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Record W2067133351 · doi:10.1115/detc2009-87131

Electromagnetic Valve Actuation System With Two Configurations

2009· article· en· W2067133351 on OpenAlexafffund
Rudolf Seethaler, Konrad Duerr

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsValve actuatorElectrohydraulic servo valveAutomotive industryMechanism (biology)Automotive engineeringComputer scienceActuatorControl systemCamshaftEngineeringValve timingControl engineeringInternal combustion engineControl theory (sociology)Mechanical engineeringControl (management)Electrical engineering

Abstract

fetched live from OpenAlex

Electromagnetic valve actuation systems for automotive combustion engines must provide extremely fast valve motion when the engine speed is high, but they also need to ensure low valve seating velocities during engine idle. These two constraints are difficult to combine in conventional spring assisted electromagnetic valve actuation devices that operate at a fixed resonance frequency. This paper focuses on a mechanism with two distinct configurations for low and high speed engine operations respectively. The mechanism is based on two pivoting cams. The synthesis of the cam profile ultimately determines the performance of the actuation system. An algorithm is presented that provides a time optimum cam profile for the high speed cam. The low speed cam is designed to allow for servo control of the valve system. A control scheme that aims to minimize electric losses in the drive system is also introduced. Both the cam synthesis algorithms and the control algorithm are applied to a typical automotive valve train and a digital simulation is used to validate the effectiveness of the mechanical cam design and control scheme.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.897
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.004
GPT teacher head0.195
Teacher spread0.191 · 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

Citations5
Published2009
Admission routes2
Has abstractyes

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