On the stability of nanofluid ice slurry produced via impinging stream method under thermal and phase-change cycles
Bibliographic record
Abstract
Nanofluid ice slurry has shown promising potential due to superior heat transfer and lower supercooling degree. The stability of the nanofluid ice slurry is of paramount importance to ensure high performance for practical applications. This study evaluates the stability (flow and thermal properties) of nanofluid ice slurry produced via a dynamic, impinging-stream method and compares with its conventional counterpart, i.e., the static, non-impinging-steam approach. Thermal cycling (heating-cooling) with phase change (freezing-thawing) cycles were carried out to measure absorbance, thermal conductivity, and viscosity. The results showed that when using the method of producing nanofluid ice slurry by impinging flow, nanofluid ice slurry with concentrations of 0.1, 0.2, and 0.3 w.t.% decreased in absorbance by 17%, 21%, and 26%, in thermal conductivity by 0.5%, 1.4%, and 2.17%, and in viscosity by 3.6%, 5.3%, and 10.4%, respectively, after nine freezing and thawing cycles. Additionally, for instance, for a nanofluid with a concentration of 0.2 w.t%, after 9 phase change cycles, the decrease in absorbance of the nanofluid solution using dynamic cycling was 7.1, 3.7, and 1.6 times higher than that of static cycling with an IPF of 25%, 50%, and 75%, respectively (thermal conductivity 4.1, 2.4, and 1.4 times; viscosity 2.8, 1.8, and 1.3 times). Higher nanoparticle concentration reduces the stability of the nanofluid ice slurry; the degree of reduction is first increased and then stabilized after six cycles. It was found that the dynamic method had an impact on the stability of the nanofluid compared with the static approach. Furthermore, empirical correlations were developed to predict the thermophysical properties based on phase-change cycles for practical applications.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".