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Record W2036263823 · doi:10.4271/2015-01-0934

Fuel Spray Tip Penetration Model for Double Injection Strategy

2015· article· en· W2036263823 on OpenAlexaff
Pascal Tétrault, E. Plamondon, Matthieu Breuze, Camille Hespel, Christine Mounaïm–Rousselle, Patrice Seers

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2015
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsPenetration (warfare)Materials scienceAutomotive engineeringMechanicsEnvironmental scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">In this paper, a semi-empiric fuel spray tip penetration model is proposed. It is applied to single and double injection strategies taking into account the early and far field penetration. The model is based on the momentum flux as initially proposed by [<span class="xref">1</span>] for single injection but it is derived from mean mass flow rate herein. Fuel spray interaction with entrainment air is taken into account for the second injection. The proposed model is calibrated and validated using data from 9 experiments conducted with an indirect piezoelectric diesel injector under various injection strategies. The experiments included 1) injection rate measurements using the Bosch method to determine mean mass flow rate during injector opening as well as obtaining injection duration which are both entry parameters to the model; 2) Fuel spray tip penetrations were measured in a pressure vessel using high speed photography for single and double injection strategies. The experimental results were used to calibrate the model's constants and to verify the model's ability to predict spray behavior. Finally, the experimental results for some fuel injection strategies have shown that there is an interaction between the first and second spray and the paper shortly discusses these cases.</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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.787
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.037
GPT teacher head0.280
Teacher spread0.243 · 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 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

Citations8
Published2015
Admission routes1
Has abstractyes

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