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This model detects the geometric abnormality of airfoils or wing sections quickly without using expensive computational fluid dynamic models. In addition, we develop a discriminative model based on convolutional neural networks. This network is trained to learn the underlying features among the existing airfoils and is able to generate sample airfoils that are notably more realistic than those generated by other sampling methods. We propose a new sampling method for airfoils and wings, which is based on a deep convolutional generative adversarial network. To improve the efficiency of surrogate-based optimization, we apply recent machine learning techniques to reduce the abnormality of both initial and infill samples. Although a large number of variables are required for the shape parametrization, many of the shapes that the parametrization can produce are abnormal and do not add meaningful information to a surrogate model. Surrogate-based optimization has been used in aerodynamic shape optimization, but it has been limited due to the curse of dimensionality.
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Moreover, novel optimal rule compliant designs are presented for driver-type and putter-type discs, as well as their compromise, the so-called mid-range discs. The presented numerical optimization results also describe the many design tradeoffs between the discs that target long flight range (so-called drivers) and the discs that target flight at low speeds (so-called putters). The proposed numerical optimization method yields disc drag coefficient values as low as 0.48 (unconstrained) and 0.52 (constrained) and lift coefficient values as high as 0.26 (unconstrained) and 0.19 (constrained).
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Further, the Professional Disc Golf Association rules for permissible golf discs can be cast as nonlinear constraints for the computational optimization problem.
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The shape surrogate models facilitate free and constrained optimization in both single- and multiobjective settings, such that both aerodynamic (drag and lift) and structural (mass and moment of inertia) features of the disc are addressed simultaneously. Through application of batch Computational Fluid Dynamics simulations and Machine Learning, the disc parameterization yields functional relationships-so-called shape surrogate models-between the flying rotating disc shape and its flight characteristics. In this article, we introduce a computational methodology for golf disc shape optimization that employs a novel disc shape parameterization by cubic B-splines.