Integrated cycle-to-cycle control of exhaust gas temperature, load, and combustion phasing in an HCCI engine
Bibliographic record
Abstract
Precise and integrated cycle-to-cycle control of exhaust gas temperature (Texh), load, and combustion phasing is essential for realizing high efficiency Homogeneous Charge Compression Ignition (HCCI) engines with low exhaust emissions. In this paper a model-based control framework is developed for an integrated control of Texh, Indicated Mean Effective Pressure (IMEP), and combustion phasing in an HCCI engine. A discrete Control Oriented Model (COM) is developed to predict the HCCI outputs on a cycle-to-cycle basis and validated against steady-state and transient experimental data from a single cylinder Ricardo engine. The COM provides sufficient accuracy with an average uncertainty of 7 °C, 0.3 bar, and 1.6 CAD for predicting Texh, IMEP and combustion phasing, respectively. In addition, the COM is computationally efficient for real-time HCCI control. A three-input three-output controller is designed using a Discrete Sliding Mode Control (DSMC) method to control Texh, IMEP, and combustion phasing by adjusting the intake manifold pressure, fuel mass flow rate, and ratio of two Primary Reference Fuels (PRFs), respectively. The results indicate the DSMC is capable of maintaining the stability of the engine operation and tracking the desirable HCCI engine outputs, while also rejecting internal disturbances.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".