A novel in-cylinder fuel reformation approach to control HCCI engine combustion on-set
Bibliographic record
Abstract
Homogeneous Charge Compression Ignition (HCCI) engines have the potential to deliver high thermal efficiencies (when compared to spark ignition engines) coupled with ultra-low NOx emissions and Particulate Matter (PM) for partial-load operating regions. However, the inherent absence of Start of Combustion (SOC) or combustion on-set control has been a major obstacle for implementing this technology into production engines. In the present work, a new in-cylinder reformation strategy to control the on-set of combustion has been incorporated into a HCCI engine fuelled with lean ethanol/air mixtures. The objective of the in-cylinder reformation process is to generate hydrogen enriched gas (which includes other intermediate species) from ethanol reformation, which is then used to control the subsequent HCCI cycle combustion on-set. The experimental engine used for the study is a four-stroke, three cylinder In-Direct Injection (IDI) type compression ignition engine which was converted to single cylinder operation for HCCI combustion. A proto-type reformation chamber has been designed and fabricated with direct injection capabilities to examine the proposed in-cylinder reformation process. In order to clarify the effects of reformation products on HCCI combustion on-set, experiments were conducted with constant engine speed, initial charge temperature, and engine coolant temperature. The engine performance was evaluated based on cycle-resolved in-cylinder pressure measurements and regulated engine-out emissions. The experimental results demonstrate that the proposed in-cylinder reformation strategy is an effective method for controlling HCCI combustion on-set (SOC) and reduces the regulated engine-out emissions. Furthermore, the experimental results indicate that there is an optimal in-cylinder reformation fuelling percentage which will have a positive impact on regular HCCI combustion at given operating conditions.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".