Evaluating the Energy Efficiency of PCM‐Integrated Lightweight Steel‐Framed Building in Eight Different Cities of Warm Summer Humid Continental Climate
Bibliographic record
Abstract
Phase change materials have been applied to a building framework to decrease energy and fossil fuel consumption as well as make the building sector more sustainable. Lightweight structures are attractive and increasingly being used in residential buildings. Hence, in this research, the energy efficiency and thermal performance of buildings located in eight various cities (Helsinki, Kiev, Saint Petersburg, Moscow, Stockholm, Toronto, Montreal, and Kiev) of warm summer humid continental climate (Dfb) were evaluated. The impact of heating and cooling energy savings pattern on the selection of optimum phase change material for each city has been demonstrated. In addition, the impact of volume of PCM, precisely the effect of varying and constant volume, on energy savings was assessed for the lightweight steel‐framed building. Simulations were performed in EnergyPlus by applying eleven melting temperature ranges of PCM. Test results demonstrated that energy savings were higher in the swing season and the maximum temperature reduced during these months was 3.3°C. Heating and cooling energy savings were found to strongly influence the selection of optimum PCM. In cities where cooling energy savings were the highest, the optimum PCMs were PCMs 24‐26 while in cities where heating energy savings were the highest, the optimum PCM was found to be PCM 21. For constant volume, the performance of optimum PCM raised when the surface area was enlarged, while the thickness of PCM was reduced. Overall, the application of PCM into lightweight steel‐framed residential structure located in warm summer humid continental climate region is a feasible option.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".