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Record W3209070900 · doi:10.23977/jeeem.2021.040102

Design of Multi-Dimensional Damping Platform Based on MR Damper

2021· article· en· W3209070900 on OpenAlexvenueno aff
Weijie Zhang, Cheng Qian, Peiyuan Sun, Yucheng Ji

Bibliographic record

VenueJournal of Electrotechnology Electrical Engineering and Management · 2021
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsnot available
Fundersnot available
KeywordsMagnetorheological fluidDamperVibrationReduction (mathematics)EngineeringDamping torqueVibration isolationStructural engineeringVibration controlControl theory (sociology)Computer scienceAcousticsPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

Due to the multi-dimensional damping platform obtained by traditional vibration isolation technology can't meet the requirements of multi occasions, multi states and multi DOF, a multi-dimensional damping platform based on magnetorheological damper is proposed. By changing the link mode between the dynamic platform and the static platform to achieve the vibration reduction goal under different DOF, a multi-dimensional vibration reduction platform model based on the parallel structure design under 3-PRC is given. The spherical joint is connected in parallel with the static platform to meet the vibration reduction requirements of each branch under different directional excitation. In the meantime, the use of MR damper can achieve the purpose of multi-dimensional vibration reduction with continuous and forward and inverse adjustable damping force, large adjustable extent and fast reaction speed. It provides a more effective idea for the design of multi-dimensional damping platform working under variable excitation conditions.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.938
Threshold uncertainty score0.526

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 head0.010
GPT teacher head0.195
Teacher spread0.185 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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
Published2021
Admission routes1
Has abstractyes

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