A methodology for physically-based contact and meniscus properties in rigid-body computational knee modeling

Stephen P Wilson, University of Texas at El Paso

Abstract

Determining natural inner knee mechanics is a longstanding goal for researchers with applications to prevention and treatment of knee trauma and osteoarthritis. Physical testing has only provided limited information of knee mechanics due to technical challenges and cost. Modeling has been used for decades to obtain some of this otherwise inaccessible information, and recently finite element analysis (FEA) has become a popular means to this end. However, FEA requires time intensive mesh-creation and has large computational requirements. Ideally, model creation should be easy and simulations should be fast to allow for sensitivity analysis. Although allowing easier model creation and offering over an order of magnitude more computational efficiency than FEA, current rigid body modeling of the knee is limited by imprecise methodologies for defining material properties. Cartilage and meniscus are particular points of weakness. The following thesis develops an improved methodology for cartilage contact which is user-friendly and allows for precise definition of contact via user-supplied material properties while accounting for changes in stiffness due to discretization. Additionally, meniscus modeling is improved by developing and implementing equations which directly define stress-strain relationships to match values reported in literature or those selected by the user. Results from two implemented knee models are compared to experimental results in literature and sensitivity to material properties and driving kinematics is investigated.

Subject Area

Biomedical engineering|Biomechanics

Recommended Citation

Wilson, Stephen P, "A methodology for physically-based contact and meniscus properties in rigid-body computational knee modeling" (2015). ETD Collection for University of Texas, El Paso. AAI1594187.
https://scholarworks.utep.edu/dissertations/AAI1594187

Share

COinS