The Squish-Jet Combustion Chamber for Ultra-Lean Burn Natural Gas Engines
Bibliographic record
Abstract
Operators of natural gas engines, used for both mobile and stationary applications, are increasingly looking at running these engines under very lean air-fuel ratios in order to reduce exhaust emissions and increase thermal efficiency. Lean operation of homogeneous-charge spark-ignited engines reduces peak combustion temperatures, thereby reducing NOx emissions. Lean operation is normally restricted, however, by the “lean-limit” of combustion, as measured by the air-fuel ratio above which ignition is impossible, or combustion is incomplete. Operation under lean conditions also reduces the mixture burning rate, which can lead to increased spark advance and lower thermal efficiency. In order to increase the burning rate under ultra-lean air-fuel ratios a new “Squish-Jet” combustion chamber concept has been developed. This technique incorporates a series of passages in the crown of a bowl-in-piston type of combustion chamber which generates increased levels of small-scale turbulence just before ignition and during the early phase of combustion. This increased burning rate enables the engine to operate with a smaller spark advance under lean conditions, thereby extending the lean-limit of operation and increasing the thermal efficiency. The additional small-scale turbulence levels generated with the squish-jet type of combustion chamber is also effective in improving the completeness of combustion, thereby reducing unburned hydrocarbon emissions. This paper presents the results of a series of tests of the squish-jet combustion chamber design in a single-cylinder Ricardo Hydra research engine. The engine was run at three different engine speeds and under both naturally aspirated conditions and a boost pressure of 1.75 bar manifold absolute pressure. Two different squish-jet piston geometries were tested, together with a conventional bowl-in-piston design for comparison. The squish-jet combustion chambers were found generally to reduce emissions under ultra-lean operating conditions.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".