Structural Design of a Morphing Serpentine Inlet Using a Multi-Material Topology Optimization Methodology

Abstract

A promising avenue for development in the aerospace industry involves relocation of engines from conventional locations beneath the wings, to within the aircraft fuselage. Aircraft with these so-called embedded engines have the potential to increase engine efficiency, but necessitate the use of a serpentine engine inlet duct (S-duct) to provide the propulsion system with air. The optimal shape of an aircraft S-duct varies with flight condition, incentivizing the use of morphing systems to vary inlet parameters during flight; allowing for continual supply of the optimal airflow level. Design of a morphing system therefore requires collaboration across multiple disciplines, including aerodynamic analysis and structural analysis. This work presents a methodology for the structural optimization of morphing systems utilizing aerodynamic shape optimization results as inputs, in order to assess the relationship between morphing performance and structural stiffness. The methodology is implemented on a baseline morphing S-duct model for which shape optimization has been previously conducted. Structural optimization is conducted using a gradient-based multi-material topology optimization software with multi-phase penalization. While the conclusions of this work indicate that the impact of multiple material optimization in the S-duct case study is minimal, the methodology does provide non-intuitive designs capable of supporting morphing. At the expense of structural stiffness, the methodology is shown to increase morphing performance through the generation of compliant mechanisms. A parameter study conducted on the S-duct model successfully proves the ability of the methodology to assess the trade-offs between structural and morphing performance. By emphasizing morphing performance, mass reductions from 1.75 kg to 0.201 kg were observed at the expense of a 94% reduction in fatigue lifecycle. In addition, through verification with commercial software, the generalizability of the optimization software developed in this work is proven.