Pyrometric imaging of soot processes in a pilot ignited direct injected natural gas engine
Bibliographic record
Abstract
This work assesses the soot formation and oxidation processes in a high-pressure direct injection natural gas engine using simultaneous high-speed two-colour pyrometry and OH* chemiluminescence imaging. A parametric investigation considers the effects of fuel injection pressure, injection duration and relative pilot and natural gas injection timings. A typical combustion event consists of natural gas being ignited by diesel pilot combustion products, followed by partially premixed combustion of natural gas near the piston bowl wall. Soot is formed as natural gas is injected into this reaction zone. A toroidal soot cloud forms and grows towards the centre of the piston bowl. For the range of operating conditions tested, the peak apparent heat release rate and the onset of detectable soot were correlated, as were the timing of the peak soot fraction and end of natural gas injection. The latter indicates that the soot formation and transition to a net oxidation process are strongly influenced by the injection process, similar to diesel engines. Changes in the relative timing of the diesel pilot and natural gas injections influenced natural gas premixing times, with increased premixing leading to a higher peak apparent heat release rate and lower peak soot fraction. The injection pressure affected the peak soot fraction though enhanced oxidation was expected to ultimately reduce the engine-out soot for higher injection pressures. Based on this parametric investigation, an existing conceptual model of pilot ignited direct injected natural gas combustion is extended to also describe the soot formation and oxidation processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".