Hydraulic damping nonlinearity of a compact hydro-pneumatic suspension considering gas-oil emulsion
Bibliographic record
Abstract
Hydro-pneumatic suspension (HPS) systems could attenuate broad-frequency-range vibration mainly via the nonlinear hydraulic damping property. While the strut design with shared gas-oil chamber leads to gas-oil emulsion within strut chambers which intricately affects the fluid flows between the coupled chambers and thus the damping force. This study investigated the temperature- and frequency-dependent hydraulic damping properties of a compact hydro-pneumatic suspension strut, in terms of the flow discharge coefficients. A laboratory experiment was performed at nearly-constant strut temperature of 30, 40 and 50 °C, in the frequency range of 0.5-8 Hz. The obtained experimental data are used to identify the discharge coefficients of the emulsion flow across bleed orifices and check valves, which determine the damping property of the considered strut. An analytical model is established, and the simulation results obtained under different strut temperature and excitation frequencies showed reasonably good agreements with the experimental data. The results suggested greater discharge coefficient of the bleed orifice than that of the check valve, which might be due to the relatively complex structure of the check valves. Greater excitation frequency was shown to decrease the discharge coefficients in a nonlinear manner, irrespective of the strut temperature. Greater strut temperature, however, leaded to greater discharge coefficient of the check valve. Increasing the excitation frequency from 0.5 Hz to 8 Hz resulted in nearly 14 % decrease in the discharge coefficient of check valve at a constant strut temperature of 50 °C.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".