MétaCan
Menu
Back to cohort
Record W2990698625 · doi:10.1115/detc2019-97581

Self-Starting Cogging-Torque-Assisted Motor Drive for Use in a Heavy-Duty Diesel Engine

2019· article· en· W2990698625 on OpenAlexaff
Devin K. Reinholz, Bradley A. Reinholz, Rudolf Seethaler

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsTurbochargerAutomotive engineeringCamshaftTorqueRotor (electric)DamperDiesel engineEngineeringElectromagnetic coilCommon railComputer scienceControl theory (sociology)Mechanical engineeringControl engineeringElectrical engineeringGas compressor

Abstract

fetched live from OpenAlex

Abstract This paper describes the design process and simulation results of a variable valve actuation system modeled after the cogging-torque-assisted motor drive (CTAMD) found in literature. Unlike the CTAMD, the new design is capable of handling large exhaust pressures. Furthermore, the new design incorporates damper windings to improve upon the CTAMD by enabling self-starting. The new variable valve actuator is designed for the 6-cylinder Westport 15L diesel engine. The simulated results suggest that the new variation of the CTAMD can be effectively applied to a turbocharged heavy-duty diesel engine. Simulation results show that the designed motor is capable of self-starting, and infinitely variable lifts. The damper windings are shown to be more beneficial at enabling self-starting and infinitely variable lifts than a spring, since they do not impose additional energy requirements during a valve transition. Furthermore, the new design is also shown to be capable of actuating exhaust valves in the presence of cylinder pressures up to 16 bar with similar efficiency to that of a standard camshaft valve train.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.721
Threshold uncertainty score0.739

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 head0.015
GPT teacher head0.216
Teacher spread0.201 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same topicHydraulic and Pneumatic SystemsFrench-language works237,207