Flow force analysis and optimization of lock valve for heavy-duty automatic transmission
Bibliographic record
Abstract
One of the main factors determining the stability of lock valve during opening is the flow force on spool. The size of the flow force profoundly affects the dynamic characteristics of the spool. In this paper, the flow force on a heavy-duty automatic transmission lock valve during the opening process is analyzed and optimized, and aiming to improve the opening smoothness of the lock valve. First, numerical simulation of the opening process of the main oil chamber flow path in the lock valve is carried out using dynamic mesh technology. The influence of internal flow field on the flow force under different parameters is studied. Second, the structural parameters and peaks of flow force obtained from the random sampling method are used as samples for training and prediction using the BP neural network. The prediction results pass the accuracy test. Last, the prediction results of the BP neural network are optimized using the genetic algorithm. Subsequent results show that this optimization method significantly reduces the flow force of spool and improves the stability of the lock valve during opening by only changing the structural parameters. It also provides a new systematic direction for the optimization of other nonlinear mapping relationships.
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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 | 0.001 |
| 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 | 0.000 |
| 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".