A Study of Combustion Inefficiency in Diesel Low Temperature Combustion and Gasoline–Diesel RCCI Via Detailed Emission Measurement
Bibliographic record
Abstract
Reduction of engine-out NOx emissions to ultra-low levels is facilitated by enabling low temperature combustion (LTC) strategies. However, there is a significant energy penalty in terms of combustion efficiency as evidenced by the high levels of hydrocarbon (HC), carbon monoxide (CO), and hydrogen emissions. In this work, the net fuel energy lost as a result of incomplete combustion in two different LTC regimes is studied—partially premixed compression ignition (PPCI) using in-cylinder injection of diesel fuel and reactivity controlled compression ignition (RCCI) of port injected gasoline and direct injected diesel. A detailed analysis of the incomplete combustion products was conducted. Test results indicated that carbon monoxide (CO), hydrogen, and light hydrocarbon (HC) made up for most of the combustion in-efficiency in the PPCI mode, while heavier HC and aromatics were significantly higher in the RCCI mode.
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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".