Derivation of a Look-Up Table for Trans-Critical Heat Transfer in Water-Cooled Tubes
Bibliographic record
Abstract
A trans-critical look-up table (LUT) provides predictions of heat transfer for the region near and beyond the critical point for water. The trans-critical LUT starts at the high subcritical pressure of 19 MPa and extends to supercritical pressures, up to 30 MPa. The intended range of application of the LUT is sufficiently wide to fit all conditions for which conventional single- phase correlations do not apply. This article describes the progress made in deriving a trans- critical LUT for tubes cooled by vertical upflow of high-pressure water. The University of Ottawa (UO) team has compiled a large trans-critical water database and combined it with supercritical water (SCW) databases from other organizations. The expanded database has been carefully examined and duplicate data as well as obvious outliers and data not satisfying a heat balance have been removed. The expanded UO database includes more than 25,000 screened data points. A literature review has been performed in parallel with the LUT compilation and has identified 18 single-phase, near-critical and supercritical (SC) heat transfer correlations. The predictions of these correlations have been compared to the experimental values of the UO expanded database and a statistical error analysis of the comparison results has been performed. The parametric trends of the uncertainty of the more promising correlations are described in this paper. A skeleton LUT has been constructed in which the heat transfer coefficients are assumed to be unique functions of pressure, mass flux, heat flux (or surface temperature) and fluid enthalpy; the LUT domain has been subdivided into sub-domains, each associated with a distinct heat transfer mechanism. The sub-domains include high pressure subcritical regions (liquid, subcritical vapor, and subcritical two-phase regions), SC regions (high-density state or SC liquid-like region, and low-density state or SC vapor-like region) and a near-critical or near-pseudo-critical region. For each region, the best correlations were identified and subsequently used for the construction of the skeleton LUT, which will be updated by experimental data suitably normalized. The parametric trends of the skeleton table have been examined and compared to experimental data.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".