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Record W2761429865 · doi:10.1002/cjce.23047

Interaction between a hollow‐cone spray and the co‐axial swirling stratified flow in a novel spray pyrolysis furnace

2017· article· en· W2761429865 on OpenAlexvenueno aff
Wei Du, Ze Sun, Guimin Lu, Jianguo Yu

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsnot available
Fundersnot available
KeywordsSpray characteristicsNozzleMaterials scienceMechanicsVolumetric flow rateSpray nozzleFlow (mathematics)InletMass flow rateResidence time distributionResidence time (fluid dynamics)Ligand cone angleMass flowMechanical engineeringComposite materialConical surfaceEngineeringPhysicsGeotechnical engineering

Abstract

fetched live from OpenAlex

Pressure‐swirl nozzles producing hollow‐cone sprays are widely used in many engineering applications, but the induced high‐speed stream along the centreline of the spray cone was ignored in designing spray chambers. The high‐speed stream influences the flow field especially in minimized spray chambers, but it is difficult to characterize experimentally. Hence, computational fluid dynamics was applied to investigate the multi‐phase flow interactions inside a novel spray pyrolysis furnace for liquid ore exploitation. The mass flow rate distribution of the co‐axial swirling stratified flow inside the furnace, the gas‐solid separation efficiency, particle size distribution, and residence time were investigated under different positions and directions of the hollow‐cone spray. The results proved that different spray operations would cause variation in the air mass flow rate distribution and gas‐solid separation efficiency, which should be paid attention to in the design of process intensification. The distribution of particles sized from 30 to 40 μm were most sensitive under different flow rates. The recommended operating conditions of the spray pyrolysis process were confirmed as the inlet velocity of 5 m/s and downward spray from the top of the furnace chamber.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.463
Threshold uncertainty score0.519

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.230
Teacher spread0.214 · 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

Citations10
Published2017
Admission routes1
Has abstractyes

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