Physics Based Control Oriented Model for HCCI Combustion Timing
Bibliographic record
Abstract
Incorporating homogeneous charge compression ignition (HCCI) into combustion engines for better fuel economy and lower emission requires understanding the dynamics influencing the combustion timing in HCCI engines. A control oriented model to dynamically predict cycle-to-cycle combustion timing of a HCCI engine is developed. The model is designed to work with parameters that are easy to measure and to have low computation time with sufficient accuracy for control applications. The model is a full-cycle model and consists of a residual gas model, a modified knock integral model, fuel burn rate model, and thermodynamic models. In addition, semi-empirical correlations are used to predict the gas exchange process, generated work and completeness of combustion. The developed model incorporates the thermal coupling dynamics caused by the residual gases from one cycle to the next cycle. The model is parameterized by over 5700 simulations from a detailed thermokinetic model and experimental data obtained from a single-cylinder engine. Cross-validation of the model with both steady-state and transient HCCI experiments for four different primary reference fuel blends is detailed. With seven model inputs, the combustion timing of over 150 different HCCI points is predicted to within an average error of less than 1.5 deg of crank angle. A narrow window of combustion timing is found to provide stable and efficient HCCI operation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".