Design of smart wetting of building materials as evaporative cooling measure for improving the urban climate during heat waves
Bibliographic record
Abstract
An urban microclimate model is used to design a smart wetting protocol for multilayer street pavements in order to maximize the evaporative cooling effect as a mitigation measure for thermal discomfort during heat waves. The microclimate model covers a computational fluid dynamics (CFD) model for solving the turbulent air, heat and moisture flow in the air domain of a street canyon. The CFD model is coupled to a model for heat and moisture transport in porous urban materials, to a radiative exchange model, determining the net solar and longwave radiation on each urban surface and to a wind driven rain model able to determine the wetting flux on each surface during a rain event. We first evaluate the evaporative cooling potential for different pavement systems during normal summer conditions after a long rain event during night in order to select an optimal pavement system. Then, we design a smart wetting protocol answering the questions ‘when’, ‘how much’ and ‘how long’ a pavement should be artificially wetted for having a maximum cooling effect. We found that a daily amount of 5mm wetting over 10 minutes in the morning, preferentially between 8:00 and 10:00 am, guarantees a maximal evaporative cooling for one day and night during a heat wave.
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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 | 0.001 | 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".