Enhancing Air Conditioning Performance in Hot Climates through Salt Hydrate PCM-based Latent Heat Thermal Energy Storage: A Numerical Investigation
Bibliographic record
Abstract
The cooling of lightweight buildings in extremely hot climate regions poses a significant challenge for the future.As major energy consumers, air conditioning (AC) systems present an opportunity for substantial energy savings through even minor improvements in their performance.This numerical study investigates the use of latent heat thermal energy storage (LHTES) to improve air conditioning performance in hot conditions.LHTES aims to reduce buildings' excessive cooling or heating demands by utilizing phase change materials (PCMs).Specifically, this evaluates the performance for pre-cooling the supply air in AC systems using two types of PCMs salt hydrate (CaCl2.6H2O)with a melting range of 27.7 °C -32.23°C and RT-31 with a melting range of 29°C -33°C.Four designs of an AC duct containing enclosures of PCM have been modelled and simulated using ANSYS/Fluent at different inlet air velocities.The study investigates the impact of incorporating PCM enclosures in the AC duct on the outlet air temperature and the system's maximum coefficient of performance (COPmax).The findings reveal that the PCM-based air-pre-cooling model effectively reduces peak cooling demand, improves the COPmax, and achieves an 8.2°C drop in the supply air temperature at a velocity of 1 m/s in the case of CaCl2.6H2O with design 1 and 7.2°C drop in supply air using RT-31 as PCM.The results suggest that PCM-based LHTES systems are a promising technology for enhancing AC performance in hot environments, leading to significant energy savings and improved thermal comfort in buildings.
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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 | 0.001 |
| 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".