A Computational Study of the Effect of Fuel Reforming, EGR and Initial Temperature on Lean Ethanol HCCI Combustion
Bibliographic record
Abstract
Homogeneous charge compression ignition (HCCI) engines have great potential in ultra-low NOx emissions, high efficiency and low particulates. The major disadvantage of HCCI lies in a narrow operating range with low power output. We investigated the expansion of the acceptable operating range (AOR) using fuel reforming complemented by exhaust gas recirculation (EGR), to control the chemical kinetics which dominates HCCI combustion. The study is carried out using a single-zone well-stirred reactor model and established reaction mechanisms. The HCCI engine is fueled with ethanol of equivalence ratio (Ф) of 0.2, 0.4 and 0.5. The (AOR) must meet both the complete combustion and the maximum NOx limit. It is found that reforming enhances combustion and extends the complete combustion limit to lower initial temperatures, but also increases NOx emissions. For Ф's of 0.5 and 0.4, the NOx limit cannot be met without the complementary use of EGR to lower the NOx emission. It is found that reforming is not as effective as EGR in widening the operating range at the Ф's studied. However, reforming may still be useful in HCCI combustion, since hydrogen is reported by others to lower cycle-to-cycle variation [1].
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".