Multi-Wheeled Combat Vehicle Tire Modeling on Rigid and Soft Terrain
Bibliographic record
Abstract
Multi-wheeled off-road vehicles performance depends not only on the total engine power but also on its distribution among the drive axles/wheels. In turn, this distribution is largely regulated by the drivetrain layout and its torque distribution which is constrained by the interaction between the wheels and the terrain (rigid or soft soil). In this paper three-dimensional, non-linear Finite Element Analysis (FEA) off-road tire models on rigid and soft terrain were developed using PAM-CRASH and the general trends of vertical load-deflection, cornering characteristics and aligning moment on rigid terrains are compared with published measured data of a similar tire for validation purposes. Non-linear tire look-up tables for rigid and soft terrain were developed based on FEA off-road tire simulation results and used for vehicle simulation using the multi-body dynamics code TruckSim. The predictions of vehicle handling characteristics and transient response during lane change test on rigid road at different vehicle speeds were compared with simulation results for same vehicle configuration using real experimental tire data. Simulation results are compared on the basis of vehicle steering, yaw rates and accelerations. The published US Army validation criteria has been used to validate simulation results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".