Ignition and Combustion Improvement Via Ignition Modulation under Flow Conditions
Bibliographic record
Abstract
In order to reduce the environmental impact of transportation, the adoption of low and zero carbon fuel is needed to reduce the greenhouse gas emissions from engines, both from tailpipe and well-to-wheel perspectives. However, for some of the promising fuels, such as renewable natural gas and ammonia, the relatively low chemical reactivity and laminar flame speed bring challenge to a rapid and efficient combustion process, especially under lean or diluted conditions to suppress NOx emissions, leading to reduced combustion and thermal efficiencies. To tackle the challenge, high in-cylinder flow speed is needed to shorten the combustion duration, together with strong ignition sources to support the initial flame kernel development. In this paper, an ignition energy modulation system is developed to enhance both discharge current and discharge energy of a spark event to secure the ignition process. Moreover, a rapid compression machine is employed to compress the fuel-air mixture to the elevated pressure and temperature conditions before the ignition process. The cylinder head of the rapid compression machine is designed to generate various flow speed across the spark gap, and the crossflow speed can be adjusted from 3 m/s to 40 m/s under same mixture density and temperatures, which covers the application from low speed to high speed engines. The impact of discharge current amplitude of the spark event on the ignition and combustion characteristics will be discussed in detail, under various excess air ratios, flow speeds, and mixture densities. Ignition timing, discharge current amplitude and duration can be precisely controlled via FPGA system in microsecond order, and both in-cylinder pressure and high speed shadowgraph imaging system will be used to observe and analysis the combustion process. The aim of the study is to identify the efficacy of enhanced flow speed and ignition on the combustion efficiency lean fuel air mixture, respectively, to provide support for the research and development of future low carbon powertrains.
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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 | 0.001 |
| 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".