Approximating CFD simulations of natural ventilation: A deep surrogate model with spatial attention mechanism
Bibliographic record
Abstract
Building natural ventilation is a sustainable approach to reducing energy use and emissions from buildings by minimizing reliance on energy-intensive systems. Computational Fluid Dynamics (CFD) simulations are often used to predict natural ventilation, enhance building design, and improve indoor air quality. However, CFD simulations are time-consuming and computationally resource-intensive due to the number of spatial and temporal discretization and iterations required to solve the governing Reynolds Averaged Navier–Stokes (RANS) equations. Additionally, CFD simulations require specialized knowledge, limiting the number of concepts designers can test. This study proposes a UNet-based surrogate model with a spatial attention mechanism to overcome the limitation of CFD simulations by approximating RANS simulations of natural ventilation in cross-ventilated buildings. The proposed surrogate model was trained with data from CFD simulations performed on buildings with multiple opening sizes and wind from different directions. The model was then evaluated while comparing it with the commonly used UNet with channel-wise attention as a baseline. Based on the evaluation results, the Spatial Attention UNet model outperformed the baseline model in predicting velocity flow fields, achieving a mean absolute percentage error of 4.7% compared to 14.7% for the whole domain and 17.0% compared to 32.1% within the building. Furthermore, the trained model achieved a tremendous speed, reducing more than an hour of CFD simulation into orders of milliseconds. The result proved that the proposed model could save designers effort and time while allowing them to test several design concepts quickly, sacrificing only a small amount of accuracy. • Spatial attention UNet offers a faster option to RANS natural ventilation simulation. • Proposed surrogate model reduces simulation time from over an hour to milliseconds. • The model enables designers to test multiple designs to enhance natural ventilation.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".