A novel scheme for simulating the effect of microstructure surface roughness on the heat transfer characteristics of subcooled flow boiling
Bibliographic record
Abstract
In this study, the RPI wall boiling model is developed to investigate the surface roughness effect in the subcooled flow boiling. The surface roughness is simulated by two different schemes. A novel scheme for studying the effect of microstructure surface roughness on subcooled flow boiling is developed. The results of this newly developed scheme are compared with the traditional method and a smooth surface. A randomly distributed roughness is generated and used to present Direct Roughness Simulation. The turbulence stresses are simulated by using the k-ε model. The results of both the Surface Roughness Model and the Direct Roughness Simulation method are compared with those of the smooth surface. The surface roughness model changes the wall function near the wall while Direct Roughness Simulation creates randomly distributed cavities of the wall. Results show that the wall surface temperature decreases, and the average vapor volume fraction and heat transfer coefficient are increased by considering the microstructure surface roughness. The effects of different operating conditions such as pressure, heat flux, mass flux, and subcooled temperature on the characteristics of heat transfer in subcooled flow boiling are studied by considering microstructure surface roughness and smooth surface.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.000 |
| 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".