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Record W4412032420 · doi:10.11159/jffhmt.2025.026

Impactor-Assisted Atomization in Multi-Jet Twin-Fluid Injectors for FCC Risers

2025· article· en· W4412032420 on OpenAlexvenueno aff
Deepak Kumar, Abhijit Kushari, Hemant Mishra

Bibliographic record

VenueJournal of Fluid Flow Heat and Mass Transfer · 2025
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsInjectorJet (fluid)Materials scienceMechanicsEnvironmental scienceMechanical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

The Fluidized Bed Catalytic Cracking relies heavily on the atomization efficiency of the feed injection system, where vacuum gas oil is sprayed into the riser reactor for rapid vaporization and catalyst contact.This study experimentally investigates the spray behavior of a twin-fluid nozzle designed for FCC applications, focusing on the influence of air-to-liquid ratio (ALR) on droplet dynamics.Spatial measurements reveal that increasing ALR significantly reduces the section-averaged Sauter Mean Diameter (SMD) due to enhanced aerodynamic shear.The 3-hole injector consistently outperforms the 4-hole design, generating finer and more uniform droplets.Droplet size distributions exhibit lognormal behavior, with decreasing variance at higher ALRs, indicating improved spray uniformity.In contrast, axial velocity distributions deviate from normality, reflecting the influence of turbulent air-droplet interactions.The power-law correlation between SMD and ALR demonstrates that droplet breakup is primarily controlled by the air-to-liquid mass ratio and injector geometry.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.236
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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