A numerical and experimental study of the squish-jet combustion chamber
Bibliographic record
Abstract
The fluid flow characteristics near top dead centre (TDC) were numerically simulated for three different combustion chambers designed to generate squish flow and enhance turbulence generation in spark ignition engines. One of the combustion chambers was a plain bowl-in-piston type while the remaining two were different configurations of the squish-jet chamber, which has a unique geometry for forming jets that converge radially inwards as TDC is approached. The computational fluid dynamics (CFD) code KIVA-3V was used to produce the simulations and particular attention was given to mean velocities and turbulent fluctuations near TDC. The flow fields were measured experimentally (with particle image velocimetry (PIV) and laser Doppler velocimetry (LDV)) in a unique rapid-intake and compression machine, and compared with the output from the KIVA-3V code. The use of the rapid-intake and compression machine enabled the initial conditions to match exactly those in the KIVA-3V calculations, thus reducing uncertainty in the validation study. The study led to a greater understanding of the flow processes inside these complex combustion chambers and the results showed that squish-jet chambers tend to generate higher peak turbulence levels than do plain bowl-in-piston chambers, even though they may generate lower mean squish velocities. From this perspective, the KIVA code can be a useful tool for designing squish-jet combustion chambers. The study also showed that KIVA-3V predicted downstream squish jet velocities well. Nevertheless, the squish-jet velocities at the piston bowl rim were overestimated and the specific turbulent kinetic energy ( k) and its dissipation ( e) appear to have been overestimated during much of the compression period tested.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".