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Record W2799974098 · doi:10.1139/tcsme-2016-0063

DESIGN AND EVALUATION OF A RIDE COMFORT BASED SUSPENSION SYSTEM USING AN OPTIMAL STIFFNESS-DETERMINATION METHOD

2016· article· en· W2799974098 on OpenAlexvenueno aff
Jing Zhao, Pak Kin Wong, Zhengchao Xie, Caiyang Wei, Rongchen Zhao

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsnot available
Fundersnot available
KeywordsAir suspensionDamperSuspension (topology)StiffnessSensitivity (control systems)Process (computing)Work (physics)Control theory (sociology)Computer scienceSpring (device)Structural engineeringEngineeringAutomotive engineeringMechanical engineeringMathematicsElectronic engineering

Abstract

fetched live from OpenAlex

This paper focuses on a ride comfort based suspension (RCS) system using an optimal stiffness-determination method. The proposed RCS system is composed of a variable hydraulic damper with gas chamber (VHDGC) and an air spring. In this work, the detailed structure, modeling process and parameter sensitivity of the proposed VHDGC are presented. Moreover, the mathematical relationship between the proposed damper and the air spring is considered. Numerical results reveal that the ride comfort of the proposed RCS system can be greatly improved as compared with the passive suspension. In addition, the overall performance of the RCS system is also guaranteed. In sum, the proposed RCS system is a promising solution in improving the ride comfort of the vehicle.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.261
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 source (direct Gemma or distilled Codex), 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

Citations14
Published2016
Admission routes1
Has abstractyes

Explore more

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