Validation and Assessment of a Hybrid VOF-Lagrangian Numerical Methodology for Turbulent Liquid Fuel Jets in High-Speed Crossflow
Bibliographic record
Abstract
Abstract For gas turbine combustors, injecting liquid fuel in crossflow can achieve superior mixing characteristics to improve performance and reduce emissions. Robust numerical design tools are needed to accelerate the development of low-emissions technologies. Atomization modeling is often facilitated by using the Lagrangian approach rather than the more complex and computationally expensive Volume-of-Fluid (VOF) approach. However, most Lagrangian breakup models rely on empirical constants that must be fully calibrated for jets in crossflow and representative conditions. A hybrid approach could deliver a better compromise between accuracy and computational cost. This study proposes and validates a novel numerical methodology for coupling the VOF and Lagrangian approaches using a stochastic breakup model with Adaptive Mesh Refinements for turbulent liquid fuel jets in crossflow under more representative gas turbine conditions. The predictions were validated in the near and far-field regions using datasets at high pressures (1–8 bar), Weber numbers (720–1172), and momentum flux ratios (6–33). The predictive capabilities and computational cost were also compared to the Lagrangian approach coupled with large eddy simulations (LES) and with the unsteady Reynolds-Averaged Navier–Stokes (RANS) methodology previously developed and validated by the authors. The effect of different VOF-Lagrangian transition criteria on the computational cost was also assessed and recommendations were provided for further improvements. The overall LES predictions were significantly improved by the proposed hybrid methodology. Although it tends to underpredict the spray trajectory and Sauter Mean Diameter compared to the URANS methodology, it better captures the diameter in the wake region and the droplet velocities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".