Characterization of Exhaust Gas Recirculation for Diesel Low Temperature Combustion
Bibliographic record
Abstract
Exhaust Gas Recirculation (EGR) is common on most modern diesel engines resulting in significant reduction of NOx emissions. Heavy EGR application is an enabling technique for the advanced combustion modes operating in the low temperature combustion (LTC) regime, wherein simultaneous NOx and soot emission reduction can be attained. The primary effect of EGR is the dilution of the intake charge following the displacement of fresh air by the combustion products. However, the correlation between EGR and its effectiveness is non-linear due to the lean burn nature of boosted diesel engines. This correlation is further complicated when oxygenated fuels are used for combustion in the LTC mode. In this work, the intake oxygen concentration [O 2 ] int is selected as a representative of EGR and its effectiveness in emission abatement is shown using an array of experimental results. An EGR characterization model is developed to quantify the dynamic interaction between [O 2 ] int and engine operating variables, namely the intake boost, exhaust gas recirculation (EGR) amount, the fueling quantity and the fuel type. The model is validated on the research engine platform in steady state and transient tests. Finally, the control of EGR effectiveness is experimentally demonstrated to achieve ultra-low NOx emissions at different engine operating points.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".