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Record W2334239147 · doi:10.7492/ijaec.2012.010

Performance Evaluation and Energy Saving Potential of Windcatcher Natural Ventilation Systems in China

2012· article· en· W2334239147 on OpenAlexvenueno aff
Zhe Ji, Yuehong Su, Naghman Khan

Bibliographic record

VenueInternational Journal of Architecture Engineering and Construction · 2012
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsNatural (archaeology)ChinaNatural ventilationEnergy (signal processing)Ventilation (architecture)Natural gasEnvironmental scienceComputer scienceEngineeringHistoryWaste managementMathematicsMechanical engineeringStatistics

Abstract

fetched live from OpenAlex

Windcatcher as an architectural element is a passive cooling design to improve indoor thermal comfort without energy consumption. The performance and energy saving potential of using a windcatcher system in China are evaluated through EnergyPlus simulation of an oce building installed with commercial windcatchers. The simulation results for the climate condition in Beijing indicate that the peak indoor tem- perature can be reduced by more than 2 - C with the help of windcatchers, for approximately 50% of occupied hours it can meet the basic ventilation requirement and 28% of occupied hours can reach the purge demand. Moreover, 17% of cooling load can be reduced. The performance of windcatchers can be largely enhanced if they are used along with top-hung windows to create cross ventilation. Further investigations are carried out for various climate regions to evaluate the feasibility of windcatcher applications in China. It is found that according to the accumulative hours of meeting ventilation requirement, Harbin is the most suitable city for windcatcher applications and then Shanghai and Kunming. But, Kunming appears the best city in terms of percentage saving in cooling load, followed by Harbin and Urumqi. However, from the economic perspective, Beijing looks the most cost eective

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score0.264

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 head0.002
GPT teacher head0.181
Teacher spread0.179 · 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 designSimulation or modeling
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

Citations24
Published2012
Admission routes1
Has abstractyes

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