CFD Analysis of the Effect on Buoyancy Due to Terrain Temperature Based from an Integrated DEM and Landsat Infrared Imagery
Bibliographic record
Abstract
This paper deals with the influence of concrete structures on atmospheric temperatureand the convection winds generated in the Aburra Valley in Medell´ın,Colombia. This area is characterised by low wind velocities with a high industrydensity. A digital elevation model was used from the Radar ShuttleTopography Mission and post-processed in order to obtain a valid volumetricCFD domain. The construction process includes hole-filling due to imperfectionsin the original radar data, decimation of the original cloud-of-points to reduce the excess of detail in regions with low curvature, and the introductionof a volume of air over the terrain surface (CFD domain). Landsat satellitedata was used to set the terrain temperatures for various material compositions.The converted infrared image was then registered into the CFD domainusing an interpolation technique. Navier–Stokes Equations were solved for buoyant, turbulent flow of compressiblefluids accounting for convection and heat transfer effects. Simulationincludes buoyancy and turbulence flow through the k–epsilon model using thehigh-performance computing facilities of Westgrid (Western Canada ResearchGrid). Preliminary results show wind distributions that compare to the oneobserved at low–altitude in the region. PACS: 47, 95.55.Rg, 07.57.Kp MSC: 76, 97M50
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".