applications within harsh environments, which include extremely high temperatures (from 275 °C to 1750 °C) and low temperatures (from −271.15 °C to −40 °C, namely cryogenic conditions: from 2 K to 233.15 K), and high levels of ionizing radiation (with a maximum gamma dose up to 2 GGy, and a maximum neutron fluence of approximately$5 \times 10^{19}$n/cm2). After a brief introduction of the principles of OFSs and mechanisms of interrogation, this paper focuses on the existing works for the above three operating environments. Attention have been paid to material selection for fabricating fibers, effects of doping with rare earth elements, femtosecond laser engraving, pre-processing and post-processing (i.e., annealing) that are employed to overcome issues faced byOFSs in extreme temperatures and radiation environments. Application examples and practical test cases are also presented. Through these examples, the limitations in the current state-of-the-art are acknowledged and the key problems are identified. Potential solutions to some of these problems are also elucidated. A feature of this paper is the amalgamation of many research methodologies and outcomes in three seemingly distinct environmental conditions in one place so that different solution techniques can be integrated to advance OFS technologies, especially for extreme environment applications.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".