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Record W1988434999 · doi:10.1177/1077546306068059

Frequency Response and Jump Avoidance in a Nonlinear Passive Engine Mount

2006· article· en· W1988434999 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

VenueJournal of Vibration and Control · 2006
Typearticle
Languageen
FieldEngineering
TopicVehicle Noise and Vibration Control
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsControl theory (sociology)Nonlinear systemJumpIsolatorMountFrequency responsePerturbation (astronomy)Parametric statisticsVibration isolationStiffnessDisplacement (psychology)VibrationAccelerationEngineeringMathematicsPhysicsStructural engineeringComputer scienceAcousticsClassical mechanics

Abstract

fetched live from OpenAlex

This paper explores a model for a nonlinear one-degree of freedom passive vibration isolator system, known as a smart engine mount. Nonlinearities are employed to analyze and possibly improve the behavior of the optimal linear mount. Nonlinear damping and stiffness rates of the isolator have interacting effects on the dynamic behavior of the mount. The frequency response of the system is obtained using the averaging perturbation method, and a parametric analysis shows that the effect of nonlinear stiffness rate on frequency response is opposite to that of the nonlinear damping rate. Stability of the steady state periodic response has also been analyzed. Jump avoidance criteria are introduced, and the conditions for jump avoidance are studied. Closed form solutions for the absolute acceleration and relative displacement frequency responses are derived, since they are essential to use of the RMS optimization method.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.929
Threshold uncertainty score0.330

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 head0.002
GPT teacher head0.179
Teacher spread0.177 · 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