The Biomechanical Consequences of Arthroscopic Hip Capsulotomy and Repair in Positions at Risk for Dislocation

Background: The effect of interportal (IP) capsulotomy, short T-capsulotomy, and long T-capsulotomy, and their repairs, on resistance to anterior and posterior “at risk for dislocation” positions has not been quantified. Hypotheses: Our primary hypothesis was that an IP capsulotomy would have a minimal effect on hip resistive torque compared with both short and long T-capsulotomies in the at-risk dislocation positions. Our secondary hypothesis was that capsule repair would significantly increase hip resistive torque for all capsulotomies. Study Design Controlled laboratory study. Methods: We mounted 10 cadaveric hips on a biaxial test frame in an anterior dislocation high-risk position (20° of hip extension and external rotation) and posterior dislocation high-risk position (90° of hip flexion and internal rotation). An axial force of 100 N was applied to the intact hip while the femur was internally or externally rotated at 15° per second to a torque of 5 N·m. The rotatory position at 5 N·m was recorded and set as a target for each subsequent condition. Hips were then sequentially tested with IP, short T-, and long T-capsulotomies and with corresponding repairs randomized within each condition. Peak resistive torques were compared using generalized estimating equation modeling and post hoc Bonferroni-adjusted tests. Results: For the anterior position, the IP and long T-capsulotomies demonstrated significantly lower resistive torques compared with intact. For the posterior position, both the short and long T-capsulotomies resulted in significantly lower resistive torques compared with intact. Repairs for all 3 capsulotomy types were not significantly different from the intact condition at anterior and posterior positions. Conclusion: An IP incision resulted in a decrease in capsular resistive torque in the anterior but not the posterior at-risk dislocation position, in which direction only T-capsulotomies led to a significant decrease. All capsulotomy repair conditions resulted in hip resistive torques that were similar to the intact hip in both dislocation positions. Clinical Relevance Our results suggest that it is biomechanically advantageous to repair IP, short T-, and long T-capsulotomies, particularly for at-risk anterior dislocation positions.

Biaxial testing of textile material - methodology and instrument assembly procedure

Membrane structures are becoming popular because of the potential usage in structures with higher aesthetic claims. For the roofing of these structures, different textile materials are used. These special materials offer an alternative to conventional roofing materials and allow, besides its lightweightness, also a possibility to roof a difficult floor plan and big span. When designing such a construction, there are some challenges related to the properties of the specific material. In order to exploit the full potential of textile membrane materials, it is necessary to choose an appropriate material model for numerical modelling, which takes into account its nonlinear behaviour. The two most important material characteristics needed for characterizing the behaviour are Young’s modulus of elasticity and Poisson’s ratio. Since the material is orthotropic in most cases, it is necessary to test the material in two directions; therefore, these characteristics need to be obtained from the biaxial test. This contribution is focused on the research of the methodology of biaxial tests and test instrument assembly procedure, which will be used for the following testing of textile materials.

Understanding regional mechanics of rat myocardia by fitting hyperelastic constitutive models

Background: Availability of biaxial mechanical data for heart myocardia remains high in demand for the development of accurate and detailed computational models. Lack of accurate mechanical data for myocardia may delay the understanding of heart diseases mechanisms. Therefore, the aim of this study is to develop understanding of the regional difference of wall mechanics using rat heart in the left ventricle (LV), septal wall (STW) and right ventricle (RV). This was achieved by conducting a biaxial test on three rat heart myocardia (i.e LV, RV and STW). To select the best hyperelastic model that may be utilised for the development of computational models of the heart, the Fung, Choi-Vito, Polynomial (Anisotropic), Four-Fiber family, Holzapfel (2000) and Holzapfel (2005) hyperelastic models were selected and fitted on the biaxial data of the LV, RV and STW. Results: The best hyperelastic model was selected based on evaluation index (EI) which utilises the Coefficient of Determination (R2). All the six hyperelastic constitutive models were then compared in all three rat heart myocardia. The results show that the Polynomial (Anisotropic) model outperforms the other five models in all myocardial tissues with EI’s above 90 %. The Four-fiber-family and the two Holzapfel models perform equally in the LV and STW myocardial tissue with EI of 50 and 70 %, respectively. Conclusions: The Fung and Choi-Vito models yielded poor goodness of fit in the LV and STW myocardial tissues. The results presented here will be useful for detailed development of accurate computational models studying mechanisms of cardiovascular diseases.

Understanding regional mechanics of rat myocardia by fitting hyperelatsic models

Availability of biaxial mechanical data for heart myocardia remains high in demand for the development of accurate and detailed computational models. The aim of this study is to study the regional difference of wall mechanics using rat heart in the left ventricle (LV), septal wall (STW) and right ventricle (RV). This was achieved by conducting a biaxial test on three rat heart myocardia (i.e LV, RV and STW). Fung, Choi-Vito, Polynomial (Anistropic), Four-Fiber family, Holzapfel (2000) and Holzapfel (2005) hyperelastic models were selected and fitted on the bixial data of the LV, RV and STW. The best hyperelastic model was the selected based on evaluation index (EI) determined from the coefficient of determination (R2). All the six models were then compared in all three rat heart myocardia. The results show that the Polynomial (Anisotropic) model outperforms the other five models in all myocardial tissues with EI’s above 90 % goodness of fit. The Four-fiber-family and the two Holzapfel models perform equally in the LV and STW myocardial tissue between 50 and 70 % goodness of fit. The Fung and Choi-Vito models yielded poor goodness of fit in the LV and STW myocardial tissues. Parameter fitting is useful method in advancing reliable data to be used in the development of accurate computational models.

Study of Shear Behavior of Binary Mixtures by DEM Simulation of Biaxial Test in the Membrane Boundary Condition

This paper aims to study the shear behavior of binary particles with irregular shapes by discrete element method simulations of the biaxial test in the membrane boundary condition. Binary particle samples are generated according to different volume fractions of coarse and fine particles. The deviatoric stress and volumetric strain curves are plotted to describe the contracting-dilatancy relationship of binary samples under shearing conditions. The anisotropy of the normal and tangential contact forces are explored by visualization of the orientation of contact forces to describe the evolution of micro structures of samples during the shearing process. Besides, the formations of the shear band are observed by the visualization of the newly generated contact force chains between particles. The research shows that the volume fraction of coarse particles and particle size ratio have significant influences on the shear behavior of binary particles both in macroscopic and microscopic points of view. Moreover, the increased volume fraction of coarse particles leads to a more difficult formation of a shear band.

Estimating Contact Force Chains Using Artificial Neural Network

The inhomogeneous distribution of contact force chains (CFC) in quasi-statically sheared granular materials dominates their bulk mechanical properties. Although previous micromechanical investigations have gained significant insights into the statistical and spatial distribution of CFC, they still lack the capacity to quantitatively estimate CFC evolution in a sheared granular system. In this paper, an artificial neural network (ANN) based on discrete element method (DEM) simulation data is developed and applied to predict the anisotropy of CFC in an assembly of spherical grains undergoing a biaxial test. Five particle-scale features including particle size, coordination number, x- and y-velocity (i.e., x and y-components of the particle velocity), and spin, which all contain predictive information about the CFC, are used to establish the ANN. The results of the model prediction show that the combined features of particle size and coordination number have a dominating influence on the CFC’s estimation. An excellent model performance manifested in a close match between the rose diagrams of the CFC from the ANN predictions and DEM simulations is obtained with a mean accuracy of about 0.85. This study has shown that machine learning is a promising tool for studying the complex mechanical behaviors of granular materials.