Computational Framework for Aerodynamic-Structural Coupling in Aircraft Conceptual Design: A Surrogate Model-Based Approach

Traditional optimization methods, which require high-fidelity simulations by relying on computational fluid dynamics (CFD) and finite element analysis (FEA) methods, have significant computational need and require lengthy running time, thus limiting their application in early design phases. To bypass these shortcomings, current paper demonstrates a new optimization model, AeroStruct-Opt, which significantly reduces the computational expenses, but still allows keeping the design accuracy. The paradigm uses Adaptive Multi-Fidelity Surrogate Fusion (AMFSF), which combines high-fidelity CFD data with a low-fidelity method, namely the Vortex Lattice Method (VLM) The low-fidelity models used in the first design sweep mean that overall exploration of the design space can be done in a relatively short duration, but the high-fidelity data ranks the predictions as the optimisation progresses to a convergence. They make use of Dynamic Trade-off Evolution Optimization (DTEO) to alleviate the weight age of aerodynamic and structural goals as the optimisation path progresses, producing Paretooptimal solutions. The experimental findings show that the estimated methodology decreases the degree of computations conducted by approximately 30% as compared to conventional techniques without deviating the aerodynamic and structural performance indicators by more than 5 %. The result is highly optimised aircraft design with much lower computational requirements due to more efficient, lightweight, and high-performance aircraft designs being developed. The Special Issue theme by advancing surrogate-driven aero-structural optimization to enhance autonomous air system performance within multi-domain navigation and distributed control environments. It supports autonomous navigation and distributed control by enabling efficient aero-structural optimization for next-generation air systems across multi-domain environments.