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Record W2901098847 · doi:10.1002/cjce.23343

CFD simulation of flashing jet applied to area classification

2018· article· en· W2901098847 on OpenAlexvenueno aff
Talles Caio Linhares de Oliveira, Antônio Tavernard Pereira Neto, José Jaílson Nicácio Alves

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicCombustion and Detonation Processes
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsFlammable liquidFlashingComputational fluid dynamicsHazardous wasteJet (fluid)FlammabilityVolume (thermodynamics)Work (physics)Environmental scienceSuperheatingMechanicsNuclear engineeringPetroleum engineeringMaterials scienceWaste managementEngineeringMechanical engineeringThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Abstract Industrially flammable gases are stored and transported in the form of pressurized liquid. The occurrence of leaks from pressurized flammable liquids in vessels or pipes may cause a two‐phase release, containing a mixture of liquid droplets and vapour of flammable substances. This phenomenon, called flashing, occurs when a superheated liquid comes into a lower pressure environment. The study and understanding of this type of release are of fundamental importance for the hazardous area classification that is addressed by the IEC 60079‐10‐1 standard, which recommends the use of computational fluid dynamics (CFD) for the calculation of a hazardous area. This work uses CFD to predict the hazardous area extent and volume that resulted from the complex two‐phase release, which had not been addressed previously in other literature. The influence of wind and release conditions on the area classification is analyzed. The CFD results agree with the available experimental data. A new parameter as a function of the volume and extent of the jet is proposed for hazardous area classification.

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 score0.154
Threshold uncertainty score0.251

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 head0.018
GPT teacher head0.214
Teacher spread0.195 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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