Comparison and Improvements of Correlations for Film Boiling in Tubes
Bibliographic record
Abstract
Two methodologies to predict film boiling heat-transfer coefficient have been assessed against experimental wall-temperature measurements obtained under steady-state conditions with water flow inside vertical tubes. One of these methodologies employs heat flux as the independent parameter while the other uses wall temperature as the independent parameter. The film boiling heat transfer consists of developing and fully developed film-boiling regions. Film boiling heat-transfer coefficients are predicted using the film boiling look-up tables for fully developed flow. Developing film-boiling effect is accounted for using modification factors to the fully developed heat-transfer coefficient. Wall-temperature distributions along uniformly heated tubes were established using a semi-analytical scheme and compared against measurements. Both methodologies have provided good predictions. However, the overall prediction accuracy for the heat-flux-based correlation is slightly better than that for the wall-temperature-based correlation. Wall temperatures predicted with the heat-flux-based correlation follow closely measurements at the developing post-dryout region. The wall superheat correlation predicts a sharp temperature rise once the critical heat flux is exceeded, resulting in discrepancies between predictions and measurements of wall temperature and overpredictions of the maximum temperature. The wall-temperature-based modification factor for the developing film-boiling region has been revised using the tube heat-transfer database to improve the prediction accuracy of the wall temperature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".