Assessment of synthetic turf performance has been undertaken using a variety of experimental methods but is limited in understanding the complex physics of cleat-turf interaction. Computational models could provide insight, but there is currently no validated model of synthetic turf available. The scope of this study was to develop a Finite Element (FE) model of synthetic turf using a hierarchical approach and validate the model using independent test data. A physical third-generation synthetic turf comprising slit-film fibers with sand and rubber crumb infill was constructed. Experiments were conducted using a direct impact (Clegg) device and an artificial cleat-form. Material characterization tests were performed on the individual turf components, integrated into constitutive models, and a full turf FE model was constructed. The carpet was modeled with shell elements while the granular infill was modeled using smoothed-particle hydrodynamics (SPH) elements. A method to simulate the physical pre-conditioning performed on the experimental turf was developed. Both unconditioned and pre-conditioned turfs were assessed using Clegg tests. The re-created Clegg tests on the turf model demonstrated good agreement with the physical tests, with higher acceleration for the pre-conditioned turf. The turf model was validated using experiments with a turf test apparatus including dynamic translation and rotation of a cleat-form. The model predicted results in good agreement with the experiments (average CORA rating of 0.863) on pre-conditioned turf. The resulting model and methods can be expanded to synthetic turf of different constituent materials, to investigate their effects on cleat-surface interaction, optimizing performance, and reducing injury risk.

Understanding the size and shape variations of the head is important to the design of football helmets used to mitigate the risk of head injury. Current guidelines for selecting helmets use only the circumference of the player’s head to determine an appropriate size that may not offer the best fit and protection for every player. The goal of this study was to quantify key measurements of head shape to sufficiently characterize variability among players in the National Football League (NFL), focusing on the shape variation of the cranial region. Statistical shape analysis was performed on three-dimensional head scans of 87 contemporary NFL players to identify key features of head shape variability among the players’ heads. The principal component analysis revealed two factors that explained 87% of the head shape variance: (1) height and length of the head in the sagittal plane and (2) prominent back of the head with an oval shape in the transverse plane versus prominent forehead with a round shape. Four head shape measurements (circumference, length, breadth, and height) were then defined and quantified to describe the first two principal components. This information can facilitate improvements in the design of football helmets.

Objective: The goal of this study was to explore a framework for developing injury risk functions (IRFs) in a bottom-up approach based on responses of parametrically variable finite element (FE) models representing exemplar populations. Methods: First, a parametric femur modeling tool was developed and validated using a subject-specific (SS)-FE modeling approach. Second, principal component analysis and regression were used to identify parametric geometric descriptors of the human femur and the distribution of those factors for 3 target occupant sizes (5th, 50th, and 95th percentile males). Third, distributions of material parameters of cortical bone were obtained from the literature for 3 target occupant ages (25, 50, and 75 years) using regression analysis. A Monte Carlo method was then implemented to generate populations of FE models of the femur for target occupants, using a parametric femur modeling tool. Simulations were conducted with each of these models under 3-point dynamic bending. Finally, model-based IRFs were developed using logistic regression analysis, based on the moment at fracture observed in the FE simulation. In total, 100 femur FE models incorporating the variation in the population of interest were generated, and 500,000 moments at fracture were observed (applying 5,000 ultimate strains for each synthesized 100 femur FE models) for each target occupant characteristics. Results: Using the proposed framework on this study, the model-based IRFs for 3 target male occupant sizes (5th, 50th, and 95th percentiles) and ages (25, 50, and 75 years) were developed. The model-based IRF was located in the 95% confidence interval of the test-based IRF for the range of 15 to 70% injury risks. The 95% confidence interval of the developed IRF was almost in line with the mean curve due to a large number of data points. Conclusions: The framework proposed in this study would be beneficial for developing the IRFs in a bottom-up manner, whose range of variabilities is informed by the population-based FE model responses. Specifically, this method mitigates the uncertainties in applying empirical scaling and may improve IRF fidelity when a limited number of experimental specimens are available.

The purpose of this study was to investigate the athlete biomechanical differences across three artificial turfs associated with changes in athletic performance. Eighteen university grid-iron football players performed three modified football specific movements (agility drill, vcut, three-cone drill) on three artificial turf surfaces (Control, Shockpad and Low Faceweight) that have been shown to alter athletic performance. The utilized traction and player kinematics were recorded and compared between the surfaces using a repeated-measures ANOVA within participant factors of surface and between participants’ factors of player position. The significant effect of the surface on utilized traction at takeoff, and the frontal plane ankle angle was present with the players utilizing a lower traction and peak frontal plane ankle angle on the low-traction surface (Low Faceweight). During the agility drill and vcut, the significant effect of the surface was seen for peak frontal plane rearfoot–floor angle, with players having a flatter foot on high-traction surfaces (Control and Shockpad). Across tasks, high-traction surfaces were associated with flatter foot orientations and increased frontal plane ankle and foot kinematics during the cutting movements.