On the Assessment of the Modified SSTCCM Turbulence Model to Predict Flow around 3D Delta Wing
Bibliographic record
Abstract
Various turbulence models have been developed to simulate aerodynamic flows.The eddy viscosity models (EVMs) are commonly implemented to close the Reynolds Averaged Navier-Stokes (RANS) equations, allow them to be the most popular choice for solving aerodynamic problems.EVMs are particularly valued for their robustness and computational efficiency.However, the objectives of achieving both robustness and accuracy in turbulence model development remain a formidable challenge.This study evaluates the performance of a recently developed EVM model in analysing external flows around the NACA0012 airfoil and a 3D Delta wing.Specifically, it examines the effectiveness of the Shear Stress Transport Model with Curvature Correction Modification (SSTCCM) in predicting the flow characteristics of external aerodynamic configurations.Earlier investigations have demonstrated the capability of the SSTCCM model to accurately predict confined swirling flows, such as those in cyclone separators, rotating lids, and sudden expansions.However, the model has yet to be tested in cases involving external flows, where aerodynamic geometry greatly affects the flow behaviour.This study investigates the ability of the SSTCCM model to numerically predict the behavior of external aerodynamic flows.The computational results are compared against experimental data and validated against other EVMs models.The findings show that the SSTCCM model offers a competitive alternative in computational efficiency and superior to conventional EVMs models in terms of accuracy.Conventional EVMs failed to predict lift and drag coefficients accurately, particularly near the stall angle of attack.Moreover, the SSTCCM model successfully captured the wing tip vortices in the 3D Delta wing simulations, highlighting its accurate predictive capabilities for complex flow features.
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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".