Development and experimental validation of a real-time capable field programmable gate array–based gas exchange model for negative valve overlap
Bibliographic record
Abstract
Homogeneous charge compression ignition has the potential to significantly reduce NO x emissions, while maintaining a high fuel efficiency. Homogeneous charge compression ignition is characterized by compression-induced autoignition of a lean homogeneous air–fuel mixture. Combustion timing is highly dependent on the in-cylinder state including pressure, temperature and trapped mass. To control homogeneous charge compression ignition combustion, it is necessary to have an accurate representation of the gas exchange process. Currently, microprocessor-based engine control units require that the gas exchange process is linearized around a desired operating point to simplify the model for real-time implementation. This reduces the models’ ability to handle disturbances and limits the flexibility of the model. However, using a field programmable gate array, a detailed simulation of the physical gas exchange process can be implemented in real time. This paper outlines the process of converting physical governing equations to an offline zero-dimensional gas exchange model. The process used to convert this model to a field programmable gate array capable model is described. This model is experimentally validated using a single cylinder research engine with electromagnetic valves to record real-time field programmable gate array gas exchange results and comparing to the offline zero-dimensional physical model. The field programmable gate array model is able to accurately calculate the cylinder temperature and cylinder mass at 0.1 °CA intervals during the gas exchange process for a range of negative valve overlaps, boost conditions and engine speeds making the model useful for future real-time control applications.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".