In the past decades, most of the research studies on airfoil shape design and optimization were focused on high Reynolds number airfoils. However, low Reynolds number airfoils have attracted significant attention nowadays due to their vast applications, ranging from micro-aerial vehicles (MAVs) to small-scale unmanned aerial vehicles. For low Reynolds number airfoils, the unsteady effects caused by boundary layer separation cannot be neglected. In this paper, we present an aerodynamic shape optimization framework for low Reynolds number airfoil that we developed based on the unsteady laminar N–S equation and the adjoint method. Finally, using the developed framework, we performed a test case with NACA0012 airfoil as a baseline configuration and the inverse of lift to drag ratio as the cost function. The optimization was carried out at Re = 10,000 and Ma = 0.2. The results demonstrate the effectiveness of the framework.

In this paper, we developed a GPU parallelized 3D mesh deformation based on the radial basis function (RBF) interpolation using NVIDIA CUDA C++. The RBF mesh deformation method interpolates displacements of the boundary nodes to the whole mesh, which can handle large mesh deformations caused by translations, rotations, and deformations. However, the computational performance of RBF mesh deformation depends on the quantity of grids. For 3D mesh deformation, especially for mesh with large number of boundary nodes, RBF interpolation has been verified computationally intensive. By introducing GPU acceleration, the computational performance of RBF mesh deformation code improved significantly. Several benchmark test cases were carried out to show the feasibility and efficiency of GPU parallelization. In summary, the GPU parallelized RBF interpolation shows the potential to become an alternative way to deal with 3D mesh deformation problems in an efficient way.

In actual flight, aircraft rarely operate under a single design condition; multiple flight states must be considered to meet performance requirements. With the push for green and low-carbon aviation, there is growing demand for high-performance, fuel-efficient aircraft. This study focuses on the Blended Wing Body (BWB) configuration. To address large-scale design variables and multiple constraints, a discrete adjoint-based aerodynamic optimization method is developed, improving computational efficiency and reducing cost.The optimization results show reduced drag coefficients across various flight conditions and enhanced drag divergence performance. The robustness of the multi-point optimization approach is validated, confirming its ability to improve aircraft performance across different states. The proposed method is practical and provides an effective reference for aerodynamic design of BWB aircraft.