Towards surrogate-based aero-structural design optimization of an Unmanned Aerial Vehicle

Abstract

In order to be able to assess also unconventional aircraft configurations, aircraft designers need to take into account physics-based analyses even during the early design stages. This highly multidisciplinary task requires the contributions and expertise of several different disciplinary specialists. This also applies to unmanned aerial vehicles, where improvements in performance yield a tactical advantage. In this paper, a partitioned design optimization process is presented, for the OptiMALE UAV configuration, originally introduced during the AeroStruct project and was further investigated during the AGILE project. The optimization will couple panel method aerodynamics and structural sizing to find the design with the maximum range. The process is set up in a modular fashion, using common data models as interfaces. The initial design is provided in the Common Parametric Aircraft Configuration Schema (CPACS), and serves as common input for the disciplinary model generators. The multidisciplinary analysis (MDA) process itself is implemented in Python as a Gauss-Seidel fixed point iteration, using comprehensive interfaces to the disciplinary analysis tools. The structural analysis and sizing is performed on a beam and shell model. For the aerodynamic analysis, a 3D potential method for subsonic flow applying the Green’s function method to the small perturbation potential flow equation after Morino has been implemented. The loads resulting from the converged MDA are used as inputs for a sizing optimization of the wing structural components using Lagrange. Finally, a mission simulation is performed using the updated massed to yield the range of the design. The optimization will be implemented in two steps. First, a design of experiments is performed on the wing design variables. Kriging is used to construct a metamodel from the DOE results, which provides gradients for a subsequent gradient-based optimization.