MétaCan
Menu
Back to cohort
Record W2973916889 · doi:10.1115/1.4044889

On the Liftoff of Diffusion Flame: An Experimental and Analytical Study

2019· article· en· W2973916889 on OpenAlexaff
Mohsen Akbarzadeh, Madjid Birouk

Bibliographic record

VenueJournal of Energy Resources Technology · 2019
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsNozzleDiffusion flameJet (fluid)DiffusionMechanicsAirflowMaterials scienceChemistryTurbulencePremixed flameComposite materialCombustionCombustorThermodynamicsMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract The effect of lip thickness of fuel nozzle on the liftoff of an attached diffusion methane flame with and without a coflow was experimentally and analytically investigated. Three fuel nozzles made of a long pipe with the same internal diameter but different nozzle lip thickness were tested. The co-airflow was also varied to assess its impact on the liftoff of jet diffusion flame. The results showed that the effect of fuel nozzle lip thickness on the liftoff of a free jet flame (i.e., no coflow) is marginal. However, the presence of a weak to moderate coflow (0.05 m/s < Uco < 0.3 m/s) reduced the liftoff velocity of an attached flame. The reduction rate of the liftoff velocity with co-airflow was found to be more significant for the fuel nozzle with the smaller lip thickness. At a relatively higher coflow (Uco > ∼0.3 m/s), a more pronounced drop in the liftoff velocity with coflow was observed. Flow field characteristics obtained using PIV measurements showed that the coflow stream experienced a transition to the turbulent regime for Uco > ∼0.3 m/s range which is believed to cause the inception of the flame liftoff to occur. A model for predicting the liftoff velocity of an attached flame in the presence of a coflow was developed based on the stoichiometric mixture velocity equation. This model showed good agreement with the present experimental data. However, although it can predict successfully the observed trends in the literature, further experiments are required to generalize it.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.743
Threshold uncertainty score0.231

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 head0.005
GPT teacher head0.213
Teacher spread0.208 · 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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Energy Resources TechnologySame topicCombustion and flame dynamicsFrench-language works237,207