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Record W2965781367 · doi:10.11159/htff19.199

Effectiveness Of Air Curtains As Thermal And Smoke Barrier Against High Gradients Of Flow Parameters

2019· article· en· W2965781367 on OpenAlexvenueno aff
MD ARIF MAHMUD SHUKLO SHOSHE, Md. Ashiqur Rahman

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSmokeThermalEnvironmental scienceFlow (mathematics)Materials sciencePetroleum engineeringMechanicsComputer scienceMeteorologyEngineeringPhysics

Abstract

fetched live from OpenAlex

This study aims to investigate air curtains as an effective thermal and aerosol barrier against high gradient of flow parameters generated by a typical fire incident. The numerical investigations are conducted by solving Navier-Stokes equations in Fire Dynamics Simulator (FDS). A clothing store of 12 m 2 floor area with a fire of 1528 kW/m 2 heat release rate per unit area (HRRPUA), at the furthest corner from the door, is considered as the source of high thermal and pressure gradients inside the store. The only means of ventilation for the current study, is an air curtain of 0.91 m length placed at the door at 2.13 m height from the floor. The air curtain's injection velocity, injection angle, flow rate and cross-sectional area of the flow channel are systematically varied for optimum sealing performance. The change in temperature profile and aerosol particle concentration with and without air curtain, and air curtain effectiveness E, are measured. Result suggests, the injection velocity and injection angle are the key parameters in preventing the heat and mass transfer across air curtains. The twin jet design, a commercially available feature for air curtains, is found to be less effective up to pitch ratio of 4 compared to that of a single jet design for restraining heat and mass transfer.

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 score0.001
Threshold uncertainty score0.510

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 head0.004
GPT teacher head0.181
Teacher spread0.178 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Mechanical, Chemical, and Material EngineeringSame topicWind and Air Flow StudiesFrench-language works237,207