Vulnerability-driven multi-objective topological optimization of aircraft protections

Abstract

Enhancing the survivability of platforms is paramount for increasing their safety level and limiting the loss of lives. This is particularly important in the military field, where aircraft vulnerability has been studied for decades through specifically developed frameworks. So far, most of the contributions have focused on improving the accuracy of methods for assessing the vulnerability to ballistic projectiles, mainly by advancing our understanding of the penetration of the threat through the components of the platform. Although state-of-the-art solutions allow conducting accurate assessments, the information provided by such methods has not been fully exploited yet for automatically designing corrective solutions and improving the survivability of platforms. For this reason, this work proposes a vulnerability-driven multi-objective topological optimization algorithm for designing protective surfaces. A genetic algorithm is used to describe the Pareto front delimiting the space of solutions characterized by optimal vulnerability and weight. The number of clusters that constitute the protections proposed by the optimization algorithm is minimized by adopting a spatial filter that penalizes fragmented designs. The framework is demonstrated through a case study involving a portion of a realistic remotely piloted aircraft system impacted by ballistic projectiles.