Effects of Various Discharge Strategies on Ignition of Lean Methane/Air Mixture
Bibliographic record
Abstract
Ultra-lean burn with high turbulence has high potential for improving thermal efficiency and reducing NOx emissions in spark-ignition engines. Formation of initial flame kernel in high-turbulence flow by advanced ignition technologies is crucial for successful implementation of the ultra-lean burn concept. In this study, a four-coil ignition system is designed to enable temporally flexible discharge, including the single strike, multi-strike and continuous discharge with the discharge energy range from 100 to 300 mJ. The performance of the different discharge strategies on igniting the lean methane-air mixture is evaluated in an optically accessible constant volume vessel. The initial mixture pressure of 3.0 MPa and temperature of 388 K are set to simulate typical conditions near TDC (top dead center) of turbocharged large-bore natural gas engines. Both the flow and quiescent conditions around the spark plug are taken into account with and without gas flows in the vessel. The flame kernel formation and developing processes are captured by using the Schlieren imaging technique with a high-speed CMOS video camera, while evolution of both the voltage and current in the circuit are well monitored by the high-voltage probe and current clamp. With the continuous discharge ignition, the lean limit is remarkably extended in the case of the flow condition, while it is changed only slightly under the quiescent condition, compared with the other strategies. Analysis of the current and voltage waveforms shows that the continuous discharge strategy can enable a steadier and longer discharging period than the other strategies, regardless of conditions with and without gas flow. Besides, the continuous discharge strategy can accelerate the initial flame propagation compared with the other strategies. Once the flame kernel is successfully established, an increase in the discharge energy of single strike has no obvious effects on the flame development, but it is necessary for maintaining the lean limit. Although, in principle, the multi-strike discharge strategy can increase the ignition energy released to the mixture, the current waveform is prone to be interrupted with the discharge channel strongly distorted by the gas flow under the high-pressure condition. The flame propagation speed of the ultra-lean mixture is rather slow under the high ambient pressure quiescent condition compared with the high ambient pressure flow condition. Enhancement of turbulent flow in the mixture is very crucial for realizing the highly efficient and stable combustion of the lean mixture.
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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 | 0.000 |
| 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) | 0.000 | 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".