Optimization of Window Confirguration in Buildings for Sustainable Thermal and Lighting Performance
Bibliographic record
Abstract
In recent years, there is an urban architectural evolution towards significant use of glazing in high-rise buildings. Windows play a critical role in moderating the elements of the climate. Although good for outdoor viewing and daylighting, glazing has very little ability to control heat flow and solar heat gain. As a result, about 20 - 40% of the energy in a building is wasted through windows. Finding an optimal configuration of windows is a complex task due to its conflicting objectives, such as outdoor view, daylighting, and thermal comfort demands. Further buildings interact with the microclimate in a complex manner, the aerodynamics of the building as well as the location and shape of the window affect its energy performance primarily through convective heat transfer coefficient (CHTC). Various methods have been proposed to calculate CHTC in literature, but with significant differences, which can cause errors in energy demand calculations in the order of 20 - 40%. Most CHTCs used by building energy simulations (BES) tools are primarily derived from the experimental and numerical analysis carried out on low-rise buildings with smooth façade surfaces and are not suitable for high-rise buildings with various intricate surface architectural details. This thesis aims to develop a new simulation-based optimization framework of window configuration in a high-rise building that meets the objective of minimizing the energy consumption of heating, cooling, and electric lighting. This framework integrates high resolution computational fluid dynamics (CFD) and heat transfer simulations, BES, and numerical optimizer. In this thesis, the effect of different building heights, external architectural features, and window configuration on annual energy consumption are investigated. A new concept of local-CHTC zoning, a CFD based procedure for accurate CHTC-U10 correlations evaluation, and an optimum window configuration procedure for high-rise buildings are presented. Overall, the research accomplished in this thesis provides an advancement in knowledge of accurate energy consumption analysis and optimization of window configuration in buildings, particularly in high-rise buildings using a passive strategy that can satisfy the objectives of minimum energy consumption and maximum comfort in a sustainable way.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".