A novel approach for exhaust gas recirculation stratification in a spark-ignition engine
Bibliographic record
Abstract
Over the years, various stratified exhaust gas recirculation (EGR) concepts have been proposed and studied to increase the total amount of EGR that could be trapped within the cylinder so as to decrease NO emissions while lessening the negative impact of high dilution. The different concepts normally involved modifying the intake manifold so as to sequentially feed the cylinder with air and EGR or to create an asymmetrical EGR supply in the intake ports. The present work proposes a novel proof of concept aimed at achieving a stratified EGR in a sparkignition engine. For the first time, stratified EGR is done by directly injecting exhaust gas through the cylinder head using solenoid valves controlled by an ECU. By this way, the proposed approach allows EGR injection at any time during the intake and compression stroke. The proposed concept was experimentally implemented by modifying a single-cylinder spark-ignition engine. Such an approach allows evaluating EGR stratification with respect to combustion, fuel consumption and emissions, at higher speed and load than previously reported with other stratified concepts. Three different EGR injection timings were evaluated and compared to a homogeneous EGR strategy. It has been possible to simultaneously lower fuel consumption by 6 % at 2100 RPM and high EGR rate while producing 2.6 % less NO emission when compared to a homogeneous EGR. Moreover, by injecting EGR directly in the cylinder allows enhancing the combustion process, as measured by a shorter combustion duration, by 17 % at 2500 RPM with a 20 % EGR-rate. The proof of concept has shown that stratified EGR is possible at high load and that EGR injection timing needs to be retarded with increasing engine speed for better performance.
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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 | 0.001 |
| 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".