AI-enhanced discharge performance in hexagonal shell and finned tube latent heat storage using combined longitudinal smooth and Y-shaped fins
Why this work is in the frame
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Bibliographic record
Abstract
The aim of this study is to enhance the discharge performance of shell and finned-tube heat exchanger using hexagon shell. Different fins arrangement including combinations of longitudinal straight and Y-shaped fins in uniform and non-uniform forms are assessed. An artificial intelligence approach based on artificial intelligence networks is used to learn the overall state of solution and behavior of the discharge rate respect to the control parameters and further enhance the design. The innovation consists of the methodical investigation of Y-shaped fin geometries to concurrently improve conductivity and mitigate convection, in contrast to traditional straight-fin configurations. The findings indicate that Y-shaped fins with 0.5L stems at 45° angles exhibit enhanced performance, diminishing solidification time by 95.3% and augmenting heat recovery rates by 2,277% (to 302.89 W) in comparison to finless systems. The AI results further confirm a fin with a stem in the range of 0.3L-0.5L and angle of 45° could provide the best discharging performance. The principal findings indicate that the 0.5L-45° arrangement attains excellent thermal homogeneity (inter-branch gradients < 5 K) and minimum convection disruption (72% flow obstruction), whereas wider angles or longer stems diminish efficiency due to convective bypass and thermal shadowing.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it