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Record W2245279581 · doi:10.5151/engpro-simea-pap60

CHALLENGES AND BEST PRACTICES DURING THE FINITE ELEMENT ANALYSIS FORMODAL INVESTIGATIONOF DRIVETRAIN COMPONENTS

2014· article· en· W2245279581 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBlucher Engineering Proceedings · 2014
Typearticle
Languageen
FieldEngineering
TopicMechanical Failure Analysis and Simulation
Canadian institutionsWeyerhauser (Canada)
Fundersnot available
KeywordsPowertrainAutomotive engineeringFinite element methodDrivetrainAutomotive industryEngineeringTurbochargerCamshaftVibrationTorqueNoise, vibration, and harshnessTurbopropEngine powerPower (physics)Mechanical engineeringStructural engineeringTurbine

Abstract

fetched live from OpenAlex

Engine downsizing is the use of a smaller engine in a vehicle that provides the power of a larger one. It is the result of car manufacturers attempting to provide more efficient vehicles by adding modern technologies, for instance, turbochargers, direct injection and variable camshaft. The smaller engine is also lighter and provides torque and power with similar performance to a much larger engine. However, the downsizing technique may lead to undesirable vibration effects on the driveline, such as structural damaging, vibration fatigue failure and extra noise. All these issues are related to natural frequencies investigation and they are often determined through the finite element method together with experimental tests during the product development phase. This work presents the finite element method limitation for natural frequencies determination of automotive components and a possible solution for this issue.

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.

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: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.643

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 head0.036
GPT teacher head0.237
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