Numerical studies of the ignition characteristics of a high-pressure gas jet in compression-ignition engines with glow plug ignition assist: Part 1—Operating condition study
Bibliographic record
Abstract
This article presents the results of computational studies investigating the ignition of high-pressure natural gas jets in a compression-ignition engine with glow plug ignition assist. The simulation was conducted using a KIVA-3V-based three-dimensional engine model, along with an improved fuel injector model, a detailed cut-off glow plug shield model and a modified two-step methane reaction mechanism, to simulate the natural gas injection and ignition. The simulated results demonstrate the significance of using a shield for the glow plug. Compared to an unshielded (bare) glow plug, the shield not only reduces the heat loss from the hot glow plug surface to the cold intake air charge and the cold injected gas jet but also traps the fuel mixture to increase its residence time adjacent to the hot surface. Over a representative range of heavy-duty diesel engine operating conditions, a shielded glow plug greatly improves the natural gas engine performance and provides reliable ignition, while an unshielded glow plug can only be optimized for specific conditions. The understanding of glow plug shield behavior gained from the simulations suggests avenues for improved shield designs that would yield further reduced ignition delays.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".