Exhaust gas recirculation control through extremum seeking in a Low Temperature Combustion diesel engine
Bibliographic record
Abstract
Low Temperature Combustion (LTC) modes in diesel engines are characterized by enhanced homogeneity of the combustion mixture resulting from a longer ignition delay when compared to conventional diesel combustion. This is enabled by charge density and dilution control, coupled with modulation of fuel injection parameters. Charge dilution is achieved by exhaust gas recirculation (EGR), while turbocharging enables in-cylinder charge density increase. The coupling between the EGR and turbocharging systems exhibits highly non-linear interactions in the engine air-path. In this work, a two part control strategy is investigated for the regulation of EGR and turbocharging in a diesel engine to direct the combustion to approach LTC without largely compromising the combustion efficiency. Firstly, a simplified engine air-path model is presented that emphasizes the correlation between the intake oxygen concentration ([O2-int]) set-point and the individual EGR and turbocharging actuator set-points at different engine operating points. Thereafter, experimental data is presented that highlights the sensitivity of engine-out NOx emissions and combustion efficiency against the [O2-int]. Secondly, an extremum seeking (ES) algorithm is used to determine the [O2-int] set-point using a cost function that results in a desirable emission and combustion performance. Finally, the coordinated execution of the ES algorithm and the air-path model to generate the air-path actuator set-points is discussed.
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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 | 0.000 | 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 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".