The Thermal Charging Performance of Finned Conical Thermal Storage System Filled with Nano-Enhanced Phase Change Material
Bibliographic record
Abstract
A latent heat thermal energy storage (LHTES) unit can store a notable amount of heat in a compact volume. However, the charging time could be tediously long due to weak heat transfer. Thus, an improvement of heat transfer and a reduction in charging time is an essential task. The present research aims to improve the thermal charging of a conical shell-tube LHTES unit by optimizing the shell-shape and fin-inclination angle in the presence of nanoadditives. The governing equations for the natural convection heat transfer and phase change heat transfer are written as partial differential equations. The finite element method is applied to solve the equations numerically. The Taguchi optimization approach is then invoked to optimize the fin-inclination angle, shell aspect ratio, and the type and volume fraction of nanoparticles. The results showed that the shell-aspect ratio and fin inclination angle are the most important design parameters influencing the charging time. The charging time could be changed by 40% by variation of design parameters. Interestingly a conical shell with a small radius at the bottom and a large radius at the top (small aspect ratio) is the best shell design. However, a too-small aspect ratio could entrap the liquid-PCM between fins and increase the charging time. An optimum volume fraction of 4% is found for nanoparticle concentration.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".