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A Computational Framework for Simulating Joint Mechanics

Sponsor: National Institutes of Health  

This research project is an interdisciplinary work involving biomechanics and high-performance computing. The HPC group focuses on developing efficient, scalable and reliable computational framework for simulating human joint mechanics, and Computational Biomechanics lab at the Mechanical and Aerospace Engineering Department, University of Florida, emphasizes on developing computer human joint model for simulating joint mechanics, especially contact stresses.

Specific objectives of the project include:
(1) Create a dynamic musculoskeletal model with deformable knee joint contact. Deformable contact in the knee will be studied initially since the knee is the most commonly injured joint.
(2) Incorporate this model into a parallel-processing optimization framework. Parallel processing will used to reduce the computational time for predictive optimizations from weeks to a matter of hours.
(3) Evaluate the model's ability to predict experimental movement data. Pre-existing experimental movement data will be used to evaluate the model's ability to predict motion and ultimately joint contact stresses.

The resulting functional virtual human model can then be used for basic research and clinical applications.

Simulation of joint mechanics (see Computational Biomechanics lab for more details)
Mechanical loading (contact stresses) is believed to play a major in degenerative joint diseases, so the knowledge of in vivo joint motion and loading during functional activities could help address this clinically significant issue.  Since non-invasive experimental approaches do not exist for measuring in vivo joint loading, computer simulations have been used to develop predictions given estimates of the muscle forces acting on the joint.  However, current rigid body and deformable modeling approaches are not able to calculate accurate contact stress results during movement in critical joints such as the knee. A logical solution to this problem is to incorporate deformable joint models into a larger rigid body dynamic model.

Computational framework
Since the dynamic optimization problem solved via simulation has very high computational complexity, high-performance computing is key to resolve the limitations. Parallel optimization algorithms, which parallelize each function evaluation to processors in parallel computers are potential solutions to simulation problems.  So far, parallel optimization algorithm research have been done using gradient or non-gradient based optimization algorithms. Owing to their fast convergence characteristics, gradient based methods have been used typically in these kinds of simulation even though the methods require time-consuming parameter sensitivity studies as the presence of numerical noise will often cause premature convergence. Non-gradient based methods converge to a global minimum but at the cost of slow convergence.

However, when the simulation itself has a small number of design variables, gradient based parallel optimization algorithm is not efficient to use.  For example, parallelization of gradient calculation in Quasi-Newton method can only use the same number of processors as the number of design variables.  This limitation affects scalability and the method is not efficient if resources are large.  Moreover, non-parallelized line searches in Quasi-Newton method decrease the speedup and parallel efficiency.  To overcome such limitation, another layer of parallelization at the analysis function level itself can be considered.
 

Current work focuses on the developing parallel gradient based optimization (BFGS) algorithm using commercial optimization tool, and developing finer parallel algorithms in single function evaluation. The hybrid approach can increase the scalability and overcome the limitations in speedup.  Additionally, adapting fault tolerance concepts in this simulation can provide efficient, scalable, and reliable computational framework so that the result of simulating human joint mechanics can be valuable for clinical issues on joint diseases.

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