Isotropic and Anisotropic Mesh Adaptation for Rans Simulations of a Nacelle Under Crosswind Conditions
Bibliographic record
Abstract
Mesh adaptation of unstructured meshes for aerodynamic simulations, that typically resolve the Reynolds Averaged Navier-Stokes (RANS) equations, is a promising approach to enable high numerical precision on complex geometries. Its objective is to minimize the discretization error without using empirical meshing guidelines. The most common approach of mesh adaptation is the “feature-based” isotropic mesh adaptation: from an initial flow prediction on an isotropic unstructured mesh, a local error estimator is computed using a flow variable. It is then used to adapt the mesh using isotropic tetras. Additional near-wall resolution can be achieved by extracting prism layers from the walls. A more efficient approach is to use anisotropic mesh adaptation purely with tetras that are stretched to follow the flow’s preferential directions. In this work, we demonstrate the abilities of feature-based isotropic and anisotropic mesh adaptation on a complex flow phenomenon of importance for jet engines: flow separation in a nacelle under crosswind conditions. Two different solvers, adapted for either isotropic or anisotropic meshes, are employed. Results are compared with standard unstructured simulations with user-imposed mesh refinements and highlight the ability of mesh adaptation to automatically capture all the relevant flow phenomena without any user input and at reduced cell counts.
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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 | 0.000 |
| 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".