A COMPREHENSIVE STUDY ON THE INFLUENCE OF RESOLVING AN INJECTOR ORIFICE AND THE INFLUENCE OF CREATING STRIPPED OFF DROPLETS ON SPRAY FORMATION USING THE VSB2 SPRAY MODEL
Bibliographic record
Abstract
With respect to simulating fuel sprays applied to direct injection engines, few studies in literature have investigated resolving the injector orifice and how this may influence spray predictions for high pressure and temperature diesel engine-like conditions. In this work, we used the stochastic blob and bubble (VSB2) spray model to conduct simulations in which fuel is injected into a constant volume combustion vessel. The injector orifice is resolved into nine cells. The boundary conditions were the same as the Engine Combustion Network (ECN) noncombusting case for n-dodecane Two simulation meshes were compared with experimental data: (1) injector orifice resolved and (2) injector orifice unresolved (grid cells in the orifice region equal orifice diameter). The resolved orifice mesh showed a liquid penetration length slightly higher and closer to experimental values. Spray predictions for an asymmetrical injection velocity was compared for both meshes. Finer structures near the leading edge of the spray (for mixture fraction and temperature fields) seen in the resolved mesh were missing in the unresolved mesh. Resolving the orifice requires a change in the core of the mesh, which also influences the results. The influence of creating new child blobs (liquid parcels are referred to as blobs in this work) stripped off from a parent blob by secondary breakup was also studied. The simulation results suggested that for high pressure and temperature diesel engine-like conditions, the influence of creating new child blobs is insignificant.
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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) | 0.001 | 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 | 0.000 | 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".