Fuel Chemistry Impacts on Gasoline HCCI Combustion with Negative Valve Overlap and Direct Injection
Why this work is in the frame
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Bibliographic record
Abstract
<div class="htmlview paragraph">Homogeneous Charge Compression Ignition (HCCI) combustion has the potential to produce low NOx and low particulate matter (PM) emissions while providing high efficiency. In HCCI combustion, the start of auto-ignition of premixed fuel and air depends on temperature, pressure, concentration history during the compression stroke, and the unique reaction kinetics of the fuel/air mixture. For these reasons, the choice of fuel has a significant impact on both engine design and control strategies. In this paper, ten (10) gasoline-like testing fuels, statistically representative of blends of four blending streams that spanned the ranges of selected fuel properties, were tested in a single cylinder engine equipped with a hydraulic variable valve train (VVT) and gasoline direct injection (GDI) system. By using VVT and GDI, re-compression early injection (RCEI) HCCI combustion was implemented, in which exhaust valve closes much earlier than normal valve timing to trap burnt gases into the combustion chamber to promote auto-ignition of fuel at the end of compression stroke. In the study, fuel chemistry impacts on HCCI combustion are investigated at three steady-state points with 50% mass fraction burned location (CA50) fixed at 5 degrees after top dead center (ATDC). A stepwise multiple regression method, in which fuel properties are correlated with combustion and emissions, is employed to understand the fuel effects on HCCI combustion. Results demonstrate that fuel sensitivity, distillation temperature T10, and percentages of i-paraffins, n-paraffins, and aromatics strongly affect engine emissions (HC, CO, NOx), fuel consumption, and combustion stability.</div>
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it