Robust optimization design of a blended wing-body drone considering influence of propulsion system

Abstract

In contrast to conventional configurations, blended wing-body drones exhibit a pronounced coupling between their aerodynamic and propulsion system. While this configuration significantly enhances aerodynamic efficiency, perturbations in flight conditions substantially influence aerodynamic performance. To fully exploit the performance benefits inherent in this configuration, this paper integrates the calculation of the total pressure recovery coefficient and distortion coefficient into the flow field solution and achieves the gradient evaluation of these parameters. This allows the effects of the propulsion system to be considered in the gradient-based optimization. Additionally, utilizing the Gradient-enhanced polynomial chaos expansion (GPCE) method, we construct statistical moment related to the mean and variance and analytically compute the gradients of the moment with respect to the design variables. Consequently, a gradient-based uncertainty optimization framework that accounts for the effects of the propulsion system is established. The framework can accommodate large-scale deterministic design variables and several uncertain parameters. Using this framework, both deterministic and uncertainty-based optimizations that consider the effects of the propulsion system are performed. The objective functions include statistical moments accounting for flight conditions uncertainties. The comparison reveals a 11.89% reduction in statistical moment with robust optimization, highlighting the efficacy of the framework in future robust design optimization of propulsion-airframe integration.