Characterization of N-Butanol High Pressure Injection From Modern Common Rail Injection System
Bibliographic record
Abstract
Increasing attention has being paid to alternative fuels that have the potential to reduce overall greenhouse gas emissions and fossil fuel dependence. The alcohol fuel n-butanol, as one of the advanced biofuels, can be potentially utilized as a partial or complete substitute for the diesel fuel in diesel engines. Experimental results from literature, as well as from the authors’ previous research, have shown promising trend of low soot and nitrogen oxides emissions from the combustion with n-butanol high pressure direct injection. However, due to the significant fuel property differences between n-butanol and diesel, the fuel delivery mechanism and combustion control algorithm need to be optimized for n-butanol use. A better understanding of the high pressure n-butanol injection characteristics, such as the injector opening/closing delays and spray droplet sizes, can provide the guidance for the control optimization and insights to the empirical observations of engine combustion and emissions. Meanwhile, the experimental data could be used for the model development of the n-butanol high pressure fuel injection events. In this work, injection rate measurement, high-speed video direct imaging, and phase Doppler anemometry (PDA) analysis of neat n-butanol and diesel fuel have been conducted with a light-duty high pressure common-rail fuel injection system. The injection rate measurement was performed with an offline injection rate analyzer at 20 bar backpressure to obtain the key parameters of the injector opening/closing delays, and the instantaneous pressure rise. The spray direct imaging was carried out in a pressurized chamber, and the PDA measurement was conducted on a test bench at ambient temperature and pressure. The injector dynamics and spray behavior with respect to the different fuels, variation of injection pressures, and variation of injection durations are discussed.
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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".