Finite element implementation of k−ω SST with automatic wall treatment and adjoint‐based mesh adaptation
Bibliographic record
Abstract
Summary This work presents a new methodology in finite element to simulate, according to a controlled precision on an engineering value, steady turbulent flows. First, we developed a new implementation of Reynolds‐averaged Navier‐Stokes equations combined with SST turbulence model and automatic wall treatment. Then, to simulate these complex multiscale flows, spatial discretization is critical. It is still common for expert users to generate meshes manually since they can roughly anticipate the physics of the flow. However, this remains a difficult task, especially for a neophyte. A recent mesh adaptation methodology based on an adjoint sensitivity analysis allows generating automatically appropriate meshes for analysis of steady laminar flows. Here, we extended this work to turbulent flows. The presentation is limited to two‐dimensional (2D) to demonstrate the effectiveness of the approach without getting unnecessarily entangled in the implementation details. The methodology is validated on the classic 2D zero pressure gradient flat plate verification case at Re = 5 · 106. Then, a more complex example is also presented: flow around multicomponent airfoil (30P30N, ) at Re = 9 · 106.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".