Comparative Wind Simulation Studies of Different Urban Patterns in Hot Arid Region: Touristic Resort at Qarun Lake, Fayoum Region, Egypt
Bibliographic record
Abstract
Nowadays, it is considered that urban areas consume the bulk of vital natural and energy resources that raise concerns of environmental deterioration on different scales. Now, worldwide energy assessments indicate that improving the energy efficiency and sustainability of buildings, and urban communities could free-up enormous amounts of current energy expenses. Those energy fluxes and change of airflow that resulted from urban morphology can lead to phenomenon such as the urban heat island and convective rainfall initiation.In this research it initiates – Urban Fluid Mechanics (UFM) by using Computational Fluid Dynamics (CFD) tools that delve into fundamental fluid flow problems of immediate utility for the development of resorts at Qarun lake in Egypt through a comparative study between different urban patterns through three main processes. Firstly, it starts by using of results from a mesoscale model to indicate the best place for building a wind turbine farm and to generate data for the microscale model of the urban canopy. Secondly, using qualitative modeling method for urban analysis that can evaluate the airflow conditions of the urban canopy in different seasons. Thirdly, making a comparative study between different urban patterns by changing a certain parameter. As a result from these three processes that qualitative simulation process will be of use for building and urban scale performance predictions as well as for the future simulation of pedestrian comfort. In this paper, it describes in detail one such approach, along with some sample results demonstrating the capabilities of this tool in Planning and urban design decisions, and raises questions as How Architects, Planners, Urban designers get advantages through using CFD method and its tools?, How can that method indicate the best places for renewable energy resources? and How can such ways of comparative studies can but a certain criteria for urban design in a certain zone?
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".