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Design optimisation potentially leads to lightweight aircraft structures withlower environmental impact. Due to the high number of design variables andconstraints, these problems are ordinarily solved using gradient-basedoptimisation methods, leading to a local solution in the design space while theglobal space is neglected. Bayesian Optimisation is a promising path towardssample-efficient, global optimisation based on probabilistic surrogate models.While Bayesian optimisation methods have demonstrated their strength forproblems with a low number of design variables, the scalability tohigh-dimensional problems while incorporating large-scale constraints is stilllacking. Especially in aeroelastic tailoring where directional stiffnessproperties are embodied into the structural design of aircraft, to controlaeroelastic deformations and to increase the aerodynamic and structuralperformance, the safe operation of the system needs to be ensured by involvingconstraints resulting from different analysis disciplines. Hence, a globaldesign space search becomes even more challenging. The present study attemptsto tackle the problem by using high-dimensional Bayesian Optimisation incombination with a dimensionality reduction approach to solve the optimisationproblem occurring in aeroelastic tailoring, presenting a novel approach forhigh-dimensional problems with large-scale constraints. Experiments onwell-known benchmark cases with black-box constraints show that the proposedapproach can incorporate large-scale constraints.

High-Dimensional Bayesian Optimisation with Large-Scale Constraints -- An Application to Aeroelastic Tailoring