Jet Impingement Cooling Enhanced with Nano-Encapsulated PCM
Bibliographic record
Abstract
In the present study, the laminar flow and heat transfer of water jet impingement enhanced with nano-encapsulated phase change material (NEPCM) slurry on a hot plate is analytically investigated for the first time. A similarity solution approach is applied to momentum and energy equations in order to determine the flow velocity and heat transfer fields. The effect of different physical parameters such as jet velocity, Reynolds number, jet inlet temperature, and the NEPCM concentration on the cooling performance of the impinging jet are investigated. The volume fraction of NEPCM particles plays an essential role in the flow and heat transfer fields. The results show that NEPCM slurry can significantly enhance the cooling performance of the system as it improves the latent heat storage capacity of the liquid jet. However, the maximum cooling performance of the system is achieved under an optimum NEPCM concentration (15%). A further increase in NEPCM volume fraction has an unfavorable effect due to increasing the viscosity and reducing the conductivity simultaneously. The effect of adding nano-metal particles on the heat transfer performance is also investigated and compared with NEPCM slurry. NEPCM slurry shows a better result in its maximum performance. Compared with the water jet, adding nano and NEPCM particles would overall enhance the system’s thermal performance by 16% and 7%, respectively.
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 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 | 0.001 |
| 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".