A Novel Low-Energy Ventilative Cooling System for Sustainable Built Environment
Bibliographic record
Abstract
A numerical investigation into determining the thermal and ventilation capability of wind towers integrated with the heat pipe technology was carried out in this work. The water-charged copper heat pipes were systematically arranged in a horizontal orientation and integrated inside a modern roof-mounted wind tower. Water was used as the working fluid instead of synthetic refrigerants in order to make the system carbon-neutral alongside maintaining the indoor air quality of the built environment. The three-dimensional Reynolds-Averaged Navier-Stokes (RANS) equations along with the momentum, continuity and energy equations were solved using the commercial Computational Fluid Dynamics (CFD) ANSYS code for velocity and pressure field simulations. Using the inlet wind speeds ranging from 1m/s to 5m/s, the results of the study showed that the proposed cooling system was capable of meeting the regulatory fresh air intake requirements per occupant of 10L/s. In addition, the findings determined that a passive cooling capacity of up to 11K was achievable when the system was subjected to inlet temperatures of 310K or 37°C. The work characterised the sustainable operation of wind tower in delivering energy-free ventilative cooling in regions encompassing hot and dry climatic conditions. The technology presented in this work is currently under an Intellectual Property (IP) protection (GB1321709.6).
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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".