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Record W1980057809 · doi:10.1115/ices2012-81173

A Robust Variable Valve Actuation System With Energy Recovery Mechanism

2012· article· en· W1980057809 on OpenAlexaff
Mohammad Pournazeri, Amir Khajepour

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRobustness (evolution)Ball screwAutomotive engineeringHydraulic machineryEnergy consumptionControl theory (sociology)ActuatorEnergy recoveryCrankshaftComputer scienceValve actuatorEngineeringEnergy (signal processing)Mechanical engineering

Abstract

fetched live from OpenAlex

In this paper, a new hydraulic variable valve actuation system is proposed. Using this system, the engine valve opening and closing timings and lift are flexibly controlled with two rotary spool valves actuated by the engine crankshaft. High degree of flexibility with less control complexity and high repeatability are the advantages of this system over other camless valvetrains; however, in this system, there is a trade-off between its robustness and power consumption. A numerical model of the system is developed to study the system functionality at different operating conditions. To validate the developed model, the simulation results for a random operating condition are compared with those from the experiments. A sensitivity analysis is done to study the effects of variations in different design parameters on system robustness and power consumption. The results prove that increasing engine valve return-spring stiffness and actuator piston area will reduce the mechanism sensitivity to engine cycle-to-cycle variations; however, this results in poor energy efficiency. Therefore, a neat energy recovery strategy is developed to recuperate a portion of the energy used to compress the engine valve return-spring during valve opening interval. The results show that more than 90% of the extra energy wasted for the sake of system robustness could be regenerated through the proposed energy recovery system.

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 score0.989
Threshold uncertainty score0.369

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 head0.013
GPT teacher head0.162
Teacher spread0.149 · 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
Published2012
Admission routes1
Has abstractyes

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