The influence of the climate, the materials of the walls, and the gas effects of double and triple-glazed windows in terms of energy evaluation and economic expenses
Bibliographic record
Abstract
The computation of heating and cooling loads for office buildings is influenced by the significant population and diverse occupations of the users throughout various time durations. The first step in this investigation involves using specialized software to simulate a model office structure. To improve the thermal insulation of the building's outside covering and the arrangement of the windows, modifications are performed by considering the appropriate assumptions. Furthermore, this study examined samples of double- and triple-glazed windows using argon, xenon, krypton, air, and vacuum as insulating materials. In addition, the thermal efficiency of this construction was evaluated using common wall materials such as Autoclaved Aerated Concrete (AAC), Educational Credential Assessment (ECA), and Cellular Lightweight Concrete (CLC) blocks. This research conducted a comparison and analysis of four different climates, including Canada, Saudi Arabia, Greece, Sweden, and Iran, with the climate of the study location. The aim of this study is to enhance thermal loads and energy efficiency in a simulated construction. The research demonstrates that arranging windows correctly, implementing efficient wall insulation, and ensuring appropriate climatic conditions may result in a reduction in cooling and heating loads by around 4–8 %. Finally, the economic study demonstrates that the time it takes to recoup the investment for different configurations ranges from 2.4 to 9.3 years. In summary, this research provides significant insights on how to decrease energy consumption in buildings and highlights different approaches that architects and engineers could use to build energy-efficient and sustainable structures.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".