Lookup Tables for Predicting CHF and Film-Boiling Heat Transfer: Past, Present, and Future
Why this work is in the frame
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Bibliographic record
Abstract
Lookup tables (LUTs) have been used widely for the prediction of critical heat flux (CHF) and film-boiling heat transfer for water-cooled tubes. LUTs are basically normalized data banks. They eliminate the need to choose between the many different CHF and film-boiling heat transfer prediction methods available.The LUTs have many advantages; e.g., (a) they are simple to use, (b) there is no iteration required, (c) they have a wide range of applications, (d) they may be applied to nonaqueous fluids using fluid-to-fluid modeling relationships, and (e) they are based on a very large database. Concerns associated with the use of LUTs include (a) there are fluctuations in the value of the CHF or film-boiling heat transfer coefficient (HTC) with pressure, mass flux, and quality, (b) there are large variations in the CHF or the film-boiling HTC between the adjacent table entries, and (c) there is a lack or scarcity of data at certain flow conditions.Work on the LUTs is continuing. This will resolve the aforementioned concerns and improve the LUT prediction capability. This work concentrates on better smoothing of the LUT entries, increasing the database, and improving models at conditions where data are sparse or absent.
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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 it