Characterization of Rate of Injection for Low Carbon Fuels in the Common-Rail Direct Injection System
Bibliographic record
Abstract
Internal combustion engines (ICEs) powered by liquid fuels remain the dominant powertrain system for heavy-duty road transportation, benefiting from the high energy density, ease of storage and transportation, and relatively low refueling pressure of traditional liquid hydrogen carbon fuel. However, concerns over tailpipe emissions and greenhouse gas (GHG) effects have driven the search for alternative fuels with lower carbon footprints. ICEs offer a key advantage during this transition, as they can operate on a variety of fuels, enabling a flexible approach to reducing harmful emissions and GHG while maintaining reliable power output. Alternative liquid fuels, such as dimethyl ether (DME), have shown great potential in mitigating environmental impacts while ensuring sufficient engine performance. However, the significantly different physical and chemical properties of renewable fuels necessitate the adoption of tailored combustion strategies to ensure optimal engine operation. In particular, the fuel injection strategy plays a pivotal role in governing the combustion process, as it directly influences fuel-air mixing, ignition characteristics, and hence, the combustion efficiency. Therefore, detailed characterization of the fuel injection process (rate of injection (ROI) profiles, injection delay, and injection quantities) is necessary for research and development of advanced combustion strategies. In this study, the ROI profiles for diesel, DME and polyoxymethylene dimethyl ethers (OME3) were measured using the Bosch long tube method, with diesel severed as the reference fuel. Comparative tests were conducted under varying injection pressures (300 bar to 900 bar) and injection durations (0.3 ms to 3 ms) to investigate the influence of fuel properties on ROI profiles. The results revealed that all three fuels exhibited comparable ROI at injection durations below 700 µs. However, at longer injection durations and higher pressures, significant differences emerged. At a 3 ms injection duration, DME consistently showed the lowest steady-state ROI, while OME3 exhibited the highest across all injection pressures. Furthermore, the discharge coefficient (Cd) increased with injection pressure and converged across three fuels at higher pressures. This indicated that, under those conditions, fuel density became the dominant factor influencing ROI, hence the injection quantities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".