Simulation of Vehicle Pothole Test and Techniques Used
Bibliographic record
Abstract
During the service life, the impacts of vehicle against potholes result in damage for the wheel and suspension components. Knowing the internal forces generated in the suspension components during this event would helpful to design the critical components. Measurement of these loads in physical test is more costly and not feasible for new designs. There are several virtual tools and methods available to predict the loads during this event. Using the ABAQUS FE solver, the non-linear dynamic behavior could be captured accurately during the impact. The tire model plays an important role during this event by absorbing energy during the impact. The CAE tire model is validated with some physical tests results and it is used in the vehicle pothole impact simulation. In vehicle pothole physical test, the force and acceleration measurement are taken and compared with the CAE results. The effect of the tire pressure variations and the vehicle speed at pothole impact is also studied. This paper explains more about the modeling of tires, vehicle, simulation methodology and the correlation of the CAE results against the test values.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".