Predicting the Distribution of Combustion Timing Ensemble in an HCCI Engine
Bibliographic record
Abstract
Control of Homogeneous Charge Compression Ignition (HCCI) engines to obtain the desirable operation requires understanding of how different charge variables influence the cyclic variations in HCCI combustion. Combustion timing for consecutive cycles at each operating point makes an ensemble of combustion timing which can exhibit different shapes of probability distributions depending on the random and physical patterns existing in the data. In this paper, a combined physical-statistical control-oriented model is developed to predict the distribution of HCCI combustion timing (CA50, crank angle of 50% fuel mass fraction burnt) for a range of operating conditions. The statistical model is based on the Generalized Extreme Value (GEV) distribution and the physical model embodies a modified knock integral model, a fuel burn rate model, a semi-empirical model for the gas exchange process and an empirical model to estimate the combustion timing dispersion. The resulting model is parameterized for the combustion of Primary Reference Fuel (PRF) blends using over 5000 simulations from a detailed thermo-kinetic model. Empirical correlations in the model are parameterized using the experimental data obtained from a single-cylinder engine. Once the model is parameterized it only needs five inputs: intake pressure, intake temperature, Exhaust Gas Recirculation (EGR) rate, equivalence ratio and engine speed. The main outputs of the model are CA50 and the Probability Density Function (PDF) metrics of CA50 distribution. Experimental CA50 is compared with predicted CA50 from the model and the results show a total average error of less than 1.5 degrees of crank angle for 213 steady-state operating points with four different PRF fuels at diversified operating conditions. Predicted PDF of the CA50 ensemble is compared with that of the experiments for PRF fuels at different running conditions. The results indicate a good agreement between simulation and the experiment.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".