Hybrid geometric optimization of wavy tubes with Y-shaped fins for enhanced solidification in latent heat storage systems
Bibliographic record
Abstract
This study addresses the inherent low thermal conductivity of phase change materials (PCMs) by introducing a hybrid passive enhancement strategy that integrates wavy inner tube geometry with Y-shaped fin arrays within a vertical double-pipe heat exchanger. The novelty lies in optimizing both macro-scale surface area and micro-scale heat diffusion paths to accelerate solidification. A comprehensive parametric analysis was conducted using ANSYS Fluent with enthalpy-porosity method, examining 18 cases with varying wave amplitudes (2.5–10 mm), fin lengths, Y-fin angles (15°–30°), and fin-to-shell distances (2–6 mm). Results show that Case 17, featuring a 10 mm wave amplitude, 2 mm fin-shell gap, and 30° Y-fin angle, achieved complete solidification in 1565s (42 % faster than the smooth-wall baseline and recorded a peak discharge rate of 113.14W, nearly tripling the base case's 38.3W. These improvements are attributed to enhanced natural convection, reduced thermal resistance, and uniform heat distribution. Compared to previously reported designs, the proposed configuration offers a synergistic gain in both heat transfer rate and PCM utilization. This work is significant as it demonstrates that coupling geometric and fin-based strategies in a hybrid design can substantially overcome PCM thermal limitations, paving the way for more efficient and compact thermal energy storage systems.
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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 | 0.000 |
| 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) | 0.001 | 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".