Parametric investigation of internal Y-shaped fin configurations under natural convection in a concentric annulus
Bibliographic record
Abstract
Natural convection heat transfer inside concentric annular enclosures has been well researched for decades. Such enclosures have been employed as intermediate layers between fluid streams in heat exchangers, particularly in latent heat thermal energy storage applications. Convection heat transfer coefficients originating from natural convection are relatively low, which drives the need for heat transfer enhancements by using extended surfaces (e.g., fins, heat sinks). In this study, numerical simulations are performed to investigate the natural convection heat transfer inside a concentric annulus in the presence of Y-shaped fins. Each Y-shaped fin has four important geometric parameters: (i) length of the fin base a, (ii) length of the fin branches b (i.e., the V-section), (iii) primary fin spacing angle θ, and (iv) secondary fin angle or branch angle α. Steady-state simulations are conducted using COMSOL Multiphysics® with constant temperature boundary conditions to generate the laminar natural convection profiles in the enclosure filled with air. The average Nusselt numbers, calculated at both the inner and outer walls of the enclosure, are used to gauge the heat transfer. The model is built with thin layer approximated fins first and subsequently compared to a modified model with fins with finite thickness. The optimized case using the thick fin model is found to yield a percent increase in Nu of 358.8% compared to the case with no fins. Lastly, the entropy generation is considered to determine the second law efficiency of the system, which is approximately 91%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".