The National Research Council of Canada’s large-scale frazil ice facility
Bibliographic record
Abstract
Frazil ice particles form in supercooled turbulent waters. Frazil ice is known for clogging the water intakes of infrastructure such as municipal water supplies or power generating facilities. It also has the potential to cause flooding events. The frazil ice facility of the National Research Council of Canada was designed to induce controlled frazil ice events, and investigate accretion and potential clogging on underwater structures at near full scale. It is a re-circulating channelbased design that, from above, looks like a running track - an oval with straight sides. The channel is 3 m wide, with the maximum water depth of 3 m the volume of water being recirculated is ~800 m3 . At the centreline of the channel it has a total length of 88 m. The water can be circulated at speeds up to 1.5 m/s using four thrusters. A bank of six wind-fans can create a wind-field over a section of the flume of up to 55 km/hr. These systems, combined with air and water refrigeration, enable water-column cooling rates of up to 0.07 °C/hr to be achieved. Tests typically take a day to execute with some frazil events having a duration > 1 hr. The facility is instrumented with air-temperature sensors, highprecision thermistors for water temperature, pressure sensors, acoustic Doppler sensors, above and underwater cameras, a laser scanner for quantifying frazil accretion, and a manual frazil concentration measurement system. In this paper we describe the facility, discuss the challenges, and provide an overview of experiments undertaken in 2022 to create frazil, investigate frazil accretion on a steel trash rack and a concrete intake grill, and to produce datasets for developing and validating frazil ice numerical models. The facility is available for academic, industry and government organisations to use
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.013 |
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".