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Record W2909247840 · doi:10.1115/imece2018-86143

An Energy-Regenerative Suspension System

2018· article· en· W2909247840 on OpenAlexaff
Thomas Lato, Huiyong Zhao, Lin Zhao, Yuping He

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMagnetic Bearings and Levitation Dynamics
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsAutomotive engineeringSuspension (topology)TachometerShock absorberEngineeringEnergy (signal processing)SimulationMechanical engineeringElectrical engineeringDC motor

Abstract

fetched live from OpenAlex

This paper presents an energy-regenerative suspension device that is able to harvest some of the wasted energy that is generated in a suspension system. For a traditional road vehicle suspension system, shock absorbers are mainly dissipating energy to reduce vibration. The dissipated energy may be collected to improve the fuel economy of road vehicles. In this research, CarSim and Simulink are used to simulate and determine the harvestable energy in a conventional shock absorber under different operating conditions. A conceptual energy-regenerative absorber is designed and tested using a fabricated prototype. A variable speed motor is implemented to adapt the change of stroke length of a mechanism due to the various road roughness. Instruments, e.g., laser tachometer, pressure gauge, ammeter, voltmeter, and stopwatch, are used to collect data. The simulation and prototype testing results indicate that the proposed energy-regenerative suspension device could harvest dissipated energy to improve vehicle fuel economy.

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: Empirical
Teacher disagreement score0.973
Threshold uncertainty score0.409

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 head0.197
Teacher spread0.191 · 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
Published2018
Admission routes1
Has abstractyes

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