Closed Loop Electromagnetic Valve Actuation Motion Control on a Single Cylinder Engine
Bibliographic record
Abstract
In an effort to improve the efficiency of internal combustion engines, much focus has been put into variable valve actuation technologies in recent years. Electromagnetic solenoid valves can provide the cycle-by-cycle flexible valve timing needed for throttleless engine control or high efficiency combustion modes such as Homogeneous Charge Compression Ignition. One challenge with electromagnetic solenoid intake and exhaust valves is the robust control of the motion to achieve smooth landing under a variety of operating conditions. Promising algorithms have been demonstrated under test-bench conditions, but no work to date has demonstrated a robust electromagnetic valve-train on a functional engine that also satisfies soft landing and transition timing criteria. In this work, two previously developed valve motion controllers are experimentally tested on a single cylinder test engine. The controllers are compared for the opening transition of the exhaust valve with large variations in combustion pressure. A new control algorithm that combines favorable aspects of both methods is also presented. The new algorithm is shown to operate reliably under a wide range of operating conditions. An analysis indicates that the electrical energy consumed by the camless valve system is comparable to that of an equivalent conventional low friction cam-based valve train.
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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 | 0.001 | 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".