Bibliographic record
Abstract
Fluid flow characteristics near top dead centre (TDC) were measured in three different combustion chambers designed to generate squish flow and to enhance turbulence generation in internal combustion engines. One of the combustion chambers was a plain bowl-in-piston type, whereas the remaining two were different configurations of the squishjet chamber, which has a unique geometry for forming jets that converge radially inwards as TDC is approached. Both particle image velocimetry (PIV) and laser Doppler velocimetry (LDV) were used to measure mean velocities and turbulent fluctuations near to TDC. To accurately and consistently set the initial and boundary conditions, the University of British Columbia Rapid Intake and Compression Machine (UBC-RICM) was used. The microscopic particles used for PIV and LDV seeding were introduced into the cylinder by a novel system developed to suit the momentary flow in the UBC-RICM. The experimental study led to a greater understanding of the flow processes inside these complex combustion chambers. The results also indicated that squish-jet chambers tend to generate higher levels of turbulence than plain bowl-in-piston chambers do, even though they may generate lower mean squish velocities. These results will also be used in a future study to assess the validity of squish flow predictions made by the computational fluid dynamic code, KIVA-3V.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".