A Parametric Analysis of Internal Airflow Patterns in Supertall Passive House Multi-unit Residential Buildings
Bibliographic record
Abstract
In the face of accelerating climate change, a growing need for high density housing, and the demand for excellent indoor air quality through suitable ventilation, this study simulates internal airflow patterns of a supertall, multi-unit residential building (MURB) using parametric analysis on 87 steady-state models in CONTAM. Research is expanded from prior investigations of cold climate MURBs to the CTBUH 300 m supertall height criteria, and Passive House level airtightness. Corridor pressurization and direct-to-suite ventilation systems are modeled to predict the suitability of each system for this typology. Stepped outdoor temperature ranges identify the cold climate impacts The findings suggest vertically compartmentalization of core shafts most effectively reduce stack pressures leading to internal airflow spread. Secondly, improved airtightness of internal pressure boundaries should be applied from the core outward, in conjunction with a slightly pressurized corridor and balanced in-suite ventilation. Improved airtightness of suite doors is highlighted as a concern where other mitigation measures have not been applied, as stack pressures can become too great for ease of door operability. This study emphasizes that a Passive House envelope alone will not eliminate stack-induced suite-originating contaminated airflow.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".