Editorial: Advances in Compression Ignition Natural Gas–Diesel Dual-Fuel Engines
Bibliographic record
Abstract
Advances in Compression Ignition Natural Gas-Diesel Dual-Fuel EnginesNatural gas (NG) has been recognized as a low-carbon clean fuel that generates about 20-30% less carbon dioxide (CO 2 ) and much less particulate matter (PM) emissions than diesel.Replacing diesel by NG in compression ignition diesel engines helps reduce CO 2 and PM emissions.Dual-fuel technology is an effective way to replace diesel by NG in diesel engines.However, there are some technical issues that are limiting the wide application of NG-diesel dual-fuel engines.These include the limited replacement ratio, efficiency deterioration, and especially the low combustion efficiency of NG due to the incomplete combustion of methane.Emissions of unburned methane are also an issue because of its very high global warming potential.This research topic gathers contributions that highlight the advantages/disadvantages and address the existing issues of NG-diesel dual-fuel engines.The first article (Boretti) reviews the advantages and disadvantages of compression ignition dualfuel engines compared to those of diesel engines.After a comprehensive literature review, the article indicates that dual-fuel engines allow for comparable or better performances of diesel-only internal combustion engines in terms of steady-state torque, power, and fuel conversion efficiency while dramatically improving the CO 2 and engine-out PM and NOx emissions.Further development of the fuel injection system for the second fuel may lead to novel dual-fuel engine designs with better performance.The article by Yoshimoto et al. presents an investigation on trade-off improvements by combining exhaust gas recirculation (EGR) and supercharging in a dual-fuel engine using next-generation bioalcohol-blended FAME as ignition fuel.Instead of conventional diesel, Pongamia pinnata methyl ester, which has potential to further reduce CO 2 emissions, was used in the investigation.The infiuence of the ignition fuel on engine performance, combustion characteristics, and emissions is examined and compared for dual-fuel and diesel operation by combining supercharging and EGR in the article.The third article (Xu and Filipi) presents a quasi-dimensional multi-zone model of methane-diesel dual-fuel combustion.The model is further validated by experimental results.It provides a simplified but practical tool to predict and analyze the performance of dual-fuel engines.The study by Ichihashi et al. also develops and validates a control model that is able to predict the combustion efficiency for compression ignition dual-fuel engines, and can be used to construct a controller that outputs the diesel injection condition to maximize the combustion efficiency of NG.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 0.015 |
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".