Numerical and Experimental Characterization of a Natural Gas Engine With Partially Stratified Charge Spark Ignition
Bibliographic record
Abstract
This paper outlines the development of a comprehensive numerical framework for the partially stratified charge (PSC) lean-burn natural gas engine. A 3D model of the engine was implemented to represent fluid motion and combustion. The spark ignition model was based on the works of Herweg and Maly (1992, “A Fundamental Model for Flame Kernel Formation in SI Engines,” SAE Technical Publication, Paper No. 922243) and Tan and Reitz (2006, “An Ignition and Combustion Model Based on the Level-Set Method for Spark Ignition Engine Multidimensional Modeling,” Combust. Flame, 145, pp. 1–15). The EDC model (Ertesvåg and Magnussen, 2000, “The Eddy Dissipation Turbulence Energy Cascade Model,” Combust. Sci. Technol., 159, pp. 213–235) with a two-step mechanism was used to model natural gas turbulent combustion process. An open geometry simulation strategy was adopted to account for intake-exhaust gas and valve movements. Each simulation was executed for multiple cycles to produce a representative residual gas fraction. The numerical results were compared with the experimental data obtained on the Ricardo Hydra single cylinder research engine for both homogeneous and PSC cases and they were found to be in excellent agreement in pressure trace and heat release rate. The detailed investigation of the numerical data showed the development of an ignitable mixture under PSC cases, allowing stable kernel growth well beyond the lean misfire limit of the bulk mixture. Furthermore, limits on successful ignition can be identified using the ignition model, which exhibited self-similar behavior in terms of flame speed and turbulent fluctuation. It can also be shown that, at ultralean air-fuel ratios, the PSC plume helps replicate the ignition conditions that can be found under stoichiometric operation.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".