A novel approach for the design and analysis of nonlinear dampers for automotive suspensions
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes an analytical technique for frequency analysis and the design of nonlinear dampers to further improve ride dynamics performance of vehicle suspensions over a wide range of excitation frequencies. Using the energy balance method (EBM), the proposed methodology estimates the equivalent linear damping coefficient of any nonlinear passive damper whose force is a general function of the damper’s relative displacement and relative velocity. Knowing the equivalent linear damping coefficient makes it possible to perform a frequency analysis of the suspension ride performance with any nonlinear damper. Some specific criteria are defined to design the desired form of equivalent linear damping coefficient which provides a high/small damping ratio at low-/high-frequency excitations, so the corresponding nonlinear damping force required to obtain improved ride performance of the suspension using a 1-degree-of-freedom quarter car model is also defined. A sensitivity analysis is then performed to provide a design guideline. The results show that the dependency of the equivalent damping coefficient either relative to the velocity of the suspension (velocity-dependent damper) or the relative displacement of the suspension (position-dependent damper) could provide a variable damping ratio leading to better vibration isolation over the excitation frequency. A noticeable ride dynamic performance can be reached over the entire range of the excitation frequency by designing a nonlinear damper such that its equivalent linear damping ratio becomes a desired function of both its relative displacement and relative velocity (position-velocity-dependent damper).
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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