Temperature Profiles and Mixing in a Natural-Circulation Cooling Facility via Distributed Optical Sensors
Bibliographic record
Abstract
A research team at the University of Wisconsin has constructed a 1/4-scale experimental facility to study natural circulation cooling in an air-cooled reactor cavity cooling system (ARCCS) for decay heat removal. The ARCCS uses the principle of fluid buoyancy to induce a flow of air through multiple heated risers. This flow is used to remove decay heat from the reactor pressure vessel (RPV) by radiative and convective heat transfer to the risers that surround the RPV. During normal operation of a high-temperature reactor, this system is designed to protect the reactor cavity structures from excessive heat loads. The ARCCS experimental facility is equipped with new distributed temperature sensors designed by Luna Inc. The sensors are distributed optical fiber sensors that can measure a change in temperature from their initial state every 1.25 mm along a 10-m fiber at a maximum rate of 24 Hz. These fibers are standard communication-grade fibers, which are flexible and can be orientated in whatever shape needed to collect data, based on what the facility dictates. The standard available coatings can allow for continuous operation at temperatures of up to 300°C before degradation; however, the silica fiber itself can be taken as high as 700°C. The data from the fibers can be used to analyze the temperature distribution of the air in the ARCCS as it mixes and vents out of the system. The data produced from these fibers may prove to be useful for validation of the modeling of natural-circulation phenomena and the mixing of buoyancy-dominated flows with greater resolution.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".