Experimental Study of Flow Boiling Heat Transfer at Low Heat Fluxes
Bibliographic record
Abstract
This study presents an experimental investigation of heat transfer characteristics at low heat flux conditions.The focus is to compare experimental findings with qualitative descriptions of heat transfer coefficient reported in literature.The study also compares the experimental results with 3 correlations developed based on different theories.For the experimental conditions, R134a was the refrigerant used, heat fluxes ranged from 4.6-8.5 kW/m2 and mass flux from 200-300 kg/m 2 s.The experimental heat transfer coefficient results were also compared with Wojtan et al flow patterns map to determine the flow patterns observed during the study.In covering heat transfer coefficient over a broad range of vapor qualities, the findings revealed that, the qualitative descriptions proposed by different authors do not entirely validate the actual representation of heat transfer coefficient within the experimental conditions considered.At vapor qualities around zero (0), heat transfer coefficient rises to a maximum peak and decreases to a local minimum before increasing as vapor quality increases until it reaches dry-out.The flow pattern predicted are slug flow at low vapor-quality region, intermittent flow at mid vapor quality region and annular, dry-out and mist flow at high vapor quality region.None of the flow boiling correlations considered in this study was able to accurately predict the heat transfer data within a mean absolute error (MAE) of 30%..
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".