Grey-box modeling and control of HCCI engine emissions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Real-time model based control of Homogeneous Charge Compression Ignition (HCCI) engines faces a critical challenge of maintaining a perfect balance between model accuracy and computational load. In particular, currently available HCCI emissions models in the literature are highly computationally expensive for control applications. This paper develops a computationally efficient grey-box HCCI engine model for predicting Total Hydrocarbon (THC), Carbon Monoxide (CO), and Nitrogen Oxides (NO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</inf> ). The grey-box model consists of a feed forward Artificial Neural Networks (ANN) model in combination with physical models for estimating combustion phasing and Indicated Mean Effective Pressure (IMEP). The emission model is experimentally validated over a large range of HCCI engine operation including 208 steady state test conditions. The validation results show that the grey-box model is able to predict NOx, CO, and THC with average relative errors less than 10%. Using a Genetic Algorithm optimization method along with the developed emission grey-box model, an optimum CA50 trajectory is obtained for every given load trajectory in order to minimize THC and CO emissions. A model-based controller is designed and tested on the grey-box virtual engine model for tracking IMEP and the optimum CA50 trajectories, while indirectly minimizing the engine emissions. Control results show that the developed grey-box model is of utility for real time HCCI control applications.
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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