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Natural Ventilation in Workshop with Different Horizontal Arrangement of Heat Source

2012· article· en· W2013097444 on OpenAlexaff
Yaxin Su, Xin Wan

Bibliographic record

VenueApplied Mechanics and Materials · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsLa Cité Collégiale
FundersFundamental Research Funds for the Central UniversitiesNatural Science Foundation of Shanghai
KeywordsNatural ventilationInletVentilation (architecture)TurbulenceComputational fluid dynamicsAirflowMechanicsEnvironmental scienceMeteorologyFlow (mathematics)Warm frontAir velocityFresh airEngineeringMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

The natural ventilation in a heating workshop with different horizontal arrangement of heat source was numerically simulated based on computational fluid dynamics (CFD) method. Realizable k- turbulent model was used to calculate the air flow and temperature distribution. Simulation results showed that the horizontal arrangement of the heat source in the workshop influenced heavily the air flow and temperature distribution. When the heat source was placed at the workshop centre, the heat distribution factor was minimal, the average air temperature at operation zone was lowest and the hot air exhausting velocity was highest, the air flow field and temperature distribution was reasonable for the natural ventilation. When the heat source was placed to be close to the air inlet opening, the fresh air would travel a short path and directly rise to exit and the fresh air did not reach to the right part of the workshop, leading to a possible accumulation of pollutant emission there. When the heat source was placed at the right side of the workshop, the benefit would be that the possible pollutant could be taken away by the air flow, however, the ventilation rate decreased.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.242

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 head0.007
GPT teacher head0.193
Teacher spread0.186 · 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 designBench or experimental
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

Citations1
Published2012
Admission routes1
Has abstractyes

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