Development and experimental validation of a field programmable gate array–based in-cycle direct water injection control strategy for homogeneous charge compression ignition combustion stability
Bibliographic record
Abstract
Homogeneous charge compression ignition is a part-load combustion method, which can significantly reduce oxides of nitrogen (NO x ) emissions compared to current lean-burn spark ignition engines. The challenge with homogeneous charge compression ignition combustion is the high cyclic variation due to the lack of direct ignition control. A fully variable electromagnetic valve train provides the internal exhaust gas recirculation through negative valve overlap which is required to obtain the necessary thermal energy to enable homogeneous charge compression ignition. This also increases the cyclic coupling as residual gas and unburnt fuel is transferred between cycles through exhaust gas recirculation. To improve combustion stability, an experimentally validated feed-forward water injection controller is presented. Utilizing the low latency and rapid calculation rate of a field programmable gate array, a real-time calculation of residual fuel mass is implemented on a prototyping engine controller. Using this field programmable gate array–based calculation, it is possible to calculate the amount of fuel and the required control interaction during an engine cycle. This controller prevents early rapid combustion following a late combustion cycle using direct water injection to cool the cylinder charge and counter the additional thermal energy from any residual fuel that is transferred between cycles. By cooling the trapped cylinder mass, the upcoming combustion phasing can be delayed to the desired setpoint. The controller was tested at several operating points and showed an improvement in the combustion stability as shown by a reduction in the standard deviation of combustion phasing and indicated mean effective pressure.
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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) | 0.001 | 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) | 0.002 | 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".