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New Computational Framework for Biomechanical Multibody System Applications

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posted on 24.10.2013, 00:00 by Florentina M. Gantoi
Most of the computational finite element (FE)-based bio-mechanics models employ static or quasi-static assumptions. These models fail to capture the response of the human body and its joints to high speed cyclic loading. They also fail to accurately capture the change in the system motion that is governed by highly nonlinear differential and algebraic equations. This thesis aims at addressing this important issue by developing a new computational framework for modeling human body and its joints, with particular interest in the knee joint mechanics. The new computational framework is based on successful integration of multibody system (MBS) and large displacement FE algorithms. In this new computational framework, the absolute nodal coordinate formulation (ANCF) is used as the basis for the description of the rigid geometry as well as the deformation of the very flexible ligaments. ANCF finite elements have many desirable features that can be exploited in modeling complex bio-mechanics systems. These elements can be used to capture the deformations of the ligament cross sections, allow for the use of general material laws that are suited for developing accurate ligament models, have a constant inertia matrix that lead to an optimum sparse matrix structure, and their kinematic description is consistent with the description used in computational geometry methods, thereby allowing for converting CAD models to FE meshes without geometry distortion. The new computational framework used in this thesis also allow for modeling more general boundary conditions at the ligament bone insertion sites. The approach described in this thesis can be used to develop more realistic models of the human body joints and is applicable to future research studies on ligaments, muscles and soft tissues (LMST).



Shabana, Ahmed


Mechanical Engineering

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University of Illinois at Chicago

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Committee Member

Brown, Michael Foster, Craig Lilley, Carmen Royston, Thomas

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