## 1 Introduction  
Total knee arthroplasty (TKA) is a prevalent treatment option for osteoarthritis [1], which is the indication for 88% of TKA surgeries [2]. Ten years after surgery, survival rates are 86.1-93.8% [3, 4, 5, 6, 7, 8] and satisfaction rates are 81%. This means 19% of patients are either dissatisfied with or uncertain about the results of their procedure [9]. The rate of revisions due to aseptic causes in the first 15 years after primary TKA is 3% [10].  
The detection and elimination of implant-related issues that may lead to clinical problems once the implant system is marketed is the main focus of the pre-clinical testing phase. Aside from continuous biomechanical research, there are additional mandatory steps for implant manufacturers. To achieve regulatory clearance, standardized tests need to be performed in the pre-clinical testing stage. The majority of these tests focus on single components under high cyclic [11; 12; 13] or static [14] loading conditions. Pre-clinical test methods under physiological loading conditions (e.g., simulation of daily living activities in a tribological wear simulator [15; 16; 17; 18; 19]) exist and enable more complex assessments, such as the analysis of the resulting knee kinematics with an implant.  
However, running these tests is time-consuming and cost-intensive [20]. Hence, they are usually only executed towards the end of the design process, with only the final design or with very few design variants in a single size combination.  
To analyze knee kinematics and possible biomechanical causes for unmet patient expectations after TKA, computational simulation of joint mechanics is frequently used [21; 22; 23; 24; 25; 26]. The state of the art in biomechanical research to precisely predict the kinematics for one specific combination of implant design and subject are musculoskeletal models [27]. However, the great effort needed to adapt such musculoskeletal models to several design variants or size combinations prevents their routine use in early stages of implant design. On the other hand, there are only a few FE models published that are suitable for kinematic analyses [28; 29].  
To the best of our knowledge, there are no models available with the ability to compare multifarious implant designs and size combinations concerning kinematics and implant loads without massive effort for every single variation. With such a model, it would be possible to identify the combination of design parameters which leads to the best possible fit to the defined requirements [30].  
This work is part of a project which aims to address this lack of dynamic FE models of the knee joint for design stage and pre-clinical testing of new prostheses. The newly developed model is intended to be a tool to compare implant loading, component stresses, and kinematic behavior of the tibiofemoral and the patellofemoral joint for various implant design variants or for variations of other parameters, such as the stiffness of the patellar tendon or any other ligaments. In this model, only standardized tibial loads and flexion angles [31] are needed as an input, and the relative kinematics are part of the results.  
To be able to run meaningful parametrical studies to improve implant designs, the model should be fast to solve without compromising the informative value of the dynamic results. In accordance with all standardized testing scenarios, the influence of effects caused by individual patient parameters needs to be restrained in order to achieve comparable results.  
Besides the possibility to analyze effects of implant design changes, this model is also intended to uncover effects of changes in the explicitly modeled anatomical structures. This will be shown within this study by analyzing the effects of varying patellar tendon stiffness and initial proximo-distal position of the patella on patellar kinematics.