Comparison of different urban climate modelling tools for predicting pedestrian thermal comfort
Bibliographic record
Abstract
Estimating pedestrian thermal comfort in urban settings is crucial for climate adapted planning of buildings and districts. Practitioners and urban planners are increasingly relying on simulation tools to assess the local thermal conditions in projects. Simulation tools are now widely available, however, performance comparisons between tools are rare regarding computational load and accuracy. This makes it difficult to choose the optimal tool for a certain urban planning project considering urban heat mitigation.In this study, to specifically assess performance of the most relevant simulation tools for pedestrian thermal comfort, we present a benchmark case comparing simple microclimate models (SMM) and all-physics microclimate models (AMM). As SMM we consider SOLWEIG which does not use complex CFD solving of the flow around buildings. As AMM codes we consider urbanMicroclimateFoam, developed by the authors, PALM and ENVI-met, all resolving the flow around buildings based on different CFD turbulence models among other differences in their modeling approaches. The benchmark case is based on an idealized geometry of an isolated street canyon. Simulation conditions are harmonized as much as possible between the different tools. The comparison focuses on heatwave conditions.Based on the results and workflow, the authors make suggestions for using these tools in research and practice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".