Investigation of Surface Aging Effects on the Repeatability of Saturated Pool Boiling Heat Transfer
Bibliographic record
Abstract
Surface condition has been shown to be the main parameter impacting pool boiling heat transfer performance, namely heat transfer coefficient (HTC) and the critical heat flux (CHF). Many surface modification methods have been developed and studied to improve boiling heat transfer performance by creating nano/micro-scale surface topologies to induce more nucleation sites that can be activated at lower wall superheat, hence improving the HTC.In the current work, the effect of the boiling surface aging on pool boiling performance enhancement is investigated through a repeatability study. First, the design, development, and calibration of a high-accuracy pool boiling apparatus with a relatively large boiling surface area is detailed. This apparatus was subsequently used to investigate the effect of the surface oxidation and contamination on the pool boiling performance for bare copper surfaces. Tests were performed at the saturated conditions using deionized water at atmospheric pressure. The surfaces were tested seven times which amounted to 40 hours of testing. The experiment results demonstrated that the surface aging had a significant effect on the HTC, which reached up to 22% of improvement compared to the freshly prepared surface. However, its effect on CHF is minimal with at least 25 hours of tests are required to get repeatable results within 5%.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".