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Record W4293702515 · doi:10.4271/2022-01-1049

Experimental and Simulation Analysis of Spray and Combustion Characteristics in a Swirl-Chamber Diesel Engine

2022· article· en· W4293702515 on OpenAlexaff
Zenta Sudo, Yuma Hozen, Beini Zhou, Jin Kusaka, Kenya Ajiro, Tomohiro Koga

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2022
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsM.A. Turbo Engine (Canada)
Fundersnot available
KeywordsCombustion chamberSpray characteristicsDiesel engineCombustionAutomotive engineeringDiesel fuelMaterials scienceHomogeneous charge compression ignitionMechanicsEnvironmental scienceNuclear engineeringMechanical engineeringSpray nozzleEngineeringPhysicsChemistryNozzle

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">A swirl-chamber diesel engine has an indirect injection system in which fuel is injected into a pre-chamber called the swirl-chamber that is separated from the main chamber. Indirect fuel injection systems can be directly mechanically controlled by the camshaft, which is cheaper than electronic control. For these reasons, they are used in diverse industrial applications and automobiles. However, optimization of the swirl-chamber shape and performance tests have been mainly experimental, and there has been insufficient verification of the accuracy of simulations. Thus, we have attempted to verify simulations using a rapid compression and expansion machine that can reproduce the combustion in one engine cycle, with a chamber like a swirl chamber in the cylinder head to visualize the behavior of evaporative sprays and the combustion process. In this study, the authors focused on the wall impingement of the fuel spray and took photos of its liquid phase and ignition. Furthermore, the authors constructed simulation models using commercial 3D-CFD software based on the experimental results and established an analytical method. As a result, flow, spray, and ignition behavior in the swirl chamber were clarified through visualization experiments. In addition, numerical simulation models were constructed to reproduce the in-cylinder pressure and rate of heat release during combustion obtained in experiments using a rapid compression and expansion machine.</div></div>

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.993
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.011
GPT teacher head0.258
Teacher spread0.247 · 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.

Study designObservational
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
Published2022
Admission routes1
Has abstractyes

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