Large eddy simulation of a double-injection cycle and the impact of the needle motion on the sac-volume flow characteristics of a single-orifice diesel injector
Bibliographic record
Abstract
Study of the flow within diesel injector has gained in importance over the years as several authors have reported that the injector flow in the sac volume highly influenced the nozzle-flow characteristics at low lifts. Few studies have, however, characterized the sac volume and its dynamics. Thus, this paper reports on a numerical characterization of the needle displacement effects (static vs dynamic) on the internal flow of a sac-volume, single-hole diesel injector. To this end, a transient double-injection “closing-opening-closing-opening” cycle was simulated with a monophasic incompressible CFD model in combination with a moving mesh strategy to capture axial needle displacement. A large eddy simulation (LES) approach was chosen to gain better insight into the complexity of this unsteady turbulent flow. The emphasis of the paper is on the dynamic effects of needle movement on the sac flow, while static needle LES results are also shown to illustrate differences. The main findings reported herein are that the dynamic model shows a hysteresis effect associated with the needle motion between opening/closing phases. Quantitatively, the transient needle movement caused a difference in mass flow rate between the sac entrance and exit that was found to reach a maximum of [Formula: see text]. The hysteresis effect was found to be more pronounced at low needle lifts; both static and dynamic models seem to have performed similarly at very high needle lifts. Qualitatively, the LES sac-volume flow representations revealed a fuel jet attachment/detachment with needle movement, while static partial-lift simulations always predict an attached fuel jet. Further analysis of sac vortex dynamics revealed a high-energy vortex-structure breakdown just before the nozzle entrance that could help explain the higher turbulence production reported in the literature, both experimentally and numerically, at low needle lifts.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| 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".