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Record W4226391275 · doi:10.1504/ijvp.2022.122062

Roll dynamics of long combination semi-trailers with steerable axles

2022· article· en· W4226391275 on OpenAlexaffabout
Borna Monazzah Moghaddam, Wei Huang, Luke Steiginga, Gordon Poole, Robin Chhabra

Bibliographic record

VenueInternational Journal of Vehicle Performance · 2022
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsNational Research Council CanadaCarleton University
Fundersnot available
KeywordsAxleTrailerVehicle dynamicsAutomotive engineeringEngineeringTraction control systemControl theory (sociology)Controller (irrigation)Lock (firearm)SimulationComputer scienceControl engineeringStructural engineeringControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

An assessment of the dynamic performance of long combination vehicles (LCV) with steerable axles was undertaken to facilitate the regulation of LCVs for wider use on Canadian roads. A base dry box van A-train LCV and four steered combinations with different steerable axle mechanisms on the trailer are modelled using TruckSim. TruckSim's driver logic is augmented through an optimised controller to more accurately capture the driver's decision-making in response to the LCV dynamics. Anti-lock braking system (ABS) and traction control mechanisms are added to compensate for the reduced stability caused by improving the manoeuvrability. The models are run through highspeed lane change and turn simulations, the primary manoeuvres for the assessment of roll dynamics of LCVs. The configurations are compared in terms of standard performance parameters: static roll threshold, rearward amplification (RWA), load transfer ratio (LTR), highspeed off-tracking and transient off-tracking. All mechanisms are shown to satisfy standard stability requirements.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.340

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 head0.003
GPT teacher head0.179
Teacher spread0.176 · 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

Citations4
Published2022
Admission routes2
Has abstractyes

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