Large Eddy Simulation of Vaporizing Sprays Considering Multi-Injection Averaging and Grid-Convergent Mesh Resolution
Bibliographic record
Abstract
A state-of-the-art spray modeling methodology, recently presented by Senecal et al. (2012, “Grid Convergent Spray Models for Internal Combustion Engine CFD Simulations,” Proceedings of the ASME 2012 Internal Combustion Engine Division Fall Technical Conference, Vancouver, Canada, Paper No. ICEF2012-92043; 2013 “An Investigation of Grid Convergence for Spray Simulations using an LES Turbulence Model,” Paper No. SAE 2013-01-1083) is applied to large eddy simulations (LES) of vaporizing sprays. Simulations of noncombusting Spray A (n-dodecane fuel) from the engine combustion network are performed. An adaptive mesh refinement (AMR) cell size of 0.0625 mm is utilized based on the accuracy/runtime tradeoff demonstrated by Senecal et al. (2013, “An Investigation of Grid Convergence for Spray Simulations using an LES Turbulence Model,” Paper No. SAE 2013-01-1083). In that work, it was shown that grid convergence of key parameters for nonevaporating and evaporating sprays was achieved for cell sizes between 0.0625 and 0.125 mm using the dynamic structure LES model. The current work presents an extended and more thorough investigation of Spray A using multidimensional spray modeling and the dynamic structure LES model. Twenty different realizations are simulated by changing the random number seed used in the spray submodels. Multirealization (ensemble) averaging is shown to be necessary when comparing to local spray measurements of quantities such as mixture fraction and gas-phase velocity. Through a detailed analysis, recommendations are made regarding the minimum number of LES realizations required for accurate prediction of diesel sprays. Finally, the effect of a spray primary breakup model constant on the results is assessed.
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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 | 0.001 | 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".