A Study of Combustion Inefficiency in Diesel LTC 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 accompanying 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. The first LTC strategy, partially premixed compression ignition (PPCI), is investigated using a single, high pressure, in-cylinder injection of diesel fuel along with the application of exhaust gas recirculation (EGR). The second strategy includes dual-fuel application – reactivity controlled compression ignition (RCCI) of port injected gasoline and direct injected diesel. Moderate to high levels of EGR are necessary during engine operation in either of the two LTC pathways. A detailed analysis of the incomplete combustion products was conducted while the engine was operated in the aforementioned LTC modes. Speciation analysis of hydrocarbons was performed by sampling the exhaust gas in an FTIR. The total HC and the CO emissions were simultaneously measured using an FID and an NDIR, respectively. The production of hydrogen during the combustion process was also evaluated using a mass spectrometer. Engine tests were conducted at a baseline load level of 10 bar IMEP in the PPCI and RCCI modes. Load extension tests, up to 17 bar IMEP, were then conducted in the RCCI mode by increasing the gasoline-to-diesel fuel ratio. Test results indicated that CO, H2, and light 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".