A Deep-Learning Surrogate Model Approach for Optimization of Morphing Airfoils
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-1619.vid Analyzing and optimizing the aerodynamic performance of a morphing airfoil concept typically requires the numerical solution of many complex, computationally expensive fluid-structure interaction (FSI) problems. This approach becomes intractable against current developments in intelligent, programmable materials and additive manufacturing techniques, which drastically increase the design space and open novel opportunities for passively and actively morphing wings. To fully exploit these capabilities, a new paradigm for analyzing and optimizing aeroelastic structures in high-dimensional parameter spaces is required. This work presents an efficient numerical design approach for elastically morphing structures in aerodynamic flows. Our approach centers on using deep neural network surrogate models to predict the aerodynamic loading as a function of a given shape. The models are trained through a set of flow simulations around rigid stationary bodies randomly sampled from a parametrized design space of the shapes. Once trained, the surrogate model can be used to evaluate the aerodynamic performance of any structural design without the need for further costly flow or FSI simulations. Consequently, this approach can analyze and optimize airfoils within a higher-dimensional structure and structure-actuator problems than currently possible. Though the approach is general, we focus here on establishing a proof-of-concept of this idea for a 2D multi-hinged airfoil at a steady-state condition. The specific contributions are validating the surrogate model, estimating the cost benefits of this approach, and providing first insights into the approach's capabilities. A practical optimization of a 2D morphing airfoil in steady flows demonstrates that training and using the surrogate model reduces the number of required flow solutions by several orders of magnitude compared with a fully coupled FSI approach.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".