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Record W4379053238 · doi:10.5267/j.esm.2023.5.001

The effect of velocity on the ability to rollover of the tractor semi-trailer when turning

2023· article· en· W4379053238 on OpenAlexvenueno aff
Nguyen Thanh Tung, Luong Van Van

Bibliographic record

VenueEngineering Solid Mechanics · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsnot available
Fundersnot available
KeywordsRollover (web design)TractorAutomotive engineeringTrailerSteering wheelYawArticulated vehicleStability (learning theory)Automobile handlingComputer scienceControl theory (sociology)SimulationEngineeringTruck

Abstract

fetched live from OpenAlex

This paper aims to investigate the impact of vehicle velocity on the rollover stability of a fully loaded tractor semi-trailer during lane changes and turning. Specifically, the study focuses on velocities ranging from 30 km/h to 60 km/h. The investigation found that vehicle velocity is a critical parameter that can affect the potential for rollover during lane changes or turning. The results showed that to ensure the vehicle moves steadily and does not roll over during these maneuvers, the steering angle must be controlled and kept within certain limits. The study provided specific recommendations for maximum steering angles at different velocities. For instance, to ensure stability when the vehicle is moving at 60 km/h, the maximum steering angle should be less than 4 degrees. Similarly, at 50 km/h, the maximum steering angle is recommended to be less than 6 degrees, and at 44 km/h, it should be less than 8 degrees. At lower speeds, the recommended maximum steering angle increases, with the maximum recommended angle at 36 km/h being 12 degrees. These findings highlight the importance of carefully controlling vehicle velocity and steering angle to minimize the risk of rollover accidents. By providing specific recommendations for different velocities, this study can inform the design and safety testing of vehicles to improve their stability and safety during lane changes and turning.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.116

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.007
GPT teacher head0.194
Teacher spread0.187 · 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 designBench or experimental
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

Citations11
Published2023
Admission routes1
Has abstractyes

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