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Record W4293766830 · doi:10.1051/e3sconf/202235602010

Reduced-scale experimental and numerical investigation on the energy and smoke control performance of natural ventilation systems in a high-rise atrium

2022· article· en· W4293766830 on OpenAlexaff
Haohan Sha, Xin Zhang, Xiguan Liang, Dahai Qi

Bibliographic record

VenueE3S Web of Conferences · 2022
Typearticle
Languageen
FieldEngineering
TopicFire dynamics and safety research
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsSmokeNatural ventilationEnvironmental scienceSlabAirflowVentilation (architecture)Marine engineeringAutomotive engineeringEngineeringStructural engineeringMechanical engineeringWaste management

Abstract

fetched live from OpenAlex

Natural ventilation (NV) is an effective energy-saving strategy to remove the excessive heat in high-rise atria. The traditional NV system in high-rise atria has inlet openings at the bottom and outlet openings at the top. However, this traditional system may bring fire safety concerns due to the rapid spread of smoke during an atrium fire. To remove the fire safety concern, a new NV system was proposed in this study. This new system applies a segmentation slab to divide the high-rise atrium into upper and lower parts, which can limit the smoke movement. A ventilation shaft is installed to maintain the NV rate and extract smoke. To investigate the energy and smoke control performance of the new and traditional NV systems, a 1:20 small-scale experimental model and CFD numerical model were built. The results indicate that the new NV system with the shaft and segmentation can remove more heat than the traditional NV system. Furthermore, the new NV system can simplify the mechanical smoke exhaust system and improve the smoke control performance, e.g., requires a lower volumetric flow rate and maintains a thinner smoke layer.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.841
Threshold uncertainty score0.175

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 head0.013
GPT teacher head0.216
Teacher spread0.204 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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