Combustion control of DME HCCI using charge dilution and spark assistance
Bibliographic record
Abstract
To realize the potential of DME for clean combustion, fueling control is essential. In this research, the challenges, advantages, and applicability of high-pressure direct injection and low-pressure port injection are reviewed and evaluated, especially in relevance to HCCI combustion. In this study, emphasis is given to the applicable ranges of low-pressure fuel delivery in relevance to load, air-fuel ratio, and inert gas dilution, for realizing HCCI combustion. The strategy of high-pressure direct injection is advantageous for combustion phasing control, but the fuel handling is challenging because of the high vapor pressure of DME fuel. The strategy of port fuel injection is prone to early combustion and, consequently, tends to produce excessive pressure rise rates in the combustion chamber. This challenge is escalated at higher engine loads, making homogenous charge compression ignition difficult to achieve. In this paper, the load extension of DME-fueled HCCI combustion was explored. First, the impact of dilution on the combustion characteristics of DME HCCI was studied under lean and CO 2 diluted conditions. Under the present empirical setups, results show that the lean-burn strategy has limited capability of combustion phasing control, especially when the engine load is above 5 bar IMEP. The CO 2 dilution strategy can significantly retard the combustion phasing until the fulfillment of combustion becomes unstable. It was found that spark assistance is advantageous for combustion control. With an effective application of excess air, intake CO 2 dilution and spark assistance, an engine load of 8 bar IMEP was reached with appropriate combustion phasing, with ultra-low NO x emissions.
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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 | 0.002 | 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 | 0.001 | 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".