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Record W2697490839 · doi:10.3390/en10070845

Performance Evaluation of a Modular Design of Wind Tower with Wetted Surfaces

2017· article· en· W2697490839 on OpenAlexaff
Sajad M.R. Khani, Mehdi N. Bahadori, Alireza Dehghani-Sanij, Ahmad Nourbakhsh

Bibliographic record

VenueEnergies · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsMemorial University of NewfoundlandConcordia University
Fundersnot available
KeywordsTowerRelative humidityWind speedNatural ventilationAirflowEnvironmental scienceModular designCooling towerMeteorologyWind directionMarine engineeringApparent temperatureVentilation (architecture)Atmospheric sciencesEngineeringStructural engineeringAerospace engineeringComputer scienceMechanical engineeringGeologyWater coolingGeography

Abstract

fetched live from OpenAlex

Wind towers or wind catchers, as passive cooling systems, can provide natural ventilation in buildings located in hot, arid regions. These natural cooling systems can provide thermal comfort for the building inhabitants throughout the warm months. In this paper, a modular design of a wind tower is introduced. The design, called a modular wind tower with wetted surfaces, was investigated experimentally and analytically. To determine the performance of the wind tower, air temperature, relative humidity (RH) and air velocity were measured at different points. Measurements were carried out when the wind speed was zero. The experimental results were compared with the analytical ones. The results illustrated that the modular wind tower can decrease the air temperature significantly and increase the relative humidity of airflow into the building. The average differences for air temperature and air relative humidity between ambient air and air exiting from the wind tower were approximately 10 °C and 40%, respectively. The main advantage of the proposed wind tower is that it is a modular design that can reduce the cost of wind tower construction.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.797
Threshold uncertainty score0.205

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 head0.029
GPT teacher head0.242
Teacher spread0.213 · 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

Citations21
Published2017
Admission routes1
Has abstractyes

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