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Record W1986632396 · doi:10.1115/detc2011-47386

Property Analysis of an Electro-Mechanical Regenerative Damper Concept

2011· article· en· W1986632396 on OpenAlexaff
Changmiao Yu, Weihua Wang, Qingnian Wang, Subhash Rakheja

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsDamperDamping torqueClutchInertiaArmature (electrical engineering)Control theory (sociology)Moment of inertiaVibrationMechanicsStructural engineeringEngineeringComputer sciencePhysicsMechanical engineeringClassical mechanicsAcousticsVoltageMagnetElectrical engineeringInduction motor

Abstract

fetched live from OpenAlex

This study proposes a concept of an electro-mechanical regenerative damper, composed of a gear mechanism and overrunning clutches to achieve uni-directional angular motion of the armature, and asymmetric damping in compression and rebound. An analytical model of the regenerative damper is formulated to derive its force-velocity properties, which revealed predominant inertia damping effect in addition to the electro-magnetic damping. The formulations further revealed that the damping properties of proposed concept could be easily varied through variation in both the electrical circuit and mechanical design parameters. The model is analyzed under harmonic excitations to determine its damping properties and the influences of various design parameters. The results suggest that the total force developed comprises two major components attributed to the generator and the effective inertia. The generator force resembles that of a bilinear asymmetric damper, while the variations in the inertia force exhibit a pattern similar to a ‘high-low’ damper. The proposed concept could thus yield ‘high-low’ damping variations in an entirely passive manner, while offering energy harvesting potential.

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 categoriesInsufficient payload (model declined to judge)
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 score0.642
Threshold uncertainty score1.000

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)0.0010.000

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 head0.027
GPT teacher head0.219
Teacher spread0.192 · 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.

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

Citations0
Published2011
Admission routes1
Has abstractyes

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