A Smart Kinetic Double-Skin Window System for Enhancing Natural Ventilation in Sustainable Buildings
Bibliographic record
Abstract
This study presents the design and performance evaluation of a smart kinetic double-skin window system designed to enhance natural ventilation in buildings, especially those limited to single-sided airflow. The system dynamically adjusts external blade angles in response to real-time wind conditions, using environmental sensors and automated control to optimise airflow distribution and energy performance. Computational fluid dynamics (CFD) simulations were conducted for two blade configurations (7° and 15°) under varying wind speeds and directions. Results show that the 15° configuration enhances airflow reach and achieves up to 40% higher air change rates (ACH) compared to the 7°, making it more suitable for high-demand ventilation scenarios. In contrast, The 7° configuration produces lower but more uniform airflow, which is more appropriate for occupant comfort in residential or office environments. Detailed analysis of velocity fields, pressure distributions, and airflow paths confirms that the system effectively adapts to wind direction, maintaining balanced ventilation through integrated airflow channels. The simulations were validated against experimental data, achieving a Close correlation. While thermal and buoyancy effects were not included, future work will extend the model to hybrid ventilation scenarios. The proposed system demonstrates significant potential for sustainable ventilation applications in new and retrofitted building envelopes.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".