A Model for Thermal-Hydraulic Characteristics of Offset Strip Fin Arrays for Large Prandtl Number Liquids
Bibliographic record
Abstract
An extended model for predicting thermal-hydraulic characteristics of offset strip fin arrays is developed for fluids with Prandtl number greater than unity. This new model is based on the asymptotic behavior for the laminar and turbulent wake regions with an empirically derived correlation for the effect of Prandtl number suppression on the j factor. The proposed models are compared with existing data for air, water, and polyalphaolefin (PAO), and new data for 5W30 engine oil. Model predictions are within ±20 percent or better for most data sets. The new models for f and j cover a wide range of Reynolds number, 1 < Re < 10,000 and Prandtl number, 0.7 < Pr < 620. A total of 39 unique data sets were used in the present model development.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".