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Record W4317923930 · doi:10.1299/jmsesdm.2022.10.c1-4

An optical investigation into the reactive fuel spray of high-pressure DME and Ethanol

2022· article· en· W4317923930 on OpenAlexaff
Simon Leblanc, Long Jin, Alex Bastable, Xiao Yu, Jimi Tjong, Ming Zheng

Bibliographic record

VenueThe Proceedings of the International symposium on diagnostics and modeling of combustion in internal combustion engines · 2022
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsDiesel fuelCombustionAutomotive engineeringIgnition systemHomogeneous charge compression ignitionThermal efficiencyVolatility (finance)Fuel injectionInternal combustion engineMaterials scienceLubricityCarbureted compression ignition model engineDiesel engineEnvironmental scienceCompression ratioDiesel cycleCombustion chamberChemistryEngineeringAerospace engineeringComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Reducing harmful emissions and increasing thermal efficiency in internal combustion engines have been the foremost goals of modern engine research. Compression ignition (CI) engines offer advantages in terms of high-efficiency operation as the overall in-cylinder mixture is lean. High-pressure direct injection fueling systems, commonly employed in CI engines, allow for the precise control of the combustion through injection timing and heat release modulation with same-cycle multi-pulse injection. Diesel fuel has dominated the liquid energy sources for CI engines owing to its high reactivity and lubricity properties. The highly heterogeneous mixture criterion and the dependence on auto-ignition of CI engines allow for a wide range of fuels to be applicable for engine operation. Notably, ethers possess chemical properties similar to diesel such as high reactivity, albeit with improved volatility characteristics.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.012
GPT teacher head0.241
Teacher spread0.229 · 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
Published2022
Admission routes1
Has abstractyes

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