Development of a novel continuous nanofluid ice slurry generator: Experimental and theoretical studies
Bibliographic record
Abstract
Ice slurry has remarkable energy storage density due to significant amount of latent heat during phase change, making it a unique environmentally friendly alternative. This technology can be used for renewable cooling in buildings, foods, and medical products. Nanofluid ice slurry offers higher thermal conductivity and lower supercooling degree as compared to conventional ice slurry which improves its effectiveness in applications. Therefore, an efficient nanofluid ice slurry generator needs to be developed. In this study, a novel continuous nanofluid ice slurry generator is developed using an opposed nozzle impinging jet where Alumina (Al2O3) nanofluid (ANF) impinged with cold air jet. To achieve an optimal thermal performance and maximize slurry production, a model of the slurry generator should be developed. To accomplish this, a novel mathematical framework incorporating multiscale nanofluid freezing, i.e. liquid supercooling, nucleation, recalescence, equilibrium freezing, and solid subcooling, along with a spray-droplet dynamics model is developed and validated against experimental data. The results suggest that the performance of the impinging jet nanofluid ice slurry generator is superior to that of the non-impinging counterpart (up to 3.4 times more ice produced). The addition of Alumina nanoparticle expedites freezing time by about 20%. The ice packing fraction was found to increase with nanofluid concentration and peak at concentration of 0.2 weight percentage. The ice packing fraction decreases with the increase of the initial temperatures of nanofluid and air. Conducting a parametric study, it is shown that the ice slurry production can be maximized via an appropriate selection of the generator size, spray pressure and configuration, and nanoparticle concentration.
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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 | 0.001 |
| 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".