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Record W2994634918 · doi:10.1109/tmech.2019.2959523

Cylindrical Cam Electromagnetic Vibration Damper Utilizing Negative Shunt Resistance

2019· article· en· W2994634918 on OpenAlexaff
Seyed Hossein Kamali, Mohammad Hossein Miri, Mehrdad Moallem, Siamak Arzanpour

Bibliographic record

VenueIEEE/ASME Transactions on Mechatronics · 2019
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsDamperActuatorMechanism (biology)VibrationRectificationResistive touchscreenShock absorberVibration controlControl theory (sociology)EngineeringStructural engineeringElectrical engineeringMechanical engineeringAcousticsVoltageComputer sciencePhysics

Abstract

fetched live from OpenAlex

Two common problems in utilizing electromagnetic dampers are addressed in this article. The first problem is the maximum available damping that can be created in an electromagnetic actuator using a positive resistive load. Maximum damping is achieved when the motor terminals are placed in a short circuit configuration, but it might not be enough in certain applications. As a solution, a method to produce negative resistance using power electronics and control techniques is presented in this article. The negative resistance cancels part of the resistance of the electromagnetic machine, which leads to producing larger electrical current in the actuator coils. As a result, higher damping levels are generated. The other contribution of this article is devising an effective motion rectification mechanism for converting the translational motion into rotary motion that drives an electric machine. To this end, a cylindrical cam mechanism is proposed which can provide longer strokes while keeping the geometry and size of the damper similar to the axial dampers used in automotive and bicycle dampers. The mechanism shows longer values for the ratio of stroke to maximum length compared to other designs reported in the literature.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.873
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 integrity0.0000.001
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 head0.007
GPT teacher head0.202
Teacher spread0.195 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

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