MétaCan
Menu
Back to cohort
Record W2009692306 · doi:10.1115/imece2005-79631

Isolation Application of Controlled and Uncontrolled Magnetorheological Dampers for Random Base Excitaion

2005· article· en· W2009692306 on OpenAlexaff
A. Narimani, M. F. Golnaraghi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDamperIsolatorMagnetorheological fluidShock absorberTransient (computer programming)Control theory (sociology)Base isolationMagnetorheological damperBase (topology)Suspension (topology)Structural engineeringTransient responseShock (circulatory)Computer scienceEngineeringElectronic engineeringMechanical engineeringMathematicsControl (management)

Abstract

fetched live from OpenAlex

This paper presents experimental investigation of modeling and control of magnetorhological damper for transient base excitation inputs. Force characteristics of a commercially available MR damper (RD-1005-3) for shock and other transient base excitation are analytically obtained and validated using a scaled suspension model. The proposed model characterizes damper behavior more accurately and efficiently for analytical applications. The time and frequency responses of the developed model are compared with the experimental results and show good agreement. Finally, using the RMS optimization method the performance of the system for different types of controllers is compared with the optimal values of linear isolator system. Experimental results show that the performance of base isolation systems for transient and shock inputs significantly improves by utilizing a controlled semi-active damper over uncontrolled MR damper or an optimally designed passive isolator.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.922
Threshold uncertainty score0.344

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 head0.006
GPT teacher head0.204
Teacher spread0.198 · 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
Published2005
Admission routes1
Has abstractyes

Explore more

Same topicVibration Control and Rheological FluidsFrench-language works237,207