A Comprehensive Numerical Study of Diesel Fuel Spray Formation with OpenFOAM
Bibliographic record
Abstract
The accuracy and robustness of spray models and their implementation in current commercial CFD codes vary substantially. However, common features are that the resulting spray penetration and levels of spray-generated turbulence - two factors that strongly influence the rate of heat released during combustion - are to a great extent grid size-dependent. In the work presented here a new kind of spray model has been implemented and thoroughly tested, under various ambient conditions, in the open source code OpenFOAM. In addition, since the turbulence model applied in simulations is known to strongly affect spray penetration rates, results obtained using both the standard k-ε and RNG k-ε models have been compared. In the new spray model, designated VSB2, the traditional Lagrangian parcel has been replaced by a so-called stochastic blob containing droplets with a distribution of sizes, rather than a number of uniform-sized droplets. These blobs do not interact with the grid directly, but through bubbles of locally determined size. One advantage of the VSB2 spray model is its less grid size dependence due to the interaction with the gas phase in the bubbles instead of the entire grid cell it currently occupies. Another benefit of the model is its robustness, resulting from rigorous calculation of equilibrium values for momentum and thermodynamic (saturation and temperature) parameters affecting transfer rates between the phases ensuring a bounded solution. Results obtained using the code are compared here with experimentally acquired data regarding spray penetration under various ambient conditions. Qualitative comparisons of the evolving spray shapes are also presented.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".