A novel approach for the design and analysis of nonlinear dampers for automotive suspensions
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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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".