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Record W4409641301 · doi:10.1109/tps.2025.3558478

Plasma Resistance Control for Ignition Energy Improvements Under High-Speed Flow Conditions

2025· article· en· W4409641301 on OpenAlexaff
Xiao Yu, Linyan Wang, Ming Zheng

Bibliographic record

VenueIEEE Transactions on Plasma Science · 2025
Typearticle
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsIgnition systemPlasmaFlow control (data)Energy (signal processing)MechanicsFlow (mathematics)Materials scienceNuclear engineeringComputer sciencePhysicsThermodynamicsEngineering

Abstract

fetched live from OpenAlex

Modern spark ignition (SI) systems tend to face a fuel-lean mixture of elevated density and intensified flow for improving engine efficiency and exhaust emission. A more effective ignition source, e.g., a properly modulated current profile of on-demand elevated energy is preferred to assist the flame kernel formation process and developments. The strong air motion blows the plasma channel away from the spark gap, causing the plasma channel to stretch, which leads to restrike events when stretched excessively. In this work, the impact of spark plasma stretching on the discharge processes has been investigated under various flow velocities and background densities. It is observed that the cross-flow may raise the deposition efficiency of discharge energy between the spark electrodes via plasma stretching, but the prolonged stretching may challenge the plasma stability. The mechanisms of restrike and blow-off events are investigated corresponding to plasma resistance and discharge voltage. Furthermore, a boosted current strategy is applied to study the effectiveness of discharge current modulation (50 mA–3 A) on the plasma resistance control under the flow conditions. The study comprehensively investigates the impacts of discharge current, flow velocity, and background pressure on plasma stretching and energy release efficacy.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.888
Threshold uncertainty score0.824

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.012
GPT teacher head0.271
Teacher spread0.259 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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