Passive Cooling Strategy for Reducing Load in a Building with an Integrated PCM on the Rooftop
Bibliographic record
Abstract
Increasing energy consumption is a critical worldwide challenge.Based on the gathered data, heating, ventilation, and air conditioning systems in residential sectors consume 40% of the provided energy, primarily from non-renewable sources.To overcome this challenge, scientists are actively working to reduce reliance on fossil fuels by adopting renewable energy resources.In this research article, the thermal performance of a natural ventilation system within a building in Las Vegas by implementing phase change material during the hottest day of summer is investigated.The city of Las Vegas is selected because of its hot and arid climate.This system includes a solar chimney, an earth-to-air heat exchanger, and phase change material.This system is simulated in ANSYS Fluent software, and the RNG 𝑘𝑘 -𝜀𝜀 is selected as the turbulence model.The n-Henicosane is chosen as a phase change material to evaluate its impact on the cooling load when placed on the roof.Furthermore, the effect of phase change material layer thickness on the thermal performance of the natural cooling system is studied.The natural ventilation system reduces the peak indoor temperature by 15.5 K and saves 28.19 kW of energy during the day.Based on the simulations, the optimum thickness obtained is 70 mm.After integrating phase change material with this thickness in the building, the maximum indoor temperature is further reduced by 2.5 K to reach 298.5 K.This system at the peak load cuts 81% of the energy consumption and saves 33.21 kW in 24 hours.In conclusion, incorporating PCM into a building rooftop with a natural ventilation system is a prudent option in reducing the indoor temperature and energy demand in a hot and arid climate.
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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 | 0.000 |
| Open science | 0.001 | 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".