Fuel Burn Rate Control to Improve Load Capability of Neat n-Butanol Combustion in a Modern Diesel Engine
Bibliographic record
Abstract
This research work investigates the control strategies of fuel burn rate of neat n-butanol combustion to improve the engine load capability. Engine tests of homogeneous charge compression ignition (HCCI) and partially premixed combustion (PPC) with neat n-butanol show promising NOx and smoke emissions; however, the rapid burn rate of n-butanol results in excessive pressure rise rates and limits the engine load capability. A multi-event combustion strategy is developed to modulate the fuel burn rate of the combustion cycle and thus to reduce the otherwise high pressure rise rates at higher engine load levels. In the multi-event combustion strategy, the first combustion event is produced near TDC by the compression ignition of the port injected butanol that resembles the HCCI combustion; the second combustion event occurs near 7~12 degrees after TDC, which is produced by butanol direct injection (DI) after the first HCCI-like combustion event. A third combustion event can also be enabled, via post DI, to further top up the engine load as needed. As demonstrated by the test results, the peak pressure rise rates can be curbed by controlling the amount of fuel burn during the first combustion event. Due to the temperature and pressure increases from the first HCCI-like combustion event, the ignition delay of DI butanol is significantly shortened, and the fuel burn rate is well controlled similar to that of conventional diesel diffusion burning. As one of the advantages of an oxygenated fuel, smoke emissions are generally low even when EGR is applied to reduce NOx emissions. By applying the multi-event combustion strategy, the maximum pressure rise rates can be reduced from over 20 bar/°CA in HCCI and PPC to 5~7 bar/°CA that is in the preferred range for modern production diesel engines. The engine load capability is thereafter improved from ~7 bar IMEP to over 14 bar IMEP while the pressure rise rate remains below the practical limits.
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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".