Similarity Criteria for Modeling Mixed-Convection Heat Transfer in Ducted Flows of Supercritical Fluids
Bibliographic record
Abstract
A key subject of interest for technologies that involve flows of fluids at the supercritical thermodynamic state is the development of prediction methods that capture the fluid dynamics and convection heat transfer at this state. Due to the elevated temperatures and pressures associated with certain working fluids at this thermodynamic state, surrogate fluids are often used as substitutes for performing experiments during the design stages of prototype development. The success of this approach depends on the development of similarity criteria or fluid-to-fluid models. Similarity criteria for mixed-convection heat transfer in supercritical fluids are proposed based on a set of nondimensional dynamic similarity parameters and state-space parameters developed through our current understanding of the physical mechanisms that affect heat transfer in fluids at this state. The proposed similarity criteria are successfully validated using data from ducted flows of supercritical fluids with configurations having upstream, downstream, or wall-normal-oriented gravitational acceleration.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".