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Record W4376637838 · doi:10.1139/tcsme-2023-0019

Magnetic circuit design of MR controller for improving the performance of the suspension

2023· article· en· W4376637838 on OpenAlexvenueno aff
Chong Chen, Ran Ma, Wan Ma

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsnot available
Fundersnot available
KeywordsMagnetorheological fluidDamperSuspension (topology)Controller (irrigation)StiffnessMagnetic fieldVibrationMaterials scienceControl theory (sociology)Electromagnetic coilCylinderStructural engineeringMechanical engineeringComputer scienceEngineeringPhysicsAcousticsElectrical engineering

Abstract

fetched live from OpenAlex

Magnetorheological (MR) damper is widely used in civil engineering, heavy-duty vehicles to speed up safely, and so on. In this paper, an MR controller for adjusting the stiffness and damping of a semi-active air suspension is designed to improve the vibration resistance of the Electric Multiple Units suspension system. The 3D magnetic field simulations of the MR controller are carried out. The influences of factors such as shaft material, damping channel width, cylinder wall thickness on the magnetic induction intensity at the damping channel are studied. An orthogonal experiment is carried out and the optimal combination of the structural parameters is determined. Then, the four working conditions of the MR controller are simulated and analyzed, respectively, and its structure is improved to ensure its stability. Finally, using fluid simulation software analysis, it can be seen that due to the fluidity of magnetorheological fluids (MRFs), the heat generated by the coil has little effect on MRFs.

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: Empirical · Consensus signal: none
Teacher disagreement score0.966
Threshold uncertainty score0.248

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 head0.015
GPT teacher head0.180
Teacher spread0.165 · 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
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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicVibration Control and Rheological FluidsFrench-language works237,207