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Record W4389540749 · doi:10.17118/11143/21166

A comparison of directional performance of articulated heavyvehicles

2023· article· en· W4389540749 on OpenAlexaff
Qinghui Zhou, Haonan Zhang, Yongzhu Huang, Yuping He

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsUniversity of Ontario Institute of Technology
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Abstract: With the increase of international logistics supply chains, modular articulated heavy vehicle (AHV) configurations in freight transport are expected to develop rapidly in China. It is in the process to make a decision on Chinese modular AHV configurations, i.e., what modular configuration for AHVs should be firstly developed and deployed? In order to address the issue, two configurations of AHV were evaluated considering the actual transport situations in China. The lateral stability and the maneuverability of the two configurations AHV, i.e., type-A and -B, were examined using multi-body dynamic modelling and simulation. Numerical simulations were conducted to assess the main directional performance measures, i.e., rearward amplification (RWA) and path-following offtracking (PFOT). Simulations show that the RWA measure of type-B is greater than that of type-A in high-speed evasive maneuvers. In contrast, low-speed PFOT of type-A is larger than that of type-B. Type-A is recommended to be developed first due to the following facts: 1) this AHV exhibits better high-speed lateral stability, 2) the low-speed PFOT of this AHV can be enhanced using advanced vehicle safety systems, e.g., active trailer steering. The achieved results may provide useful guidelines for manufacturers to select and develop effective modular configurations for AHVs.

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 score0.076
Threshold uncertainty score0.119

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 head0.041
GPT teacher head0.317
Teacher spread0.276 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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