Advanced effective road profile filter for a rigid ring tyre quarter-vehicle model
Bibliographic record
Abstract
This paper describes the development and integration of an advanced effective road profile filter with an in-plane rigid ring tyre quarter-vehicle model. This novel fully integrated model improves upon the standard twin-cam effective road profile filter developed by taking into account the effects of vertical loading on tyre deflection and contact length; the model developed herein is said to be a force-dependent effective road profile (FDERP) rigid ring quarter-vehicle model (RRQVM) because the effective road shape parameters are treated as functions of the vertical contact force at each integration time step. The model is capable of simulating the dynamic response of a free rolling tyre over arbitrarily uneven road surfaces. The RRQVM is validated with tyre spindle vertical acceleration data from virtual finite element analysis (FEA) quarter-vehicle model (QVM) tests. A baseline in-plane RRQVM with a standard – force-independent effective road profile (FIERP) – twin-cam effective road profile filter is also developed for comparison with the FDERP RRQVM. Results for a described durability test event show that the FDERP RRQVM predicts the vertical tyre spindle acceleration more accurately than the FIERP RRQVM.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".