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Ball sports are becoming faster and more demanding than ever before, pushing traditional ball designs to their limits. In order to meet the increasing performance requirements, the ball manufacturers are producing new designs progressively. A traditional spherical football made of 32 leather panels stitched together in 1970s has now become only 14...
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Context 1
... order to understand the flow structure around a 32-panel ball and a 14-panel seamless ball, the airflow was visualized using smoke (see Figure 3). Due to the roughness created by the seams in 32-panel ball, the airflow over ball became turbulent and subsequently generated favorable pressure gradient and delayed flow separation as shown in Figure 3(a). ...
Context 2
... order to understand the flow structure around a 32-panel ball and a 14-panel seamless ball, the airflow was visualized using smoke (see Figure 3). Due to the roughness created by the seams in 32-panel ball, the airflow over ball became turbulent and subsequently generated favorable pressure gradient and delayed flow separation as shown in Figure 3(a). The airflow appears to be separated at around 100° from horizontal direction. ...
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Increasing technological advancements and demand for performance compel the ball manufacturers to introduce new designs. Construction of spherical footballs has been significantly changed over the years since 1970 from 32-panel leather stitched ball to 8-panel synthetic thermally bonded modern football. Despite being most popular game in the world,...
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Citations
... Their results show that at 30 m s −1 and 0 • seam angle, lift force, which acts perpendicular to the swing and drag forces, increases from less than 0.1 N at 0 Hz rotation rate, to between 0.3 and 0.45 N at 8.3-16.7 Hz, with these forces only 'weakly dependent on the speed of rotation'. These results are explained by the Magnus effect which causes balls in tennis, baseball, football and golf to curve in the air, as described in Mehta & Pallis (2001), Alam et al. (2010aAlam et al. ( , 2012, Bearman & Harvey (1976) and Mehta (1985). Mehta (2014) discusses how slow bowlers, as well as unusual side-arm bowlers, are able to use the Magnus effect to make cricket balls deviate laterally, but these phenomena are not the focus of this review. ...
This paper reviews 70 years of cricket ball swing literature and reassesses the results in light of new measurements. A comparison of ball tracking data with published experimental results shows that current understanding does not explain the behaviour observed in professional cricket matches. Descriptions of cricket ball boundary layer aerodynamics are updated with new results which show that the seam acts more like a series of vortex generators than a trip and that there is a laminar separation bubble on the seam side of the ball. Previous results for reverse swing are consolidated and compared with new data, showing that different magnitudes of swing occur depending on the condition of both sides of the ball. Variation in pressure and temperature should be included when the Reynolds number, a non-dimensional ball speed, is calculated, while humidity can be neglected. Studies on the effect of wind speed and direction are summarised and results considering the effect of free-stream turbulence are compared with new measurements. Throughout the paper, recommendations for future work are suggested. These include quantitative study of the effect of surface defects and roughness, an assessment of whether atmospheric turbulence can affect swing and investigation into the effect of backspin on swing.
... For example, the effects of the projectile angle on a particle traveling trajectory, velocity, and acceleration are studied using the equation of motion with consideration of air resistance [10]. Additionally, the effects of initial velocity, kick force, and kick position on the projectile trajectory of a soccer ball are explored [11,12,8]. ...
... The integration of three sub-models established in Sections 2.1-2.3 builds a dynamics simulation system for capturing kinematics and dynamics performance of a soccer ball projectile motion. Using a tool of dynamics software [11], it can The solution for these models supports the sports analysis of soccer ball. The dynamics models can help players to find the kicking force, kicking position and kicking angle to achieve an expected trajectory. ...
这The kinematics and dynamics modeling are an important factor to realize intelligent analysis of a soccer ball motion in a virtual environment. The problem of how to accurately create a three-dimensional (3D) soccer model is addressed in this Chapter. The main steps are introduced to build a basic dynamics model of soccer ball. A numerical validation method is suggested to validate the dynamics models of soccer ball. The virtual prototype technology is applied for the solution of model validation problems in the quest for using a virtual simulation instead of a physical simulation. A case study illustrates the dynamics model is highly reliable in predicting the flight trajectory of a soccer ball.
