Flow Characteristics of a Gas-Blast Fuel Injector for Direct-Injection Compression-Ignition Engines
Bibliographic record
Abstract
Natural gas has a high auto-ignition temperature, therefore natural gas engines use sparks, hot surfaces or separate diesel pilot injects to promote ignition. For example, the high-pressure direction-injection (HPDI) system, available commercially for heavy-duty truck engines, uses a small diesel injection just prior to the main gas injection. A new type of HPDI injector has been developed that injections diesel and gas simultaneously through the same holes. In this paper the operation and flow characteristics of this “co-injector” will be discussed. An injection visualization chamber (IVC) was developed for optical characterization of injections into a chamber at pressures up to 80 bar. A fuel supply system was constructed for precise control of injector fueling and injection timing. Diesel and natural gas are replaced by VISCOR ® and nitrogen to study non-reacting flows. A novel feature of the IVC is a retracting shroud that allows the injector to reach steady-state prior to imaging, without fouling the IVC windows. Results indicate the mechanical delay of the developed injector is independent of chamber backpressure. However, for constant commanded injection duration, gas injection quantities are increased by higher chamber backpressure and decreased if the liquid mass injected is increased. These results are compared to theory using an AMESim model developed for an existing production injector. Changes in the gas/liquid ratio (as measured by the fluid supply system) are reflected in different jet image characteristics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".