Multizone modelling of a hybrid ventilated high-rise building based on full-scale measurements for predictive control
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Hybrid ventilation is an effective approach to reduce cooling energy consumption by combining natural and mechanical ventilation. Previous studies of full-scale whole-building measurements of high-rise hybrid ventilation are quite limited due to the complexities of buildings and variable ambient conditions. As a result, validated and accurate whole-building simulations of hybrid ventilation often cannot be found in the literature. This paper reports a series of full-scale measurements of hybrid ventilation in a 17-storey high-rise building and associated whole-building simulations by 15-zone detailed and a 5-zone simplified multizone models. The paper is one of the first studies of using multizone models and real-world full-scale data and sharing key operational and performance experience and case studies of high-rise hybrid ventilation. Both the test data and the validated simulation models can be used for the comparison and validation of simulation models. The 5-zone simplified model developed from this study was able to model such a complex high-rise building by only a few zones, making possible the on-line model predictive control of a high-rise building. This was illustrated in this paper by an example of optimizing the uniformity of the hybrid ventilation on different floors by modifying inlet areas.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it