Laminar–Turbulent Transition Prediction on Industrial Computational Fluid Dynamics Applications
Bibliographic record
Abstract
This paper presents Ansys Fluent laminar–turbulent transition results using the shear stress transport [Formula: see text] model applied to the workshop cases of the First American Institute of Aeronautics and Astronautics Computational Fluid Dynamics (CFD) Transition Modeling Prediction Workshop. The key objectives of this workshop were to assess the current state-of-the-art laminar–turbulent transition models in an industrial Computational Fluid Dynamics environment and to determine and document the best practices to simulate laminar–turbulent transition flows. Sensitivity of the shear stress transport [Formula: see text] model to mesh refinement was established on a zero-pressure-gradient flat plate. Two other cases [a two-dimensional natural laminar flow (NLF) (1)-0416F airfoil, and a scaled Common Research Model (CRM)-NLF aircraft model] were selected as validation cases using a hierarchy of structured and unstructured meshes. Due to the complexity of the geometry and the airflow around the Common Research Model (CRM)- Natural laminar Flow (NLF) aircraft model, mesh adaptation cycles were also conducted to capture the shock, the wake, and the wing-tip vortices produced by the CRM-NLF. The accuracy of the [Formula: see text] model is evaluated using transition location measurements obtained with temperature-sensitive paint, pressure coefficient distributions at multiple wingspan stations, and aerodynamic coefficients at numerous angles of attack. The outcome of these comparisons will provide guidelines to conduct laminar–turbulent transition simulations with the [Formula: see text] model on simple and complex aerospace designs.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".