Airflow Prediction in Buildings for Natural Ventilation Design: Wind Tunnel Measurements and Simulation
Bibliographic record
Abstract
Natural/hybrid ventilation systems with motorized operable windows, designed and controlled to utilize the potential for cross-ventilation, represent an area of significant interest in sustainable building design as they can substantially reduce energy consumption for cooling and ventilation. Presently, there is a need for accurate prediction models that can contribute to the improvement of indoor environmental quality and energy performance of buildings, and the increased use of low energy, naturally driven cooling systems. In this regard, the present research aims to enhance airflow prediction accuracy for natural ventilation design of buildings considering advanced experimental and simulation methods. The study considers a Boundary Layer Wind Tunnel (BLWT) approach to investigate the wind-induced driving forces and ventilation flow rates in various building models subject to cross-ventilation. The Particle Image Velocimetry (PIV) technique was used for the first time to evaluate accurately the air velocity field for various cross-ventilation configurations. Detailed measurements were performed to determine mean and fluctuating internal pressures since they affect airflow prediction, occupants' thermal comfort, as well as cladding and structural wind load design of buildings with operable windows. PIV data for the inflow velocity were compared with those by using conventional techniques (e.g., hot-film anemometry) and results show differences, between the two methods, up to a factor of 2.7. This clearly indicates that accuracy can be enhanced with carefully conducted PIV experiments. The study provides guidelines for implementation of cross-ventilation in design practice. These guidelines were developed on the basis of parametric experimental investigations, which quantify the impact of relative inlet-to-outlet size and location on ventilation airflow rates and thermal comfort of building occupants. The study develops a novel simulation methodology combined with a sensitivity analysis focused on modelling issues, such as the impact of zoning assumptions, to predict the envelope pressures and related air-exchange rates in buildings due to wind, stack, and mechanical system effects. An integrated simulation tool (ESP-r) was used to model the airflow/energy interactions in an existing high-rise residential building, and simulation results agree well with monitoring data.
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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 | 0.000 |
| 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 | 0.001 | 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".