The impact of intake pressure on high exhaust gas recirculation low-temperature compression ignition engine combustion using borescopic imaging
Bibliographic record
Abstract
In diesel engines, high levels of exhaust gas recirculation can be used to achieve low-temperature combustion, resulting in low emission levels of both nitrogen oxides (NO x ) and particulate matter. This work studied the effects of varying the intake manifold pressure on in-cylinder combustion processes and engine-out emissions from a light-duty single cylinder diesel engine under conventional and high exhaust gas recirculation low-temperature combustion regimes. The work was conducted at a part-load cruise condition of 1500 r/min and at an indicated mean effective pressure of approximately 600 kPa. Exhaust gas recirculation rates were varied between 0% and 65% at absolute intake pressures of 100–150 kPa. Very low NO x emissions were achieved (<10 ppm, ∼0.05 g/kW h) for intake oxygen mass fractions below about 11%, independent of boost pressure. Smoke emission levels were lower than for non–exhaust gas recirculation combustion at oxygen mass fractions below ∼9%, depending on the boost pressure. High intake pressures reduced fuel consumption by 15% and combustion by-product emissions by 50%–60% compared to low boost. For the low intake boost case, little visible flame was apparent through borescope imaging. At higher boost pressures, intense flame luminosity was observed within the piston bowl early in the expansion stroke. Spatially averaged soot luminosity based on photomultiplier tube data showed that peak soot luminosity was five times greater and occurred 8 °CA earlier for high boost. This work demonstrates how the combination of appropriate boost pressures and exhaust gas recirculation rates can be used to mitigate the emissions and thermal efficiency penalties of high-dilution low-temperature combustion to achieve near-zero NO x operation.
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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) | 0.001 | 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".