Frequency-velocity-deflection-dependent damping properties of a hydro-pneumatic strut with gas-oil emulsion
Bibliographic record
Abstract
A newly introduced gas-oil emulsion strut (GOES) is utilized in suspension systems studies, however, parametric analysis of the stiffness and damping characteristics of this strut has not been precisely addressed considering the effect of frequency, velocity, and deflection. Therefore, this study aims to analyze GOES’s properties based on parametric variations, namely, charge pressure, gas volume fraction, and initial gas-to-oil volume ratio. The experiment was conducted under three different charge pressures (0.68, 1.38, 1.79 MPa). The pressure/force-displacement and pressure/force-velocity properties of the strut were measured using harmonic excitations within the 0.1–6 Hz frequency range. The nonlinear pressure-dependent model was developed using the real gas law and was validated via the experimental data. The spring force and stiffness of the GOES were evaluated under ramp excitation with very low velocity (1 mm/s), and the effect of variations in terms of charge pressure, gas fraction volume, and gas-to-oil volume ratio on the stiffness properties were investigated. The predicted results revealed a negligible effect of gas volume fraction on GOES’s spring force and stiffness. However, stiffness properties were highly dependent on the charge pressure and gas-to-oil volume ratio. Experimental results demonstrated that the damping force of GOES is influenced by velocity, the strut’s instantaneous deflection, and the frequency of excitation. Furthermore, despite the same velocity and deflection resulting from different excitations, the absolute value of the damping force decreases with an increase in excitation frequency. In addition, parametric analyses focusing on damping force and coefficient were also performed. The results reveal that charge pressure has a minimal impact on damping properties. However, increasing the gas volume fraction from 0% to 15% and gas-to-oil volume ratio from 14% to 20% contribute to significant decreases in the maximum damping coefficient by 77% and 35%, respectively.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".