VALIDATION OF A STOCHASTIC BREAKUP MODEL FOR TURBULENT JETS IN HIGH-SPEED CROSSFLOW: ASSESSMENT OF TURBULENT INTERACTIONS AND SENSITIVITY TO BOUNDARY CONDITIONS
Bibliographic record
Abstract
Improving the mixing of fuel and air by injecting a turbulent liquid fuel jet into a high-speed cross-flowing gas can reduce the emissions of gas turbine applications. To facilitate and hasten the development of such low-emissions technologies, accurate predictions of the spray characteristics are needed. The objective of the present study is to validate the predictive capabilities of a stochastic breakup model for turbulent transverse jets over a wide range of representative pressures and atomization characteristics. The effect of turbulence modeling is also assessed to provide accurate and computationally less expensive Eulerian-Lagrangian transient approaches. To do so, the predictions made with the large eddy simulation (LES) approach for different subgrid-scale (SGS) models and with the synthetic eddy method (SEM) are compared to the ones made using the unsteady Reynolds-averaged Navier-Stokes (RANS) approach with and without a turbulent dispersion model. The sensitivity of the numerical methodology to the upstream velocity profile, pressure, and momentum flux ratio were also assessed. Properly accounting for the upstream gas velocity profile was found to be critical to ensure accurate predictions of the spray characteristics. The unsteady RANS (URANS) turbulent approach coupled with the turbulent dispersion model showed good agreement with experimental data, but the LES approach tends to overpredict the spray penetration and underpredict the Sauter mean diameter (SMD). This could be due to the lower turbulent interactions it predicts, which may lead to lower momentum transfer between the phases.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".