... For example, the effects of the projectile angle on a particle traveling trajectory, velocity, and acceleration are studied using the equation of motion with consideration of air resistance [10]. Additionally, the effects of initial velocity, kick force, and kick position on the projectile trajectory of a soccer ball are explored [11,12,8]. ...
... The integration of three sub-models established in Sections 2.1-2.3 builds a dynamics simulation system for capturing kinematics and dynamics performance of a soccer ball projectile motion. Using a tool of dynamics software [11], it can The solution for these models supports the sports analysis of soccer ball. The dynamics models can help players to find the kicking force, kicking position and kicking angle to achieve an expected trajectory. ...
In this Chapter, a spatial two-body dynamics model of soccer field-ball has been developed. The model involves gravity, aerodynamic drag, and contact force. The trajectory of the ball flight and the force applied to the ball are visualized through virtual kinematics and dynamics simulation. All works form a complete procedure to illustrate the application of dynamic simulation on soccer ball projectile motion analysis.
... The ball traveling trajectory of a successful direct free kick likes a banana so that the ball flies over the player wall to the goal. A model predicting a banana free-kick must include both the effects of gravity, aerodynamic characteristics (such as the air resistance), and Magnus [3][4][5][6][7][8]. The effects of the initial velocity, kick force, and kick position on the ball traveling trajectory were investigated [9][10][11][12]. ...
... The dynamics model of the soccer ball is established under the four assumptions[13]: (1) the air resistance applied on the ball to be homogenous, (2) ignoring the ball flying spin,(3) ignoring the Magnus effect on the ball, (4) ignoring the gyroscopic moment of the ball, and (5) neglecting acceleration of Coriolis. Then, Equations of Motion 3.1-3.3 ...
The intelligent analysis of the motion of a soccer ball requires accurately predicting its trajectory and finding the accurate design parameters to achieve a goal. The optimization design can efficiently improve the design parameters within the required range to exact evaluation of flight trajectory. In this Chapter, an optimization method is proposed to plan, evaluate, and optimize the traveling trajectory of a soccer ball. The parameterization technology has been integrated into the optimization design for automatically capturing the expected target, which is expressed as a function of all design parameters. A case study goes through multi-body dynamics modeling, dynamic simulation, and optimal objective modeling.
... The ball traveling trajectory of a successful direct free kick likes a banana so that the ball flies over the player wall to the goal. A model predicting a banana free-kick must include both the effects of gravity, aerodynamic characteristics (such as the air resistance), and Magnus [3][4][5][6][7][8]. The effects of the initial velocity, kick force, and kick position on the ball traveling trajectory were investigated [9][10][11][12]. ...
... The dynamics model of the soccer ball is established under the four assumptions[13]: (1) the air resistance applied on the ball to be homogenous, (2) ignoring the ball flying spin,(3) ignoring the Magnus effect on the ball, (4) ignoring the gyroscopic moment of the ball, and (5) neglecting acceleration of Coriolis. Then, Equations of Motion 3.1-3.3 ...
In this Chapter, the dynamics modeling, and virtual simulation of soccer ball free kick are investigated. A three body dynamics model is created based on the Newton second law and Hooke’s law. The model includes gravity, aerodynamic drag, Magnus force, and contact force. The motion of soccer ball is established as the time-dependent ordinary differential equations (ODEs). The virtual prototype technology is introduced and applied for the dynamic simulation of soccer ball free kicks. Some typical results are obtained.
... It revealed that the Reynolds number and the total seam length had a strong correlation. Alam et al. [13][14][15] experimentally evaluated with aerodynamic forces and moments for several FIFA-approved soccer balls under different wind conditions. Another experimentation of similar nature was undertaken by Alam et al. [16]. ...
