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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".