Gamma radiation resistant Fabry–Perot fiber optic sensors
Bibliographic record
Abstract
The Nuclear Regulatory Commission (NRC) in 1998 completed a study of emerging technologies that could be applicable to measurement systems in nuclear power plants [H. M. Hashemian et al., “Advanced Instrumentation and Maintenance Technologies for Nuclear Power Plants,” NUREG/CR-5501 (1998)]. This study concluded that advanced fiber optic sensing technology is an emerging technology that should be investigated. It also indicated that there had been very little research related to performance evaluation of fiber optic sensors in nuclear plant harsh environments, although substantial research has been performed on nuclear radiation effects on optical fibers in the last two decades. A type of Fabry–Perot fiber optic temperature sensor, which is manufactured by Fiso Technologies in Canada, is qualified to be a candidate for potential applications in nuclear radiation environment due to its unique signal processing technique and its resistance to power loss. The gamma irradiation effects on this type of sensors are investigated in this article. Two sensors were irradiated in a gamma irradiation field and one of them was irradiated up to a total gamma dose of 133 Mrad. The sensor on-line performance was monitored during each gamma irradiation test. Furthermore, the sensor static and dynamic performance before and after each irradiation test were evaluated according to the Standard ISA-dS67.06.01 (“Performance Monitoring for Nuclear Safety-Related Instrument Channels in Nuclear Power Plants”, Standard ISA-dS67.06.01, Draft 7, Instrument Society of America, 1999). Although several abnormal phenomena were observed, analysis shows that gamma irradiation is not accredited to the abnormal behavior, which implies that this type of sensor is suitable to a gamma irradiation environment with a high gamma dose.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".