Effect of Flow Speed and Turbulence on Methane Combustion in a Rapid Compression Machine
Bibliographic record
Abstract
Recent experimental work from the authors’ laboratory demonstrated that applying a boosted current ignition strategy under intensified flow conditions can significantly reduce combustion duration in a rapid compression machine (RCM). However, that study relied on spark anemometry, which provided only localized flow speed estimates and lacked full spatial resolution of velocity and turbulence near the spark gap. Additionally, the influence of turbulence on combustion behavior and performance across varying flow speeds and excess air ratios using a conventional transistor-controlled ignition (TCI) system was not thoroughly analyzed. In this study, non-reactive CFD simulations were used to estimate local flow and turbulent velocities near the spark gap for piston speeds ranging from 1.2 to 9.7 m/s. Simulated local velocities ranged from 0.7 to 96 m/s and were used to interpret experimentally observed combustion behavior under three excess air ratios (λ = 1.0, 1.4, and 1.6). Combustion was analyzed using pressure-based normalized cumulative heat release (NCHR) durations and high-speed shadowgraph imaging. At stoichiometric conditions (λ = 1.0), combustion duration decreased by over 70% with increasing flow speed, with optimal behavior observed between 33 and 72 m/s. At 96 m/s, durations increased again due to early spark kernel displacement and greater convective losses. For λ = 1.4, the shortest durations occurred near 23 m/s, corresponding to an 87% reduction in flame initiation time. At higher flow speeds, ignition consistency declined, with complete misfires at 72 m/s. For ultra-lean mixtures (λ = 1.6), stable combustion was only observed at low flow speeds (≤ 9 m/s); beyond this, ignition failed entirely due to heat loss and limited mixture reactivity. Shadowgraph imaging confirmed that larger, faster-growing flame kernels formed at optimal flow speeds, correlating with shorter combustion durations and higher peak pressures. At excessive flow intensities, however, early flame kernel disruption and elevated convective losses led to slower combustion or complete misfire.
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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 | 0.001 |
| 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".