Incorporating convective heat transfer and humidity effects in urban microclimate modeling: Should we care?
Bibliographic record
Abstract
• Convective heat transfers and humidity effects in microclimate modeling are discussed. • Customized solver buoyantHumidityPimpleFoam models humidity with dynamic BCs. • Solver captures temp & humidity fluctuations, matching real data with high accuracy. • Humidity inclusion boosts prediction of UHI, energy demands, and public health. • Time-dependent BCs allow simulation to reflect varying conditions, enhancing realism. Accurate microclimate data, obtained through observation or CFD models, is crucial for urban design and environmental improvements. One approach to quantifying microclimate conditions involves the use of isothermal CFD simulations combined with convective heat transfer and relative humidity modeling, implemented via the buoyantHumidityPimpleFoam solver in OpenFOAM. This research investigates the additional complexity when incorporating these factors into unsteady-state modeling for urban microclimate simulations. This study reports the approaches through simulations employing a simplified canyon model. The study site is the campus of the Toronto Metropolitan University campus in Toronto, Ontario, Canada. The simulation data is validated using real-time data collected from the weather station located on the roof of one of the buildings on the downtown campus. By comparing the simulated data with real-time observations, the study assesses the effectiveness of the new features and evaluates their suitability for integration into existing urban microclimate modeling frameworks. The results show that adding humidity not only improves the model realism but also greatly increases its ability to predict complex urban microclimate dynamics. These findings highlight the importance of this approach for applications such as thermal comfort optimization, public health planning, and climate resilience strategies, demonstrating its potential to advance urban microclimate simulations.
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.
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".