Bibliographic record
Abstract
The cyclic flow was simulated with a square cross-section optical water analog engine. A stereoscopic particle image velocimetry was constructed using two charge-coupled device (CCD) cameras to produce three component velocity vectors over a 17 mm by 20 mm area. Measurements were taken at nine different locations behind the upper-valve centerline. Engine speeds were at 15 and 20 revolutions per minutes (RPM). Three different valve configurations were studied using different combinations of two open valve lift (10 and 20 mm). Statistically significant numbers of image sets were obtained at a number of specific crank angles. The adaptive cross-correlation algorithm (Usera, 1999) was applied to extract velocity fields from captured images. Three different averaging methods were employed to decompose the instantaneous velocity into mean and turbulence components: ensemble, cyclic and wavelet-based averaging. The ensemble averaging does not consider the cycle-to-cycle mean variation as opposed to cyclic and wavelet averaging. The wavelet-based averaging better met the mean value based criteria introduced by Catania and Mittica (1990) than cyclic averaging. The large-scale vortex structure found during the intake stroke broke down at 180 CAD and disappeared during the exhaust stroke. The turbulence level was relatively high while the large-scale vortex structure was sustained in the process. Mean Reynolds stress and turbulence intensity showed that the out-of-plane component of turbulence was significantly higher than the in-plane components and significant turbulent energy transfer occurred between two in-plane components. A number of statistical quantities indicated large discrepancies in scales and distributions determined using different averaging schemes, which may lead to differing physical interpretations of the flow. The objective of the present study was to experimentally investigate the mixing characteristics of cyclic flow. The technical and analytical limitations from previous experiments were defined and their impacts on the interpretation of cyclic flow were discussed. Two shortcomings were identified from past experimental studies: (1) technical limitations because of incomplete measurement velocity components and insufficient measurement points, (2) analytical limitations because of inappropriate averaging of the turbulence. The current experimental study closes these gaps.
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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".