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Record W2801138230 · doi:10.3390/en11051100

Investigation of Airflow Patterns in a New Design of Wind Tower with a Wetted Surface

2018· article· en· W2801138230 on OpenAlexaff
M. Soltani, Alireza Dehghani-Sanij, Ahmad Sayadnia, Farshad Moradi Kashkooli, Kobra Gharali, SeyedBijan Mahbaz, Maurice B. Dusseault

Bibliographic record

VenueEnergies · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAirflowTowerEnvironmental scienceMarine engineeringMeteorologyAtmospheric sciencesEngineeringStructural engineeringGeologyMechanical engineeringGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.246

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.203
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations53
Published2018
Admission routes1
Has abstractyes

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