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Record W2885134491 · doi:10.3390/en11081967

Dry Fuel Jet Half-Angle Measurements and Correlation for an Entrained Flow Gasifier

2018· article· en· W2885134491 on OpenAlexafffund
Francis Kus, Robin W. Hughes, Arturo Macchi, Poupak Mehrani, Marc Duchesne

Bibliographic record

VenueEnergies · 2018
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsNatural Resources CanadaUniversity of Ottawa
FundersGovernment of Canada
KeywordsJet (fluid)MechanicsComputational fluid dynamicsWood gas generatorRange (aeronautics)Materials scienceFlow (mathematics)Jet fuelEnvironmental scienceThermodynamicsChemistryPhysicsWaste managementEngineeringComposite materialCoal

Abstract

fetched live from OpenAlex

Reduced order models (ROMs) are increasingly applied to entrained flow gasification development due to reduced computational requirements relative to computational fluid dynamics (CFD) models. However, they require greater a posteriori knowledge of the reactor physics. A significant parameter influencing ROM outputs is the jet half-angle of the solid fuel and oxidant mixture in the gasifier. Thus, it is important to understand the geometry of the jet in the gasifier, and how it is dependent on operating parameters, such as solid and carrier gas flow rates. In this work, an existing model for jet half-angles, which considers the ratio of surrounding gas density to jet core density, is extended to a dry solids jet with impinging gas. The model is fitted to experimental jet half-angles. The jet half-angle of a non-reactive flow was measured using laser-sheet imaging for solid fluxes in the range of 460–880 kg/m2·s and carrier gas fluxes in the range of 43–90 kg/m2·s at the transport line outlet. Jet half-angles ranged from 5.6° to 11.3°, increasing with lower solid/gas loading ratios. CFD simulations of two reactive conditions, with solid and gas fluxes similar to experiments, were used to test the applicability of the proposed jet half-angle model.

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 score0.251
Threshold uncertainty score0.323

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 head0.026
GPT teacher head0.233
Teacher spread0.207 · 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

Citations1
Published2018
Admission routes2
Has abstractyes

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