CURRELI, CRISTINA
Combining musculoskeletal and FE models to predict contact mechanics and wear in total knee arthroplasty [Tesi di dottorato]
Pisa University, 2019-10-22

Despite the established success of total knee arthroplasty (TKA), wear of the polyethylene tibial insert remains a major limitation to the longevity of the implant. The present research aims at discussing and improving a method for a patient specific contact mechanics and wear prediction based on the combination of two modeling techniques: the musculoskeletal (MSK) and the finite element (FE) analyses. This combined modeling approach is a new and promising strategy in the field of computational biomechanics; however, its application to wear problems is still rare in the literature and some critical aspects remain unsolved. A FE submodeling procedure is proposed to reduce the computational cost of the wear simulations. It is first introduced and validated using simple 2D single point and 3D multipoint contact and wear problems FE wear models have been developed in Ansys® mechanical APDL using its new wear simulation and mesh smoothing routines. The analyses of the MSK system were performed using the software OpenSim and Matlab®. As case study, a specific patient implanted with instrumented knee prosthesis was considered whose experimental data is available in the literature. The effect of different MSK models definition on the kinematic and dynamic output that can be used as input condition to a TKA FE model is discussed. Finally, the effect of different boundary conditions on the contact mechanics and wear estimations is investigated using 3D FE wear models of the TKA. Results suggest that the proposed wear submodeling approach can significantly reduce the computational cost of TKA FE wear models. Some interesting points on the effect of the MSK modeling choice on the contact pressure and wear estimations are also highlighted.

diritti: info:eu-repo/semantics/embargoedAccess
application/pdf
Di Puccio, Francesca
ING-IND/13


Tesi di dottorato. | Lingua: it. | Paese: | BID: TD20017912