Aerostructural Design of a Laminar Dry Wing for Hydrogen Aircraft using Multidisciplinary Bayesian Optimization

Exploring the potential of using hydrogen as energy carrier is in the current focus of research into sustainable aviation. The absence of fuel from the wing constitutes an important feature of hydrogen aircraft as opposed to their kerosene-based counterparts, potentially paving a way to unexplored design boundaries. The midfidelity preliminary design of such a ’dry’ wing for a medium-range hydrogen aircraft, operating at Mach 0.78 is undertaken in this work. A carbon-fibre reinforced plastic (CFRP) wing equipped with a hybrid laminar flow control (HLFC) suction system is assumed for enhanced weight and viscous drag reductions, thereby requiring a holistic aerostructural design of a composite laminar dry wing. The aerodynamics is modelled with a conical flow 2.75D approach using Euler-boundary layer equations, while the structural sizing is performed using linear finite element formulation on a structural shell model. Considering the aerostructural design variables for such a wing, namely the planform geometry, the airfoil profiles, the lift distribution and the suction distributions led to a 116 dimensional multidisciplinary optimization problem, which had to be resolved in the absence of any gradient information. The application of Bayesian optimization in synergy with a modified individual discipline feasible (IDF) architecture for successfully overcoming a huge challenge in terms of performing a highdimensional gradient-free wing design is the core focus of this study. The resulting laminar wing exhibits a fuel mass saving of 8.8% in comparison to an optimized turbulent wing with robust aerodynamic performance within a suitable design range obtained via multi-point optimization.