Effect of Fueling Control Parameters on Combustion and Emissions Characteristics of Diesel-Ignited Methane Dual-Fuel Combustion
Bibliographic record
Abstract
Diesel-ignited dual-fuel (DIDF) combustion of natural gas (NG) is a promising strategy to progress the application of NG as a commercially viable compression ignition engine fuel. Port injection of gaseous NG applied in tandem with direct injection of liquid diesel fuel as an ignition source permits a high level of control over cylinder charge preparation, and therefore combustion. Across the broad spectrum of possible combustion conditions in DIDF operation, different fundamental mechanisms are expected to dominate the fuel conversion process. Previous investigations have advanced the understanding of which combustion mechanisms are likely present under certain sets of conditions, permitting the successful modeling of DIDF combustion for particular operating modes. A broader understanding of the transitions between different combustion modes across the spectrum of DIDF warrants further effort. In this investigation, a 2-litre single-cylinder research engine was operated in a variety of DIDF modes achieved through broad sweeps of fueling parameters. Through variations in the relative NG and diesel quantities, characterized using the global equivalence ratio (φglobal) and pilot fuel ratio (Rpilot), a DIDF operating space was mapped for one engine speed. At each operating mode, defined by φglobal and Rpilot, the timing and pressure of the diesel direct injection were varied. Through the identification of distinct and common features in apparent heat release rate (AHRR) profiles and engine-out emissions trends, regimes of the explored DIDF operation modes having common combustion mechanisms were identified and mapped in the Rpilot- φglobal operating space.
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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 | 0.001 |
| 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) | 0.001 | 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".