Effect of Hydrogen Addition on the End-Gas Autoignition Process Using a Rapid Compression Machine
Bibliographic record
Abstract
Abstract To alleviate environmental concern, the deployment of low to zero carbon fuel is necessary for reducing the greenhouse gas emissions from internal combustion engines (ICEs). Dimethyl ether (DME) and hydrogen fuel are viable alternative energy sources that can replace petroleum-based fossil fuels, offering emission reductions. In addition, dual-fuel spark-assisted compression ignition (SACI) is an advanced combustion mode that offers precise control of the combustion phasing, extends engine operating range, and improves thermal efficiency. However, engine knocks are generated from end gas autoignition, which restricts the expansion of the high load limit. This study explores the knocking resistance of SACI combustion using the various ratio of hydrogen addition with DME/air mixtures in a rapid compression machine. The low autoignition temperature of DME fuel enables the DME/air mixtures to be readily ignited at the elevated temperature and pressure. Hydrogen fuel with a high octane number can be utilized to reduce the reactivity of the air-fuel mixture for knocking suppression, and the fast laminar flame speed leads to reduce energy density during the end-gas autoignition. The volume ratio of hydrogen is prepared from 10 to 90%, in order to observe the onset of the end-gas autoignition limits. The raw pressure measurement is analyzed to investigate the impact of hydrogen addition on knocking and ringing intensity from the generated pressure waves. The high-speed images are utilized to define the autoignition time to further compute the energy release during the residence and excitation periods, and the correlation of different stages of energy release is compared to explore the knocking behavior. The deflagration to detonation transition (DDT) is observed and identified via the non-dimensional parameter π.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".