Characterization and calibration of Raman based distributed temperature sensing system for 600°C operation
Bibliographic record
Abstract
Fiber optic distributed temperature sensing based on Raman scattering of light in optical fibers has become a very attractive solution for distributed temperature sensing (DTS) applications. The Raman scattered signal is independent of strain within the fiber, enabling simple packaging solutions for fiber optic temperature sensors while simultaneously improving accuracy and robustness of temperature measurements due to the lack of strain-induced errors in these measurements. Furthermore, the Raman scattered signal increases in magnitude at higher fiber temperatures, resulting in an improved SNR for high temperature measurements. Most Raman DTS instruments and fiber sensors are designed for operation up to approximately 300˚C. We will present our work in demonstrating high temperature calibration of a Raman DTS system using both Ge doped and pure silica core multi-mode optical fiber. We will demonstrate the tradeoffs involved in using each type of fiber for high temperature measurements. In addition, we will describe the challenges of measuring large temperature ranges (0 – 600˚C) with a single DTS interrogator and will demonstrate the need to customize the interrogator electronics and detector response in order to achieve reliable and repeatable high temperature measurements across a wide temperature range.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".