Aerodynamic shape optimization in transonic conditions through parametric model embedding
Why this work is in the frame
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Bibliographic record
Abstract
The paper presents a novel approach for aerodynamic shape optimization problems using the parametric model embedding (PME) method. PME reduces the design-space dimensionality while maintaining a connection to the original design parameters, addressing the curse of dimensionality. The optimization of an airfoil's drag in transonic conditions demonstrates the method, using the RAE-2822 airfoil at Mach 0.734 and a Reynolds number of 6.5 million. Employing the covariance matrix adaptation evolution strategy, the process is performed with 1,000 function evaluations in both original and PME-reduced design spaces. Moreover, statistical criteria based on advanced risk function are introduced to characterize and study the evolution of the optimization process. Results show that PME effectively retains essential design space characteristics, capturing at least 95% of the geometric variance associated with the original design space. This leads to significant aerodynamic improvements, including reduced drag and smoother pressure distributions. Additionally, the statistical analysis helps to understand the advantages and disadvantages of different levels of parameter space compression. • PME is proposed for aerodynamic shape optimization and its effectiveness is demonstrated to improve RAE-2822 performance. • PME does not require changes in the computation chain allowing the reconstruction of the original parameterization. • Statistical criteria based on advanced risk functions are introduced to study the evolution of the optimization process. • Statistical analysis helped to understand the advantages and disadvantages of different levels of parameter space reduction. • The computational chain used is entirely based on open-source component.
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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 | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it