Performance analysis of a high-frequency two-stage proportional valve piloted by two high-speed on/off valve arrays
Bibliographic record
Abstract
Two-stage proportional valves (TSPV) have been used extensively for electro-hydraulic control systems with large-flow applications. Due to the inherent characteristics of the pilot spool, such as hysteresis, motion quality, and dead zone, the dynamic performance of the traditional TSPV cannot be further improved. To solve this issue, this paper proposes a high-frequency two-stage proportional valve (HFTSPV) piloted by two high-speed on/off valve arrays, which integrates the advantages of dual nozzle flapper mechanism and parallel-connected valve technology. First, an entire mathematical model is established, in which some key parameters are estimated by experiments, such as the actual diameters and flow coefficients of orifices, and flow coefficient of pilot valve. Then, the influences of fixed orifices with different diameters on the dynamic characteristics of main spool are explored. Subsequently, the optimal diameter of fixed orifice is calculated theoretically by using the maximum pressure sensitivity and flow linearity, aiming to realize the high dynamic and small displacement fluctuation. Finally, step and sinusoidal tracking experiments show that the delay time of the HFTSPV is close to 3.3 ms, and the displacement fluctuation decreases with the increase of tracking frequency. Amplitude–frequency results indicate that −3 dB frequency of the HFTSPV reaches 16 Hz under ±50% full scale and 30 Hz under 0% –50% full scale.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".