Optimizing Chaos: Aerodynamic Design using High-Fidelity Scale Resolving Simulations
Bibliographic record
Abstract
Current industry-standard aerodynamic shape optimization is performed using a combination of Reynolds Averaged Navier-Stokes (RANS) solvers and adjoint-based optimization. However, despite decades of development RANS is often deficient, particularly for separated and transitional flows. High-fidelity scale-resolving techniques, such as Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS), have been demonstrably more accurate in these flow regimes. Over the past decade enabling technologies for efficient high-fidelity simulations, including high-order spatial discretizations, temporal discretizations, and many-core hardware architectures, have significantly reduced their computational cost. However, significantly less attention has been dedicated to the development of suitable optimization frameworks for LES/DNS. This talk will focus on two recently proposed optimization frameworks for LES/DNS. The first is a gradient-based approach, which uses a combination of reduced order modelling, least squares shadowing, and the adjoint. The second is a gradient-free approach using Mesh Adaptive Direct Search (MADS). It will be demonstrated that both of these frameworks are suitable for the fundamental chaotic behavior of scale resolving simulations. The utility of these frameworks will then be demonstrated for general chaotic systems, aerodynamic optimization, and aeroacoustic optimization including low-pressure turbine cascades.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".