Dynamic input loads evaluation of a recreational vehicle frame using multibody dynamics hybrid modeling validated with experimental and full analytical modeling data
Bibliographic record
Abstract
Knowledge of frame loads at the limits of the intended driving conditions is important during the design process of a vehicle structure. Yet, retrieving these loads is not trivial as the load path between the road and the frame mounting point is complex. Fortunately, recent studies have shown that multibody dynamic (MBD) simulations could be a powerful tool to estimate these loads. Two main categories of MBD simulations exist. Firstly, full analytical simulations, which have received great attention in the literature, are run in a virtual environment using a tire model and a virtual road. Secondly, hybrid simulations, also named semi analytical, uses experimental data from Wheel Force Transducers and Inertial Measurement Units to replace the road and tire models. It is still unclear how trustworthy semi analytical simulations are for frame load evaluation. Both methods were tested for three loads cases. It was found that semi analytical simulations were slightly better in predicting vehicle dynamic and frame loads than the full analytical simulations for frequencies under the MF-Tyre model valid frequency range (8 Hz) with accuracy levels over 90%. For faster dynamic maneuvers, the prediction accuracy was lower, in the 50%–80% range, with semi analytical simulations showing better results.
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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) | 0.002 | 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".