Fuel Stratification and Partially Premixed Combustion With Neat N-Butanol in a Compression Ignition Engine
Bibliographic record
Abstract
Homogeneous Charge Compression Ignition (HCCI) has been considered as an ideal combustion mode for compression ignition engines due to its superb thermal efficiency and low emissions of nitrogen oxides (NOx) and particulate matter (PM). However, a challenge that limits practical applications of HCCI is the lack of control over the combustion rate, which either deteriorates thermal efficiency at low engine load, or produces excessive pressure rise rate and combustion noise at high engine load. Fuel stratification and partially premixed combustion (PPC) have considerably improved the control over the heat release profile with modulations of the ratio between premixed fuel and directly injected fuel, as well as injection timing for ignition initiation. It leverages the advantages of both conventional direct injection compression ignition and HCCI. Compared with those of HCCI, the ignition ability and combustion efficiency of PPC are significantly enhanced at low engine load, and the low emissions of NOx and PM are maintained with lower pressure rise rate. In this study, neat n-butanol is employed to generate the fuel stratification and partially premixed combustion in a single cylinder compression ignition engine. A fuel such as n-butanol can provide additional benefits of even lower emissions, and can potentially lead to a reduced carbon footprint and improved energy security if produced appropriately from biomass sources. Intake port fuel injection (PFI) of neat n-butanol is used for the delivery of the premixed fuel, while the direct injection (DI) of neat n-butanol is applied to generate the fuel stratification. Effects of PFI-DI fuel ratio, DI timing, and intake pressure, on the combustion, are studied in detail. Different conditions are identified at which clean and efficient combustion can be achieved at a baseline load of 6 bar IMEP. An extended load of 14 bar IMEP is demonstrated using stratified combustion with combustion phasing control.
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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".