MétaCan
Menu
Back to cohort
Record W4225685866 · doi:10.1504/ijvp.2022.122064

Analytical and experimental investigation of roll stability of a truck towing a special purpose trailer with no suspension

2022· article· en· W4225685866 on OpenAlexaff
Luke Steiginga, Wei Huang, Gordon Poole

Bibliographic record

VenueInternational Journal of Vehicle Performance · 2022
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsTowingTrailerSuspension (topology)TruckAutomotive engineeringStability (learning theory)EngineeringArticulated vehicleVehicle dynamicsLongitudinal static stabilityMarine engineeringComputer scienceStructural engineeringAerospace engineeringAerodynamicsMathematics

Abstract

fetched live from OpenAlex

There are an increasing number of systems available to increase roll stability; however some are not present on all vehicles. Most recent research is focused on ways to improve roll stability, but research into the roll stability of existing vehicles is also important in determining safe operating conditions. This study investigates the roll stability of three vehicles towing a special-purpose trailer currently in use that has no brakes or suspension. Multibody dynamics models of the vehicles were built to simulate vehicle performance on high-speed turns and lane changes. Model validation was performed by comparing results between the simulation and physical testing. Roll stability was assessed by comparing static roll threshold (SRT) and load transfer ratio (LTR) values. All of the vehicles were shown to meet performance standards on a smooth surface, but introduction of surface roughness significantly decreased the roll stability of the trailer due to the lack of suspension.

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 score0.752
Threshold uncertainty score0.273

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 head0.009
GPT teacher head0.206
Teacher spread0.197 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Vehicle PerformanceSame topicVehicle Dynamics and Control SystemsFrench-language works237,207