Smart wetting of permeable pavements as an 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 is built upon 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 and to a radiative exchange model, determining the net solar and thermal radiation on each urban surface. A two-layer pavement system, previously optimized for maximal evaporative cooling applying the principles of capillary pumping and capillary break, is considered to design a smart wetting protocol answering the questions “when,” “how much,” and “how long” a pavement should be artificially wetted. It was found for the current optimized pavement solutions that a daily amount of 6 mm wetting over 10 min in the morning, preferentially between 8:00 and 10:00, guarantees a maximal evaporative cooling for 24 h during a heat wave.
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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 | 0.000 | 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 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".