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Record W2031685126 · doi:10.5539/mas.v3n6p45

The Effect of Radial Swirl Generator on Reducing Emissions from Bio-Fuel Burner System

2009· article· en· W2031685126 on OpenAlexvenueno aff
Mohamad Shaiful Ashrul Ishak, Mohammad Nazri Mohd Jaafar, Yehia A. Eldrainy

Bibliographic record

VenueModern Applied Science · 2009
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsnot available
FundersMinistério da Ciência, Tecnologia e InovaçãoUniversiti Malaysia PerlisKementerian Sains, Teknologi dan Inovasi
KeywordsCombustorNozzleMaterials scienceFuel injectionCombustionNOxGas burnerBoiler (water heating)InjectorMechanicsAirflowCombustion chamberLiquid fuelInletEnvironmental scienceAutomotive engineeringThermodynamicsMechanical engineeringPhysicsChemistry

Abstract

fetched live from OpenAlex

A liquid bio-fuel burner system with various radial air swirlers attached to combustion chamber of 280 mm inside diameter and 1000 mm length has been investigated. All tests were conducted using crude palm oil as fuel. A radial flow air swirler with curved blades having 50 mm outlet diameter was inserted at the inlet plane of the combustor to produce swirling flow. Fuel was injected at the back plate of the swirler outlet using central fuel injector with single fuel nozzle pointing axially outwards. The swirler vane angles and equivalence ratios were varied. Tests were carried out using four different air swirlers having 45°, 50°, 60°and 70° vane angles. NOx emissions reduction of about 12 percent was obtained at swirl number of 1.911 as compared to 0.780 at the same equivalence ratio of 0.83. In addition, emission of carbon monoxide decreased as the swirl number increased. The results shows that a proper design of air swirler has a great effect on mixing process and hence the combustion and emission.

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 score0.814
Threshold uncertainty score0.321

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 head0.200
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

Citations11
Published2009
Admission routes1
Has abstractyes

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