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Record W2118428269 · doi:10.5897/jmer.9000033

Kinematics and kinetic analysis of the slider-crank mechanism in otto linear four cylinder Z24 engine

2011· article· en· W2118428269 on OpenAlexvenueno aff
Mohammad Ranjbarkohan, Mansour Rasekh, Abdol Hamid Hoseini, Kamran Kheiralipour, Mohammad Asadi

Bibliographic record

VenueMechanical Engineering Research · 2011
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsnot available
Fundersnot available
KeywordsCrankshaftCrankConnecting rodMechanism (biology)KinematicsAutomotive engineeringSliderCylinderPiston (optics)Four-stroke engineMechanical engineeringDiesel engineEngineeringPhysicsClassical mechanicsCombustion chamberChemistryCombustion

Abstract

fetched live from OpenAlex

Nissan Z24 is one of the numerous vehicles in Iran. MegaMotor's reports show high rate damaging in the crankshaft and connecting rod of this engine vehicle. It is necessary to carry out a complete research about slider-crank mechanism because of high expensive repairs and replacement of these parts and their reverse effects on the other parts such as cylinder block and piston. Results of initial researches show that an important reason of these parts’ damaging is using of downshifting in driving. In this research, we are concerned on the analysis of kinematics and kinetic of slider-crank mechanism of the engine in maximum power, maximum torque and downshifting situation. The influence of different parameters such as engine RPM and downshifting effects were investigated on crankshaft and connecting rod loads. Two methods for analyzing of slider-crank mechanism were used: solving Newton's law and MSC/Adams/Engine software.   Key words: Engine, slider-crank mechanism, kinematics and kinetic, downshifting, Newton's Law, engine rpm.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.092
GPT teacher head0.298
Teacher spread0.206 · 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 source (direct Gemma or distilled Codex), 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

Citations13
Published2011
Admission routes1
Has abstractyes

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