Effect of Cooled and Hot EGR on Performance and Emission Characteristics of S-I Engine
Bibliographic record
Abstract
Exhaust gas recirculation (EGR) is the most effective and practical method for reduction of NOx emission in Internal Combustion Engines. However, improper application of EGR in the engine causes higher power loss, excessive specific fuel consumption (SFC) and an increase in the concentration of other pollutants. For this reason, the amount and temperature of EGR must be tailored for each engine in order to get optimum performance and emission results. In this research work, after comparison of hot and cooled EGR for an S-I engine, pollutant behavior has also been studied around Dew point temperature of exhaust gasses. Results indicate that the pollutant’s behavior depend on the EGR mixture (amount and temperature), thermal capacity of EGR and amount of water condensation. For example, when the amount of EGR is 10% there is a higher reduction of NOx with ignorable power loss. Results also showed that the optimum temperature in experimental conditions and in this particular engine is about 340–343 Kelvin, which is just above the Dew point of exhaust gases.
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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 | 0.000 |
| 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 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".