The football game is the most popular, played, and loved sport around the world. The advent of technological breakthroughs and the continuous increase in consumer demand have led to a revolution in football’s design and manufacturing process. In the past, studies in soccer ball aerodynamics mainly were limited to the investigation of lift and drag forces inside a wind tunnel apparatus. A few researchers have analyzed the flow around the different soccer balls using computational fluid dynamics simulations with the Reynolds-Averaged-Navier–Stokes equations model. This study primarily intends to simulate a modern soccer ball (Adidas Telstar 18) using the Large Eddy Simulations technique. The whole research is divided into two phases. In the first phase, the flow around a smooth sphere is simulated numerically to validate the meshing strategy, boundary conditions, and solution methodology. The same modeling approach is used in the later stage to simulate the flow around a soccer ball. The effect of panels and seam on the boundary layer flow separation and overall turbulent flow structure around the soccer ball are visualized. The results indicate that the large-eddy simulations help predict the flow intricacies by resolving small eddies near the panels.
... We simulated different trajectories under two different conditions: gravity only and gravity + air drag. Air viscosity around the ball (ρ) was set to 1.225 kg/m 3 (value at sea level), and C d was set to 0.346 or 0.4 for baseball and soccer balls, respectively (Alam et al., 2010;Kagan and Nathan, 2014). We introduced one initial vertical speed V y 0 = 9.807 m/s (2 s of flight time under gravity only conditions) for each trajectory and corresponding approaching speeds (V y 0 = 7.5, 15, 25 m/s) different balls launched at the origin. ...
Catching a ball in a parabolic flight is a complex task in which the time and area of interception are strongly coupled, making interception possible for a short period. Although this makes the estimation of time-to-contact (TTC) from visual information in parabolic trajectories very useful, previous attempts to explain our precision in interceptive tasks circumvent the need to estimate TTC to guide our action. Obtaining TTC from optical variables alone in parabolic trajectories would imply very complex transformations from 2D retinal images to a 3D layout. We propose based on previous work and show by using simulations that exploiting prior distributions of gravity and known physical size makes these transformations much simpler, enabling predictive capacities from minimal early visual information. Optical information is inherently ambiguous, and therefore, it is necessary to explain how these prior distributions generate predictions. Here is where the role of prior information comes into play: it could help to interpret and calibrate visual information to yield meaningful predictions of the remaining TTC. The objective of this work is: (1) to describe the primary sources of information available to the observer in parabolic trajectories; (2) unveil how prior information can be used to disambiguate the sources of visual information within a Bayesian encoding-decoding framework; (3) show that such predictions might be robust against complex dynamic environments; and (4) indicate future lines of research to scrutinize the role of prior knowledge calibrating visual information and prediction for action control.
... Based on the sphere diameters and that the measurement was taken at 20˚C, the Re range is between 6 x 10 3 and about 2.9 x 10 5 and the C d value is reported to be around 0.45-0.50 [39,53,54]. Using the wind tunnel setup described above, steady sphere drag was measured through the wind velocity range of 1 to 22 m s -1 . ...
Vascular epiphytes represent almost 10% of all terrestrial plant diversity. Being structurally dependent on trees, epiphytes live at the interface of vegetation and atmosphere, making them susceptible to atmospheric changes. Despite the extensive research on vascular epiphytes, little is known about wind disturbance on these plants. Therefore, this study investigated the wind-epiphyte mechanical interactions by quantifying the drag forces on epiphytic bromeliads when subjected to increasing wind speeds (5–22 m s ⁻¹ ) in a wind tunnel. Drag coefficients ( C d ) and Vogel exponents ( B ) were calculated to quantify the streamlining ability of different bromeliad species. Bromeliads’ reconfiguration occurred first via bending and aligning leaves in the flow direction. Then leaves clustered and reduced the overall plant frontal area. This reconfiguration caused drag forces to increase at a slower rate as wind velocity increased. In the extreme case, drag force was reduced by 50% in a large Guzmania monostachia individual at a wind velocity of 22 m s ⁻¹ , compared to a stiff model. This species had one of the smallest C d (0.58) at the highest wind velocity, and the largest negative mean B (-0.98), representing the largest reconfiguration capacity amongst the tested bromeliads. The streamlining ability of bromeliads was mainly restricted by the rigidity of the lower part of the plant where the leaves are already densely clustered. Wind speeds used in this study were generally low as compared to storm force winds. At these low wind speeds, reconfiguration was an effective mechanism for drag reduction in bromeliads. This mechanism is likely to lose its effectiveness at higher wind speeds when continuous vigorous fluttering results in leaf damage and aspects such as root-attachment strength and substrate stability become more relevant. This study is a first step towards an understanding of the mechanical bottleneck in the epiphyte-tree-system under wind stress.
