A FEniCS-based model for prediction of boundary layer transition in low-speed aerodynamic flows
Bibliographic record
Abstract
In the paper, a finite element model is developed that predicts boundary layer transition in low-speed aerodynamic flows. The model is based on a Reynolds-averaged Navier-Stokes approach, where the incompressible form of the Navier-Stokes equations is solved together with a three-equation eddy-viscosity model utilizing the FEniCS framework. A least-square stabilized Galerkin method is employed in order to prevent numerical oscillations that can arise from dominant advection terms. The proposed FEniCS model is ideal for applications with complex geometries and is tested on high performance computing platforms for parallel processing. The FEniCS model is validated by comparing the skin friction coefficient as well as profiles of velocity and total fluctuation kinetic energy with the benchmark experimental data for transitional boundary layers on a flat plate. The validity of the solver is further examined using experimental measurements reported for a NLF(1)-0416 natural laminar flow airfoil at different angles of attack. The airfoil results are also compared with those obtained using XFOIL, a well-known tool for the design of two-dimensional airfoils. These comparisons suggest that the proposed FEniCS-based model can effectively simulate aerodynamic flow fields that involve laminar-to-turbulent transition.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".