Dynamic Modeling of HCCI Combustion Timing in Transient Fueling Operation
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Bibliographic record
Abstract
<div class="htmlview paragraph">A physics-based control-oriented model is developed to dynamically predict cycle-to-cycle combustion timing in transient fueling conditions for Homogeneous Charge Compression Ignition (HCCI) engines. The model simulates the engine cycle from the intake stroke to the exhaust stroke and includes the thermal coupling dynamics caused by the residual gases from one cycle to the next cycle. A residual gas model, a modified knock integral model, a fuel burn rate model, and thermodynamic models for the gas state in combustion and exhaust strokes are incorporated to simulate the engine cycle. The gas exchange process, generated work and completeness of combustion are predicted using semi-empirical correlations. The resulting model is parameterized for the combustion of Primary Reference Fuel (PRF) blends using 5703 simulations from a detailed thermo-kinetic model. Semi-empirical correlations in the model are parameterized using the experimental data obtained from a single-cylinder engine. The dynamics of fuel transport from intake port into the cylinder is described using the wall wetting fuel dynamic model. Step Air Fuel Ratio (AFR) excursions are used to excite the HCCI engine to determine the parameters for fuel dynamics using methods of system identification. In addition, the dynamics of exhaust gas transport and AFR sensor are included to correctly interpret measurements from the AFR sensor.</div> <div class="htmlview paragraph">Step fueling transient tests of the equivalence ratio and octane number are used to validate the model. Compared to the experimental measurements the model captures the ignition timing (CA50) transient dynamics with a maximum offset less than 2 degrees of crank angle. The model developed in this work seems promising for HCCI control as: all of its inputs can be relatively easily measured or estimated on an engine; has low computation requirements, and seems to have sufficient accuracy for control applications.</div>
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 it