Characterization of Knocking on Spark Assisted Compression Ignition Mode in a Rapid Compression Machine
Bibliographic record
Abstract
Abstract Spark-assisted compression ignition (SACI) employs a direct spark event to control the combustion phasing of homogeneously charged compression ignition mode. However, the induced deflagration process results in elevated pressure and temperature within the unburned gas zone, leading to the end gas autoignition. In addition, the rapid heat release in the chamber potentially leads to the internal damage caused by conventional knocking, heavy knocking, or detonation knocking. This represents the primary challenge to improving power density and thermal efficiency under a high compression ratio and boosted engine. This study investigates knocking combustions and characterizes the oscillating pressure waves from the end gas autoignition using the DME/air mixtures in a rapid compression machine. The circumferential frequency of the vibration modes is computed with respect to the combustion chamber (Ø50mm). The first (11.5kHz) circumferential mode is the primary focus to explore the impact of end-gas autoignition on the individual wave intensity since they contain the majority of the acoustic energy from the decomposed harmonic waves. A detailed analysis of the ringing intensity, knock intensity, and duration of ratio on excitation to residence are performed to characterize the knocking behaviors under the HCCI and SACI combustion modes. The high-speed images are utilized to observe the end-gas autoignition onset timing and process, and identify transition of the fast flame propagation to detonation which exceeds 2km/s. The knocking suppression is also investigated by increasing the excessive air-fuel ratio up to lambda 2.
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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 | 0.000 | 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".