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Record W2140093702 · doi:10.1115/1.4030631

A Methodology for Optimal Design of a Vehicle Suspension System With Energy Regeneration Capability

2015· article· en· W2140093702 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of vibration and acoustics · 2015
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSuspension (topology)Energy (signal processing)StiffnessPower (physics)Automotive engineeringRegenerative brakeComputer scienceControl theory (sociology)EngineeringControl (management)Structural engineering

Abstract

fetched live from OpenAlex

This paper proposes a systematic methodology for predicting and optimizing the performance of an energy regenerative suspension system to efficiently capture the vibratory energy induced by the road irregularities. The method provides a graphical design guideline for the selection of stiffness and damping coefficients aimed at either best ride comfort or maximum energy harvesting. To achieve energy regeneration capability, a low-power electronic circuit capable of providing a variable load resistance is developed and fabricated. The circuit is controlled to provide an adjustable damping coefficient in the real-time. A test-bed is utilized to experimentally verify the proposed techniques. The results indicate that the analytical and simulation results concerning the optimal values for dynamic control and power regeneration match the experimental results.

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.

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)0.001
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 score0.774
Threshold uncertainty score0.187

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.055
GPT teacher head0.251
Teacher spread0.196 · 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