The number of flights have increased by 80% between 1990 and 2014, and the demand for air travel continues to increase. Even though the aviation sector contributes to economical and social benefits, it still affects the climate change [1]. A first step to minimize the environmental impact is to develop more electric aircraft (MEA), where the idea is to maximize the use of electricity and improve the overall energy effciency [2]. In most of today's aircraft, large mechanical transmission shafts with a lot of components are driven by central power units, termed centralized drive systems. By the use of electromechanical actuators (EMAs), a distributed drive systems can be used instead, which increases functionality, reduces mass, maintenance and energy consumption, as well as improves manufacturing and assembly [3]. 

When designing electromechanical actuators, one must take into account a lot of parameters that affect each other in various ways. It is often a time-consuming job to find the most optimal choice of architecture. Parameters such as temperature, load, lifetime and effciency to mention a few. This master thesis offers a new analytical tool for EMAs of primary and secondary flight control systems for Saab Avionics Systems. The aim of the analytical tool is to characterize the parts of the system and identify important parameters in order to find the most optimal choice of architecture. The tool focus on the main mechanical components such as the three-phase synchronous permanent magnet motor, power-off brake, two-stage planetary gearbox and ball screw. The tool developed in this project generates an initial design of the EMA with optimized dimensions in order to minimize both mass and energy consumption. It functions by identifying three main groups of parameters:

• The input parameters: fixed values defined by the customer demands
• The design parameters: variables that the user can change to find the optimal choice of architecture
• The output parameters: resulting values of either performance or dimensions

By defining few design parameters for each component, and implementing multidisciplinary design optimization (MDO), the analytical tool can find an optimized solution for each specific project in a time-efficient way. The final values of the parameters characterize the performance of the EMA.