Experimental and computational study of convective heat fluxes in swirling two‐phase flows
Bibliographic record
Abstract
Abstract The convective mode of heat transfer is mainly due to the bulk motion of the fluid. Its turbulent nature and enhanced heat transfer coefficients have always attracted the academic, scientific community, and industrialists for many decades. The current research is based on the experimental and theoretical investigations on the turbulent convective heat transfer in swirling (60–300 rpm) steam (1–3 bars) injection into cocurrently flowing water. An exponential increase in the convective heat transfers up till the most swollen part of the swirling steam‐water volume of fluids has been observed. However, the convective heat transfer of the remaining part of the steam's plume shows an almost stagnant decreasing trend. The range of Rayleigh number that supports the transition in trends of the convective heat fluxes is 2.84 × 10 11 –3.71 × 10 11 . This transition affects the magnitude of the convective heat fluxes and the extent of the effective momentum fluxes, which is evident in the dominant role of the flow instabilities acting across the length of the steam's plume. Computational Fluid Dynamic analysis also has supported the exhibition of the heat fluxes magnitudes under the influence of the interacting Kelvin–Helmholtz instabilities and inertial instabilities across and along with the cocurrently acting shear layer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".