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Record W1719464300 · doi:10.1504/ijvd.1992.061749

Nonlinear ride analysis of heavy vehicle using local equivalent linearization technique

2014· article· en· W1719464300 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Vehicle Design · 2014
Typearticle
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNonlinear systemEngineeringControl theory (sociology)Suspension (topology)Parametric statisticsRide qualityLinearizationVehicle dynamicsTractorAutomotive engineeringStructural engineeringMathematicsComputer sciencePhysics

Abstract

fetched live from OpenAlex

A multi–degree–of–freedom ride model of a typical tractor–semitrailer configuration comprising of nonlinear suspension damping mechanisms is developed. A local equivalent linearisation technique based on energy balance is employed to obtain the ride response of the nonlinear vehicle model. The critical behaviour of vehicle suspension(s) lock–up due to high magnitude of Coulomb (dry) friction is modelled and incorporated in the analytical technique such that the nonlinear behaviour of the system can be effectively simulated by the equivalent linear system. The effectiveness of local equivalent linearisation technique is demonstrated by comparing it with respect to conventional numerical integration and statistical linearisation techniques. A comprehensive parametric study is performed to investigate the influence of vehicle suspension characteristics, vehicle geometry, speed, highway profile and trailer loading patterns.

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: Empirical · Consensus signal: none
Teacher disagreement score0.663
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.021
GPT teacher head0.270
Teacher spread0.248 · 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