Investigation of Latent Heat Thermal Energy Storage System for AirConditioning Applications
Bibliographic record
Abstract
In this study an experimental setup was constructed to investigate the solidification process of PCM using two different types of heat exchangers: pipe heat exchanger and horizontal finned-pipe heat exchanger. The PCM used in this study is Rubitherm (RT-18) that has a melting point of 18°C, and the heat transfer fluid (HTF) is water. To investigate the performance of pipe heat exchanger versus finned-pipe heat exchanger, two experiments were performed under identical initial, thermal and boundary conditions. The PCM is cooled down from 20°C to 5°C in both heat exchangers. Result of the temperature distribution as a function of time during the solidification process inside the TES system is presented and investigated. Visualization of the solidification process inside the TES system is also presented. Generally, the PCM is solid when its temperature is lower than the melting point, and it is liquid when its temperature is higher. PCM stores thermal energy during melting and releases heat as it solidifies. Both visualization and periodic temperature distribution results showed that as time progresses, the amount of the solid PCM increases in both heat exchangers. At a certain time, finnedpipe heat exchanger has higher amount of the solid PCM than the pipe heat exchanger. Also, the solidification time is reduced significantly when fins are added to the TES system. After 75,000 seconds of solidification, the average PCM temperature of pipe heat exchanger reached to 8.5°C. Whereas, the average PCM temperature of the finned-pipe heat exchanger reached to 8.5°C after 19,000 seconds of solidification.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".