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Reliability theory is used to assess the sensitivity of a passive flexion andactive flexion of the human lower leg Finite Element (FE) models with TotalKnee Replacement (TKR) to the variability in the input parameters of therespective FE models. The sensitivity of the active flexion simulating thestair ascent of the human lower leg FE model with TKR was presented before in[1,2] whereas now in this paper a comparison is made with the passive flexionof the human lower leg FE model with TKR. First, with the Monte CarloSimulation Technique (MCST), a number of randomly generated input data of theFE model(s) are obtained based on the normal standard deviations of therespective input parameters. Then a series of FE simulations are done and theoutput kinematics and peak contact pressures are obtained for the respective FEmodels (passive flexion and/or active flexion models). Seven output performancemeasures are reported for the passive flexion model and one more parameter wasreported for the active flexion FE model (patello-femoral peak contactpressure) in [1]. A sensitivity study will be performed based on the ResponseSurface Method (RSM) to identify the key parameters that influence thekinematics and peak contact pressures of the passive flexion FE model. Anothertwo MCST and RSM-based probabilistic FE analyses will be performed based on areduced list of 19 key input parameters. In total 4 probabilistic FE analyseswill be performed: 2 probabilistic FE analyses (MCST and RSM) based on anextended set of 78 input variables and another 2 probabilistic FE analyses(MCST and RSM) based on a reduced set of 19 input variables. Due to the likelycomputation cost in order to make hundreds of FE simulations with MCST, ahigh-performance and distributed computing system will be used for the passiveflexion FE model the same as it was used for the active flexion FE model in[1].

High-Performance and Distributed Computing in a Probabilistic Finite  Element Comparison Study of the Human Lower Leg Model with Total Knee  Replacement