A comparison of directional performance of articulated heavyvehicles
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".