Non-Linear Modeling of Bushings and Cab Mounts for Calculation of Durability Loads
Bibliographic record
Abstract
Cab mounts and suspension bushings are crucial for ride and handling characteristics and must be durable under highly variable loading. Such elastomeric bushings exhibit non-linear behavior, depending on excitation frequency, amplitude and the level of preload. To calculate realistic loads for durability analysis of cars and trucks multi-body simulation (MBS) software is used, but standard bushing models for MBS neglect the amplitude dependent characteristics of elastomers and therefore lead to a trade-off in simulation accuracy. On the other hand, some non-linear model approaches lack an easy to use parameter identification process or need too much input data from experiments. Others exhibit severe drawbacks in computing time, accuracy or even numerical stability under realistic transient or superimposed sinusoidal excitation. To improve bushing modeling of cab/box mounts for heavy duty/light duty trucks, a practical approach to model non-linear bushing dynamic characteristics has been tested and validated against standard bushing models. For model parameterization, several elastomeric cab mounts have been tested for their static and dynamic properties. The paper discusses the parameter identification process and validates the new non-linear bushing model regarding simulation accuracy, usability and computing time. Typical mount loads have been measured from durability events for model evaluation. This paper assesses the use of non-linear models for mounts and bushings to calculate durability loads in the context of full-vehicle simulation.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".