Non-Linear Modeling of Bushings and Cab Mounts for Calculation of Durability Loads
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">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.</div><div class="htmlview paragraph">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.</div></div>
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it