Pool boiling performance of oxide nanofluid on a downward-facing heating surface
Bibliographic record
Abstract
Abstract In this study, the pool boiling performance of oxide nanofluid was investigated, the heating surface is a 5 × 30 mm stainless steel heating surface. Three kinds of nanofluids were selected to explore their critical heat flux (CHF) and heat transfer coefficient (HTC), which were TiO 2 , SiO 2 , Al 2 O 3 . We observed that these nanofluids enhanced CHF compared to R·O water, and Al 2 O 3 case has the most significant enhancement (up to 66.7%), furthermore, the HTC was also enhanced. The number of bubbles in nanofluid case was relatively less than that in R·O water case, but the bubbles were much larger. The heating surface was characterized and it was found that there were nano-particles deposited, and surface roughness decreased. The wettability also decreased with the increase in CHF.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".