Numerical studies of the ignition characteristics of a high-pressure gas jet in compression ignition engines with glow plug ignition assist: Part 2-Effects of multi-opening glow plug shields
Bibliographic record
Abstract
The results of a previous study, part 1, showed that use of a shield can improve the thermal performance of a glow plug, and thereby reduce ignition time. However, the part 1 study also found that use of a simple shield with only one circular opening can delay flame propagation out of the shield. The conclusions of that study suggested that there is scope for further improvements of the shield design, especially the shield opening geometry. Accordingly, this article presents the results of computational studies investigating the influence of multi-opening shield designs on natural gas ignition characteristics in glow plug–assisted compression–ignition engines. Two types of multi-opening glow plug shield, consisting of four small circular openings distributed in either diamond-pattern or square-pattern arrangements, were employed. The simulated results demonstrated that both multi-opening shields can not only increase glow plug surface temperature, but also increase the residence time of fuel mixture adjacent to the glow plug surface in the early injection stage, resulting in a faster ignition than the single-opening shield. Furthermore, the diamond-pattern multi-opening glow plug shield provides a faster or comparable flame propagation path back to combustion chamber, compared to single-opening glow plug shield, while the square-pattern multi-opening glow plug shield delays the flame propagation under several specific engine conditions. Compared to the single-opening glow plug shield, the overall natural gas ignition delays are further reduced by 6%–44% when using the diamond-pattern multi-opening glow plug shield, while the square-pattern multi-opening glow plug shield is only able to reduce the natural gas ignition delay under a few specific 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".