Investigation of Airflow Patterns in a New Design of Wind Tower with a Wetted Surface
Bibliographic record
Abstract
Passive cooling systems, such as wind towers, can help to reduce energy consumption in buildings and at the same time reduce greenhouse gas (GHG) emissions. Wind towers can naturally ventilate buildings and also can create enhanced thermal comfort for occupants during the warm months. This study proposes a modern wind tower design with a moistened pad. The new design includes a fixed column, a rotating and movable head, an air opening with a screen, and two windows at the end of the column. The wind tower can be installed on roof-tops to take advantage of ambient airflow. The wind tower’s head can be controlled manually or automatically to capture optimum wind velocity based on desired thermal condition. To maximize its performance, a small pump was considered to circulate and spray water on an evaporative cooling pad. A computational fluid dynamics (CFD) simulation of airflow around and inside the proposed wind tower is conducted to analyze the ventilation performance of this new design of wind tower. Thereby, the velocity, total pressure, and pressure coefficient distributions around and within the wind tower for different wind velocities are examined. The simulation results illustrate that the new wind tower design with a moistened pad can be a reasonable solution to improve naturally the thermal comfort of buildings in hot and dry climates.
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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) | 0.001 | 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".