Bibliographic record
Abstract
The development of next-generation aircraft will rely on our ability to perform shape optimization using high-fidelity Computational Fluid Dynamics (CFD). Whereas previous low-fidelity solvers, such as the Reynolds Averaged Navier-Stokes (RANS) approach, are amenable to gradient based optimization, high-fidelity fidelity solvers, such as Large Eddy Simulation (LES), are not. LES resolves unsteady turbulent structures, resulting in chaotic divergence of the flow and objective functions when perturbed. As a result, classical sensitivity analysis via the adjoint or tangent methods is unconditionally unstable. In this presentation we demonstrate the utility of a gradient-free Mesh Adaptive Direct Search (MADS) approach for performing shape optimization with LES. We first demonstrate the suitability of MADS for optimizing model problems, specifically the Lorenz system. We then use MADS coupled with PyFR to perform shape optimization of an SD7003 airfoil and two independent optimizations of a T106D turbine cascade. Results demonstrate >20% performance improvement relative to reference designs for both cases. Importantly, these results are obtained within days via two independent levels of parallelism. This demonstrates that aerodynamic shape optimization using LES is now feasible, and that it can be used for practical aerospace geometries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".