Multidisciplinary Optimization of Unmanned Aircraft Considering radar signature sensors and trajectory constraints
CHALLENGE - The optimization problem of the present case study is about improving the radar signature and sensor capabilities of the aircraft while at the same time ensuring that it can perform well in the given mission profile and that it has acceptable flying characteristics.
SOLUTION - The optimization of the design was based on the process presented in Fig. 10, whereas the framework was modified by implementing the developed metamodels for the calculation of the RCS and sensor performance. In total, five optimization cases were investigated by considering each trajectory pattern as a fixed input constraint and by allowing a search of the design space. A genetic algorithm that was included in the optimization software modeFRONTIER was used in all cases because a thorough exploration of the design space was herein far more important than the total number of evaluations. The specific settings of the algorithm included an evolution process of 100 generations based on an initial population size of 30 individuals with 0.1 and 0.9 mutation and crossover probabilities, respectively.
BENEFITS - The proposed framework is tested against five different surveillance scenarios, and it is shown that the consideration of different trajectories in the optimization of UAVs can result in different, but generally better, designs with an improvement that ranges from 5 to 56%. At the same time, it is also shown that overlooking the trajectory can lead to suboptimal configurations that, in some cases, can illustrate up to almost 25% worse performance with respect to radar signature and target detection probability.