Exploiting turbulence to enhance spark-ignition engine performance: on premixed turbulent flame acceleration
Bibliographic record
Abstract
In a spark-ignition engine, the turbulent flame speed/turbulence intensity ratio typically increases as the flame grows. Four possible mechanisms behind this turbulent flame acceleration under moderate-turbulence conditions are presented.1.Incremental flame/eddy size. Only eddies smaller than the flame are involved in wrinkling the flame. As the flame grows from a spark kernel, the increasing flame/eddy size ratio advances flame wrinkling. This is most significant during the early flame-development period, and it levels off when the flame grows larger than the turbulent eddies.2.Expanding-pushing effect. An unburned element undergoing combustion reactions expands and pushes the adjacent reacting segment as it burns, amplifying the wrinkling of the flame front.3.Flame-turbulence evolution. Flame-turbulence interaction evolves with time, ushering in the augmentation of the turbulent flame speed.4.Darrieus-Landau hydrodynamic instability. Diverging and converging flow streams of unburned mixture through concave and convex reacting surfaces speed up and slow down local flame fronts, respectively, furthering flame corrugation.These mechanisms can be exploited to promote controllable fuel-lean, turbulent-flame propagation, furthering engine efficiency while simultaneously reducing combustion emissions.
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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) | 0.005 | 0.002 |
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".