Thermal zoning and window optimization framework for high-rise buildings
Bibliographic record
Abstract
Window sizing and configuration can have a significant influence on building energy consumption. Window selection often has a conflicting objective on heating, cooling, and lighting performance. The smaller window performs better on controlling heat loss in winter and solar heat gain in summer, while the larger window performs better on providing views, daylight, and solar heat gains in winter. Also, the energy consumption analyses of high-rise buildings have some fundamental limitations that include the changes in microclimate parameters with altitude, the treatment of building size, uncertainties associated with the existing convective heat transfer coefficients correlations (CHTC). This study provides a framework for simulation-based optimization of window configuration for a high-rise building to minimize its energy consumption. The technique involves CFD modeling to validate and develop new-CHTCs, a Building Energy Simulation used to assess the energy consumption using the newly developed CHTC, and a numerical optimizer for iterative optimal window configuration selection. The decision parameters are window size and room location. The thermal comfort temperature set points and daylight illuminance are taken as constraints. The proposed approach is implemented as a case study on a single by single room model basis positioned at different heights in an isolated 100 m tall building exposed to Boston, MA microclimate. For a room located on the 2nd, 15th, and 29th floor, an optimum window configuration of 30%, 48%, and 30%, window-to-wall ratio, respectively, are obtained.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".