Effect of the Quasi-Petal Heat Transfer Tube on the Melting Process of the Nano-Enhanced Phase Change Substance in a Thermal Energy Storage Unit
Bibliographic record
Abstract
The melting heat transfer of nano-enhanced phase change materials was addressed in a thermal energy storage unit. A heated U-shape tube was placed in a cylindrical shell. The cross-section of the tube is a petal-shape, which can have different amplitudes and wave numbers. The shell is filled with capric acid with a fusion temperature of 32 °C. The copper (Cu)/graphene oxide (GO) type nanoparticles were added to capric acid to improve its heat transfer properties. The enthalpy-porosity approach was used to model the phase change heat transfer in the presence of natural convection heat transfer effects. A novel mesh adaptation method was used to track the phase change melting front and produce high-quality mesh at the phase change region. The impacts of the volume fraction of nanoparticles, the amplitude and number of petals, the distance between tubes, and the angle of tube placements were investigated on the thermal energy rate and melting-time in the thermal energy storage unit. An average charging power can be raised by up to 45% by using petal shape tubes compared to a plain tube. The nanoadditives could improve the heat transfer by 7% for Cu and 11% for GO nanoparticles compared to the pure phase change material.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".