Different Velocity Data Analysis for Flows Near a Spark Plug in the Combustion Chamber of a Spark Ignition Engine
Bibliographic record
Abstract
Flow near a spark plug is important for early flame kernel development (EFKD) and combustion efficiency. Velocity data have been measured by a laser Doppler velocimetry (LDV) for three different positions near a spark plug within a ported single cylinder optical spark ignition (SI) engine with a heart-shaped combustion chamber and a compression ratio of 8.9. LDV measurements have been performed under the wide-open motored conditions with an engine speed of 1,000 rpm conditions and maximum data collection rates of 22 kHz per channel. This work examines the mean and turbulence flow fields as interpreted through ensemble, cyclic, discrete wavelet transformation (DWT) analysis and the energy cascade as analyzed through continuous wavelet transformation (CWT) for flows near a spark plug. The results in this paper show that the frequency of the first level DWT can be used as a cutoff frequency for filtering because the first level DWT satisfies within 5 % relative error the condition that the ensemble average of the individual cycle mean should be same as the conventional ensemble-mean velocity, and it is near the maximum frequency in frequency spectrum of the ensemble averaged velocity as Liou et al. approached [10]. Wavelet analysis is appropriate for analyzing the flow fields in the combustion engine because it gives information about the transient events in a time and a scale plane. Large-scale high energy (defined below) found with CWT corresponds well with the peak locations of the mean velocity
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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 | 0.001 | 0.001 |
| 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".