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An approach to aerodynamically optimizing cycling posture and reducing drag in an Ironman (IM) event was elaborated. Therefore, four commonly used positions in cycling were investigated and simulated for a flow velocity of 10 m/s and yaw angles of 0–20° using OpenFoam-based Nabla Flow CFD simulation software software. A cyclist was scanned using an IPhone 12, and a special-purpose meshing software BLENDER was used. Significant differences were observed by changing and optimizing the cyclist’s posture. Aerodynamic drag coefficient (CdA) varies by more than a factor of 2, ranging from 0.214 to 0.450. Within a position, the CdA tends to increase slightly at yaw angles of 5–10° and decrease at higher yaw angles compared to a straight head wind, except for the time trial (TT) position. The results were applied to the IM Hawaii bike course (180 km), estimating a constant power output of 300 W. Including the wind distributions, two different bike split models for performance prediction were applied. Significant time saving of roughly 1 h was found. Finally, a machine learning approach to deduce 3D triangulation for specific body shapes from 2D pictures was tested.

The surfing performance of two shortboard fin types with surface features were compared to a standard (control) fin with a smooth surface using dynamic computational fluid dynamics (CFD) simulations. The fins with surface features included designs with a partially grooved and serrated surface (CR), and humpback whale-inspired fins with tubercles and other features (RW). Surfboard roll, pitch and yaw during cutback maneuvers were simulated based on field data from surfers of intermediate, expert and professional (WCT) skill level surfing on ocean waves. Sustained resultant forces relative to the rider direction were significantly different between fin types, and lowest for RW at WCT-level rotations. CFD results also revealed RW’s ability to dampen effects of turbulent flow. RW fins were always the last to stall during a turn, and always exhibited the most gradual stall. CR fins had significantly lower pre-turn drag, and the highest mean resultant forces during the turn. Overall, CR fins appear best for forward acceleration and hold on the wave, while RW fins appear best for maneuverability and stability.

Compared to other Olympic sports, little research exists on competitive shortboard surfing—especially research comparing field and numerical data. In this paper, GPS sensors with 9-axis motion sensors were used to collect data on nearly 2000 surfed waves. Data were collected from four surfers of differing skill levels, ranging from intermediate/advanced (Level 6) to top-ranked professional (Level 9). The results revealed a positive correlation between surfer skill level and roll/pitch/yaw rates during a cutback. Some surfers used two different fin types: a standard commercial fin (C), and a 3D-printed, humpback whale-inspired fin (RW). Statistically significant cutback performance improvements were seen when surfers used the RW fin. Because of the skill level differences suggested by the field data, dynamic computational fluid dynamics (CFD) analysis was performed to simulate cutback maneuvers at three different rotation rates (roll/pitch/yaw). Sustained resultant forces relative to the rider direction were lower for RW fins during the turn, suggesting a less-skilled surfer could generate faster and more powerful turns using RW fins. Field results also confirmed that a skill Level 8 surfer performed closer to skill Level 9 when using RW fins, but not control fins. Surfers experienced more stability using RW fins, and CFD results confirmed RW’s ability to dampen the effects of turbulent flow.

In professional cycling, even small adjustments in position could mean that valuable seconds are gained over the course of a time-trial race. This study investigates the influence of arm position on the aerodynamic drag of a cyclist. Based on a 3D scanned model of a professional cyclist, 64 alternate positions are generated. The parameters that are investigated are the distance between elbows, elbow extension, and distance between hands. Computational fluid dynamics (CFD) simulations of all positions are performed, and a regression model is built from the results. The results indicate that the optimal posture is achieved for a minimum in all investigated parameters, which means that the hands and elbows should be kept together with hands up towards the face. Furthermore, elbow extension seems to be the most crucial parameter, followed by the distance between elbows, and then by the distance between the hands. The presented methodology can be applied to study other parameters relevant to cycling aerodynamics or be applied to other sport activities as well.

Recently, manufactures who produce surfboards using computer aided design and robotic (CNC) shaping tools have gained a larger share of the surfing market, allowing board producers and shapers to produce lighter and more durable boards. The improvement in design and production process of surfboards has been directly linked with the adaption of technologies tested and validated in other industries. However, the surfing industry still lacks methodologies, standards and testing facilities to scientifically investigate and quantify the structural and hydrodynamic properties of surfboards. It is widely accepted that distributed rigidity, damping and stiffness in the three directions play a huge role in defining surfboard performance. These properties are rarely stated by producers and never measured. The present paper compares two boards with equal outline and geometric shape but built with two different technologies: one board built with a traditional high density polyurethane (PU) blank, polyester resin and a stringer to improve longitudinal stiffness and one board built with bio epoxy resin, expanded polystyrene (EPS) and two side inserts made of cork to improve longitudinal stiffness and damping. Different tests were carried out to compare the two boards and a testing methodology with the aim to evaluate board structural properties and identify key parameters that will influence the board performances. Accelerometers were used to characterize stiffness and damping while a custom-made flex machine was used to evaluate the torsional stiffness. Differences between the two boards are highlighted, leading to the fact that the two boards, even being identical in size, will behave differently in different wave conditions and for different surfers.

The present paper aims to show the potential of Computational Fluid Dynamics (CFD) solvers for surfboard design and its applicability by comparing three different surfboards with minimal changes in design. In fact, surfboard manufacturing routines are moving towards more controlled and reproducible manufacturing processes, in particular Computer numerically controlled (CNC) shaping techniques. As a consequence, three dimensional (3D) computer models of the boards start to be available, and can be imported in Computational Fluid Dynamics (CFD) programs. This opens up a new design methodology, where the performances of the different shapes can be studied and quantitatively evaluated, highlighting details that would be otherwise impossible to identify from a field test. The commercial CFD code STAR-CCM+ is used in the present work to compare the performance of three different surfboards, with different curvature at the bottom and different tail shapes. In the simulations, an Unsteady Reynolds Navier Stokes (URANS) approach is used, with the volume of fluid (VOF) method as free surface discretization method and the k-omega-SST turbulence model as numerical closure of the RANS equations. CFD proved to be a valid tool to compare the performances of the different shapes, bringing into light subtle but important differences between the designs. In particular, the static simulations showed that the rocker affects the performances by increasing the lift but also the drag of the board, also generating higher forces in maneuvering conditions. On the other hand, the tail shape did not affect the performances of the board in the analyzed cases.