Graph Neural Network-Guided Aerodynamic Shape Optimization for Conceptual Design of Supersonic Transport Wings

Low-speed stability constraints have historically been difficult to incorporate during the conceptual design phase due to the limited accuracy of low-fidelity simulation methods. This paper presents a graph neural network (GNN)-guided multi-fidelity optimization framework for the aerodynamic shape design of supersonic transport wings at the conceptual level. A GNN-based field prediction surrogate model is integrated into the SUAVE aircraft design environment, enabling real-time, high-fidelity-informed aerodynamic shape optimization across a defined design space. Using a two-point, stability-constrained shape optimization problem, we demonstrate that the GNN-guided workflow produces a design that satisfies both stability and aerodynamic performance requirements, while the traditional vortex lattice method (VLM)- based workflow results in a configuration that violates the intended design objectives. The proposed approach improves the fidelity of aerodynamic analysis and enhances confidence in conceptual design, increasing the likelihood that the resulting configuration will perform well in later design phases.