Optimal fit clearance of control valve considering energy consumption based on loosely coupled fluid-solid interaction
Why this work is in the frame
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Bibliographic record
Abstract
The wireless intelligent water distributor is an emerging technology increasingly used in oil fields to boost oil recovery. Although the system has many advantages, the high energy consumption of its integrated control valve hinders its adoption in long-term applications. However, existing studies have not sufficiently captured the mechanical characteristics of forces acting on the spool, particularly under the influence of multiple coupled factors, such as fit clearance, deflection, and fluid-structure interactions, which limit the ability to accurately assess energy consumption. To address this gap, this study introduces a comprehensive energy consumption model of the control valve, specifically addressing the design of fit clearance, a critical factor influencing energy efficiency. Utilizing a loosely coupled fluid-structure interaction algorithm, we established a mechanical model of the spool to investigate the fluid-solid interaction mechanisms within the fit clearance. This model incorporates, for the first time, the effects of contact friction on energy consumption. An optimization algorithm was then applied to determine the optimal fit clearance by balancing low friction and minimal leakage. The validity of our numerical model was confirmed through comparison with both theoretical and experimental results. Our results demonstrate that the fit clearance has a pronounced impact on the valve's energy usage: the total maximum energy consumption for one full stroke with a 0.05 mm clearance is 3.33% higher than with a 0.25 mm clearance. When the spool is independently driven, this value is 7.21%. The optimal fit clearance is determined to be 0.127 mm. These results can improve the overall performance and extend the lifespan of intelligent water distributors. The findings and models developed in this study provide essential theoretical support and practical strategies for optimizing control valve energy consumption.
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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 | 0.000 | 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) | 0.000 | 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 it