... In contrast to the small number of published studies on pellet aerodynamics there is an abundance of published material on ball aerodynamics for golf [7], football [8] [9], baseball [10], tennis [11], table-tennis [12] [13], and cricket [14]; there is also published material on badminton shuttle-cocks [15] and on frisbees [16]. The studies on sports balls focused mostly on how surface texture influences boundary layer separation and drag, and on how ball spin influences lift and trajectory. ...
Air rifle and air pistol target shooting are included in major intentional and national sports competitions and are also highly popular sport pastimes. Published scientific studies of pellet drag are very rare, in contrast to a large number of scientific studies published on aerodynamic drag of sports balls and other sports projectiles. Measurements are presented of the drag coefficients for 31 air rifle pellets of mainly 4.5 mm (0.177 in) calibre having a wide range of geometries. The drag coefficient measurements were made with a low-turbulence open wind tunnel at flow velocity of 200 m/s (Mach and Reynolds numbers 0.57 and 56,000 for 4.5 mm pellets). The detailed geometry of some pellets was altered systematically in order to improve understanding of how pellet geometry affects drag coefficient. The drag coefficient for the 31 pellets varied widely from 0.36 to 0.78, and it was influenced substantially by the curvature of the flow separating from the pellet head rim. Large curvatures delayed flow re-attachment onto the pellet tail, thereby lowering pellet base pressure and increasing the value of drag coefficient. Pellets with hemi-spherical or ogive-shaped noses generally had lower values of drag coefficient than pellets with other nose shapes. The presence of the pellet tail was beneficial by providing a surface onto which the flow detaching from the pellet rim could re-attach. However, for minimisation of drag coefficient, the pellet tail had to be of a certain optimum length which depended on the shape of the pellet nose. Small differences in pellet geometry had significant influence on the value of drag coefficient. Increase in air velocity from 120 to 200 m/s had small influence on the value of drag coefficient for three common sports pellets having flat, conical and dome-shaped noses.
... The magnitude of the lift force is greatly affected by the speed and rotation rate of the ball. The negative Magnus force is also reported under limited conditions [3]. On the other hand, the irregular lateral forces that cause blurring balls were not measured in the drive spin ball, so irregular flights were not observed. ...
... Besides the previous visualization measurements on the jet flow [4][5][6], Hong et al. [19,20] reported that the position of flow detachment varied with the location of the ball panel grooves, the number of seams, and the spacing of the grooves, using the 2D particle image velocimetry (PIV) measurement. Visualization measurements of flow using smoke wire reported that the flow separation position moved back further for balls with larger surface roughness than balls with smaller surface roughness [3]. ...
The panel patterns of soccer balls that change with each World Cup have a significant impact on the balls’ aerodynamic and flight characteristics. In this study, the aerodynamic forces of eleven types of soccer ball with different panel patterns were measured in a wind tunnel experiment. We characterized the panel shapes of soccer balls by the length, cross-sectional area, and the panel grooves’ volume. The results confirmed that the drag and drag crisis characteristics are dependent on the groove length and volumes. Flow separation points were visualized by an oil film experiment and particle image velocimetry (PIV) measurement to understand the drag crisis of the soccer balls. The results showed that the panel shape of the ball significantly changes the position of the separation point near the critical region, where the drags crisis occurs. In the critical region, laminar and turbulent flows coexist on the ball. On the other hand, the effect of panel shape on the separation point position is small in subcritical and supercritical states.
