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Record W4200272279 · doi:10.1177/09544070211062278

Elastic breakdown via multi-core high frequency discharge for lean-burn ignition

2021· article· en· W4200272279 on OpenAlexaff
Xiaoye Han, Xiao Yu, Hua Zhu, Linyan Wang, Shui Yu, Meiping Wang, Ming Zheng

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicCombustion and Detonation Processes
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsIgnition systemMaterials scienceBreakdown voltageVoltageElectromagnetic coilHigh voltageSpark gapElectrodeCombustionNuclear engineeringMechanicsElectrical engineeringEngineeringChemistryPhysics

Abstract

fetched live from OpenAlex

An advanced ignition technique is developed to achieve multi-event breakdown and multi-site ignition using a single coil for ignition quality improvements. The igniter enables a unique elastic breakdown process embracing a series of high-frequency discharge events at the spark gap. The equivalent electric circuits and current/voltage equations are identified and verified for the first time to explain the working principle that governs such an elastic breakdown process. Benchmarking tests are first performed to compare the elastic breakdown ignition with the conventional and advanced multi-electrode ignition systems. The elastic breakdown and spark events are thereafter analyzed through current and voltage measurements and high-speed imaging techniques. Finally, ignition tests in combustion chambers are performed to examine the effects on the ignition process in comparison with conventional coil-based ignition systems. The experiments show that, the elastic breakdown ignition can distribute multiple high-frequency breakdown events at all electrode pairs of a multi-electrode sparkplug while using only one ignition coil, thereby offering significant cost saving advantage and packaging practicability.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.719
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.219
Teacher spread0.205 · 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 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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Mechanical Engineers Part D Journal of Automobile EngineeringSame topicCombustion and Detonation ProcessesFrench-language works237,207