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CST-GANs: A Generative Adversarial Network Based on CST Parameterization for the Generation of Smooth Airfoils

Experiments show that CST-GANs is a promising model, which can not only generate smoother airfoils with fewer neural network parameters but also generate more diverseAirfoils.
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Aerodynamic shape optimization using a physics-informed hot-start method combined with modified metric-based proper orthogonal decomposition

Aerodynamic shape optimization based on computational fluid dynamics still has a huge demand for improvement in the optimization effect and efficiency when optimizing the unstable flow of airfoils.…
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Data-Driven Sensitivity Analysis of the Influence of Geometric Parameterized Variables on Flow Fields Under Different Design Spaces

In aerodynamic shape optimization, geometric parameterized variables have a significant impact on the flow field, thereby influencing both the effectiveness and efficiency of the optimization…
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Aerodynamic shape optimization using design-variables-screening method

Aerodynamic shape optimization involving a complex geometric model or problem may have tens or hundreds of design variables, necessitating multiple accurate but time-consuming computational fluid…
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A hierarchical optimization strategy based on cluster characteristics for aerodynamic shape optimization

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A data-driven metric-based proper orthogonal decomposition method with Shapley Additive Explanations for aerodynamic shape inverse design optimization

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Impact of Geometric Forms on the Effectiveness and Physical Features of POD-based Geometric Parameterization

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