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Record W2028617054 · doi:10.4271/2011-01-0989

Semi-Active Suspension Design for Enhancing Vehicle Ride, Road-Friendliness and Braking Performance

2011· article· en· W2028617054 on OpenAlexaff
Dongpu Cao, Amir Khajepour

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2011
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAutomotive engineeringSuspension (topology)Computer scienceDynamic brakingVehicle dynamicsVehicle safetyBraking distanceEngineeringRetarderBrakeMathematics

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">This study proposes a novel semi-active hydro-pneumatic suspension design and investigates its performance potentials. The proposed new semi-active suspension design involves pneumatic interconnection between the front and rear suspension struts of the vehicle. The analytical formulations of suspension forces due to two suspension configurations, a passive unconnected and the proposed semi-active interconnected, are derived to analyze suspension properties. Based on a validated pitch-plane vehicle braking model, vehicle dynamic responses are conducted under a range of measured road roughness excitations and driving speeds, as well as braking inputs. The results of fundamental suspension properties and vehicle responses demonstrate the considerably potentials of the proposed novel semi-active suspension in improving vehicle pitch ride, pitch attitude control, and road-friendliness under random road excitations, without influencing vehicle vertical ride, and pitch attitude control when braking. These suggest the enhanced overall vehicle pitch-plane dynamics. Moreover, the proposed semi-active suspension design also offers considerably potential benefits in improving vehicle road-holding and therefore handling dynamics and directional stability.</div></div>

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.987
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.015
GPT teacher head0.215
Teacher spread0.200 · 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.

Study designObservational
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

Citations1
Published2011
Admission routes1
Has abstractyes

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