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A Comparative Study of Leakage Characteristics between an Under Floor Air Distribution System and an Over Head Air Distribution System

2015· article· en· W2261076170 on OpenAlexaff
Rupesh Iyengar, Chandra Sekhar, Arash Soleimani Karimabad, Fariborz Haghighat, Kenny Q. Zhu

Bibliographic record

VenueInternational Journal of Ventilation · 2015
Typearticle
Languageen
FieldEngineering
TopicFire dynamics and safety research
Canadian institutionsConcordia University
Fundersnot available
KeywordsLeakage (economics)Head (geology)Distribution (mathematics)Environmental scienceEngineeringAutomotive engineeringGeologyMathematicsGeomorphology

Abstract

fetched live from OpenAlex

This research aims at quantifying the leakage occurring in Under Floor Air Distribution (UFAD) systems and Over Head Air Distribution (OHAD) systems. The study also classifies the leakage occurring in the different systems into Category 1 leakage and Category 2 leakage. The study was performed between a plenum supply UFAD system (having a maximum flow rate of 1272 m3/h), a ducted supply UFAD system (having a maximum flow rate of 286 m3/h) and an OHAD system (having a maximum flow rate of 2004 m3/h). Results show that in plenum supply UFAD systems, 5.9% and 14.8% of the volume of air supplied by the Air Handling Unit (AHU) is attributed to leakage when the AHU is running at 50% and 100% fan speeds respectively. In ducted supply UFAD systems however, the leakage drops to 2.1% and 5.6% respectively at 50% and 100% AHU fan speeds. A ducted supply UFAD system in comparison to a plenum supply UFAD system lowers leakage by 62.2% when the AHU is operating at 100% fan speed and by 64.4% when the AHU is operating at 50% fan speed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.043
GPT teacher head0.339
Teacher spread0.296 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2015
Admission routes1
Has abstractyes

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