Bibliographic record
Abstract
A combined study based on laser Doppler velocimetry measurements and numerical simulation was undertaken in order to validate the KIVA II numerical code. A rapid intake and compression machine was used for the experimental studies, while the numerical calculations were performed with the KIVA II computational fluid dynamics code. The k-ॉ turbulence model was used to represent the effects of turbulence. The measured mean radial and tangential velocities were found to be generally higher than their computed counterparts. The differences between these velocities range from 3.5% to 7% in magnitude for the re-entrant bowl chamber while they vary from 5% to 11% for the bowl-in-piston combustion chamber configuration. The measured values of the turbulence intensity close to the bowl axis in the re-entrant bowl chamber configuration were fairly accurately predicted, but the quality of the prediction diminishes as the bowl entrance region was approached. The values of the turbulence intensity were however, poorly reproduced near the axis of the bowl-in-piston chamber assembly. The experimental and predicted values of turbulence were found to differ by between 7% to 20% (re-entrant chamber) and 13% to 20% (bowl-in-piston). The results of the experimental data that were obtained from this study show that the random uncertainties in the mean radial and tangential velocities for both chamber configurations range from ±13.2% to ±19.2%, while the uncertainties in the root mean square velocities were about ±14.1%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".