Development of Procedures for Accurate Finite Element Modeling of the Dynamic and Quasi-Static Performance of Automotive Chassis Components Incorporating Hyperelastic Materials
Bibliographic record
Abstract
Finite element models of the vehicle for crashworthiness have traditionally included simplified representations of isolators intended to improve noise and vibration. However, the low stiffness of the hyperelastic material employed in such components allows for large deformations under impact conditions with a significant effect upon the accelerations experienced by the occupant. Modeling these components is challenging due to the non-linear behaviour of the material and the large deformations. The purpose of this research was to identify practices for developing accurate and efficient finite element models of chassis components incorporating hyperelastic materials. To maximize the comprehensiveness of this process, this research included quasi-static and dynamic material characterization; material model selection and implementation; finite element modeling techniques; quasi-static and dynamic component characterization; and model validation. Conclusions included the importance of comprehensive material characterization, material model selection, variation in results due to solver updates, and methodologies for model validation through component characterization.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".