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Record W4651427 · doi:10.12783/jmc.v1i2.61

Experimental Investigation and Control of Magnetorheological Damper towards Smart Energy Absorption based Composite Structures for Crashworthiness

2013· article· en· W4651427 on OpenAlexvenueno aff
Shen Hin, B. Gangadhara Prusty, Ann Lee, Guan Heng Yeoh

Bibliographic record

VenueJournal of Medical Cases · 2013
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsnot available
Fundersnot available
KeywordsDamperCrashworthinessMagnetorheological fluidComposite numberMagnetorheological damperStructural engineeringWork (physics)Mechanical engineeringEngineeringFinite element methodComposite materialMaterials science

Abstract

fetched live from OpenAlex

This paper presents an extended experimental investigation and efficient control of magnetorheological (MR) damper towards smart energy absorption based composite structures for systems for crashworthiness. While the experimental evaluation of an existing MR damper based on the damping force was successful in our earlier work, the MR damper capability can be further examined with the wider range of velocities. Using two arms configuration, an experimental test rig is designed to enable the MR damper to be investigated throughout its full velocity range capability. A MR damper compatibility study to an existing composite tube was also conducted and showed promising quality to improve composite structures as systems for crashworthiness. A controller was then developed based on the MR damper investigation to provide automated variable control of induced current with a set crushing force and available data of composite tube crushing force. Numerical analysis on the proposed controller conveyed that MR damper was successfully controlled to provide consistent crushing force despite oscillation from the composite tube crushing force.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.774
Threshold uncertainty score0.803

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 head0.014
GPT teacher head0.237
Teacher spread0.222 · 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 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
Published2013
Admission routes1
Has abstractyes

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