Ice slurry production via opposed-jets spray: An experimental and theoretical study
Bibliographic record
Abstract
The opposed nozzle impinging method is a novel developed technology for ice slurry preparation, which has more advantages than the traditional method, such as increasing the effective heat transfer contact area, significantly reducing ice plugging, and improving the heat transfer efficiency. The injection angle, flow rate and injector distance are investigated experimentally in this work due to their great influence on system performance. Consequently, the optimum parameters and the functional relationship between the system performance parameters and the key operating parameters are obtained. It is shown that increasing the spray angle of the gas and liquid streams increases the ice packing fraction (IPF) by up to 6.09 %. The increase in the IPF growth rate is 2.974 times greater when the gas spray angle is changed from 45° to 105°, compared to a similar change in the liquid nozzle angle. The cold energy utilization rate of the ice slurry generated increases by 16.02 % when the gas–liquid spray angle increases from 45° to 105°. Moreover, the order of influencing factors on the IPF and heat transfer efficiency according to orthogonal experiments from the highest to lowest is distance between nozzles, liquid nozzle flow rate, air nozzle flow rate. The optimal values for the nozzle distance, air nozzle flow rate and liquid nozzle flow rate are 8 cm, 6.4 m 3 /h, and 48L/h, respectively. Moreover, a novel mathematical model is built up to explain theoretically the influence of spray angles, fluid properties and construction parameters on ice content.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| 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".