A Computational Study of the Effect of Fuel Reforming, EGR and Initial Temperature on Lean Ethanol HCCI Combustion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<div class="htmlview paragraph">Homogeneous charge compression ignition (HCCI) engines have great potential in ultra-low NO<sub>x</sub> 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 NO<sub>x</sub> limit. It is found that reforming enhances combustion and extends the complete combustion limit to lower initial temperatures, but also increases NO<sub>x</sub> emissions. For Ф's of 0.5 and 0.4, the NO<sub>x</sub> limit cannot be met without the complementary use of EGR to lower the NO<sub>x</sub> 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 [<span class="xref">1</span>].</div>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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