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Record W1966945516 · doi:10.1115/detc2010-28798

A New Variable Stiffness Spring Using a Prestressed Mechanism

2010· article· en· W1966945516 on OpenAlexaff
Mojtaba Azadi, Saeed Behzadipour, Simon D. Guest

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsStiffnessStaticsVibration isolationSpring (device)Structural engineeringControl theory (sociology)VibrationKinematicsVariable (mathematics)InertiaMechanism (biology)Vibration controlDamperWork (physics)InfinitesimalEngineeringComputer scienceMathematicsPhysicsMathematical analysisClassical mechanicsMechanical engineeringControl (management)Acoustics

Abstract

fetched live from OpenAlex

A novel design of a semi-active variable stiffness element is proposed, with possible applications in vibration isolation. Semi-active vibration isolators usually use variable dampers. However, it is known from the fundamental vibration theory that a variable spring can be far more effective in shifting the frequencies of the system and providing isolation. Geometry change is a common technique for building variable springs, but has disadvantages due to the complexity of the required mechanism, and slow response due to the inertia of moving parts. In the variable spring introduced here (VS), the stiffness is changed by force control in the links which corresponds to infinitesimal movements of the links, and does not need a change of geometry to provide a change of stiffness. This facilitates a fast response. The proposed VS is a simple prestressed cable mechanism with an infinitesimal mechanism. Theoretically the level of the prestress in the cables can be used to control the stiffness from zero to a maximum value that is only limited by the strength of the links. In this work, the statics, kinematics and stability of the VS are studied, the stiffness is formulated, and possible configurations of the VS are found.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.940
Threshold uncertainty score0.999

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)0.0020.000

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 head0.009
GPT teacher head0.198
Teacher spread0.190 · 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 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

Citations2
Published2010
Admission routes1
Has abstractyes

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