Physical and Mathematical Modeling in Development of Metal Delivery System for Single Belt Casting Process
Bibliographic record
Abstract
A piston type metal delivery system has been designed for the single belt strip caster at the McGill Metals Processing Centre (MMPC) of McGill University, and water modeling equipment set up to validate the proposed enclosed metal delivery system. Water flows were inspected using dye injection, and velocity fields were measured with a dual Nd: YAG PIV (Particle Image Velocimetry) system using the metal delivery nozzles proposed for optimum delivery of liquid steel. Mathematical modeling was also carried out to help anticipate flow patterns in the output chamber and the exit nozzle. When a slot type nozzle was used without a flow-modifier, a strong jet flow impinging on the substrate was generated. Bubbles generated during the initial filling of the delivery system were entrapped within re-circulatory flows in the output chamber. Bubbles were removed using a three hole type nozzle, but the impinging jet flow still remained. The FD type nozzle which had a multi-channel as a flow modifier, a slot type inlet nozzle located at the right side of the multi-channel, and an upper chamber which had the same height as the inlet slot length, was found to be outstanding for bubble removal, moderating strongly impinging flows, and providing uniform exit flows. Calculated flows were shown to be good agreement with PIV measurements.
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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 | 0.000 |
| 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 | 0.000 | 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".