Design and Analysis of a Flow-Control Valve With Controllable Pressure Compensation Capability for Mobile Machinery
Bibliographic record
Abstract
There are the problems of low flow control accuracy, small flow control difficulty, and limited flow range in the traditional pressure-compensation flow-control valve. For this, a method of continuous control pressure drop Δpratedto control flow-control valve flow is proposed. And the precise control of small flow is realized by reducing the pressure drop Δprated. In the research, the flow-control valve with controllable pressure compensation capability (FVCP) was designed firstly and theoretically analyzed. Then the sub-model model of PPRV and traditional flow-control valve were established and verified through experiments respectively. Finally, the accurate co-simulation model of the FVCP was established. The continuous control characteristics of pressure drop Δprated, the flow characteristics of FVCP, and the influence of different parameters d2were studied. The research results demonstrate that, compared with the traditional flow-control valve, the designed FVCP can adjust the compensation pressure difference in the range of 0.3 ~ 1.7 MPa in real-time. And the flow rate can be altered within the range of 44% ~ 100% of the rated flow. The diameter d2of the console shoulder has almost no effect on the compensator operating characteristics.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".