Golf discs are typically categorized as drivers, mid-rangers, or putters, and there are currently over 1300 different-shaped discs approved by PDGA. ![]() Indeed, players attempt to choose the most suitable disc for each throw situation and course layout, by exploiting the discs’ different flight characteristics. ![]() While the ball shape and appearance in traditional golf are rather fixed (clubs do vary), considerable shape variation is allowed in golf discs. For example, in Finland alone, single-disc golf courses have reportedly been visited by over 2000 players on a weekly basis in 2020. Further, the number of recreational disc golf players is considerably larger than this. In 2020, the Professional Disc Golf Association (PDGA) reported 71016 active members worldwide, \(33\%\) growth from 2019. Although perhaps not yet as popular as traditional golf, the disc sport is spreading rapidly across the world. 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.ĭisc golf is a sport that is similar to traditional golf, but instead of a club and a ball, players use rotating flying discs thrown toward standing baskets in as few intermediate steps as possible. 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). Further, the Professional Disc Golf Association rules for permissible golf discs can be cast as nonlinear constraints for the computational optimization problem. ![]() 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.
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