Modeling of Ignition and Combustion for Glow Plug Assisted Direct Injection Natural Gas Engines
Bibliographic record
Abstract
Direct injection natural gas (DING) engines offer the advantages of high thermal efficiency and high power output compared to spark ignition natural gas engines. Injected natural gas requires some form of ignition assist in order to ignite in the time available in a diesel engine combustion chamber. A glow plug — a heated surface — is one form of ignition assist. Simple experiments show that the thickness of the heat penetration layer of a glow plug is very small (≈10−5 m) within the time scale of the ignition preparation period (1–2 ms). Meanwhile, the theoretical analyses reveal that only a very thin layer of the surrounding gases (in micrometer scale) can be heated to high temperature to achieve spontaneous ignition. A discretized glow plug model and virtual gas sub-layer model have been developed for CFD modeling of glow plug ignition and combustion for DING diesel engines. In this paper, CFD modeling results are presented. The results were obtained using a KIVA3 code modified to include the above mentioned new developed models. Natural gas ignition over a bare glow plug was simulated. The results were validated against experiments. Simulation of natural gas ignition over a shielded glow plug was also carried out and the results illustrate the necessity of using a shield. This paper shows the success of the discretized glow plug model working together with the virtual gas sub-layer model for modeling glow plug assisted natural gas direct injection engines. The modeling can aid in the design of injection and ignition systems for glow plug assisted DING engines.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".