Added value of thermodynamic white-box data for HCCI combustion prediction
Bibliographic record
Abstract
Homogeneous charge compression ignition (HCCI) is a promising combustion process to reducing both greenhouse gas and pollutant emissions in the transportation sector. The combustion starts when the thermodynamic state of the cylinder charge reaches the auto-ignition properties of the fuel, which is challenging to model. In addition, the strong cyclic coupling due to required exhaust gas recirculation leads to a significant dependence on the combustion of the previous cycle. This work focuses on identifying accurate models to predict the combustion process for potential use in an MPC. In this work HCCI is achieved using negative valve overlap (NVO) on a fully flexible valve train. The fluctuating composition and temperature of the cylinder charge is not directly measurable and not taken into account in state of the art approaches. It is assumed that it has a decisive influence on the start of combustion. Hence, in this work, the thermodynamic state is modeled by a detailed physical white-box model, which is used as an additional input for the combustion prediction. To investigate the added value of the thermodynamic state data, an Artificial neural network (ANN) is trained to predict combustion both with and without the thermodynamic state data from the white-box model. The root-mean-square error of the ANN including thermodynamic data is reduced by 12.7%. Thus, by adding the thermodynamic white-box data, the HCCI combustion prediction can be significantly improved.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".