Finite element models of whole gas turbine engines, also known as whole engine models (WEMs), which consist of threedimensional solid elements are not commonly used in design optimization studies due to the high computational cost of solving them for many designs. WEMs consisting of two-dimensional shell elements can be a suitable replacement for high-fidelity solid WEMs as they approximate the responses well while being significantly quicker to solve. However, in a surrogate-assisted optimization study, the accumulation of errors in the shell WEM evaluations can result in the construction of a surrogate model that can be somewhat misleading compared to the solid WEM response surface. Such a surrogate model could return promising designs that, when validated using solid WEMs, turn out to be suboptimal or infeasible. A novel approach which combines medial meshing and multi-fidelity surrogate modelling techniques is proposed to increase the feasibility of conducting whole engine optimization studies. We demonstrate the workflow for generating medial meshes on an engine intercasing geometry. The accuracy of medial mesh simulations with respect to solid mesh simulations is evaluated and discussed in the context of their suitability as a source of low-fidelity structural information for multi-fidelity surrogate models. The impact of this combination of techniques is subsequently illustrated using two case studies. The first case study is the optimization of an intermediate compressor casing for minimum mass with constraints on the casing stiffness. The results show that the multi-fidelity approach is able to find optimum designs that are equivalent to the expensive singlefidelity approach of using only solid mesh evaluations but at a significantly lower computational cost. The second case study is the optimization of a whole engine geometry. This case study serves to demonstrate the effectiveness of the multi-fidelity approach for solving realistic design problems.

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Engine subsystem models are not commonly used in design optimization studies as it is computationally expensive to solve these models for a large number of iterations. To reduce the computational cost of such optimizations, a novel multi-fidelity Kriging-based optimization approach is proposed that uses shell FEMs to provide a low-fidelity response and solid FEMs to provide a high-fidelity response. This marks the first time that shell and solid models are used together in a multi-fidelity surrogate modelling approach. The shell FEMs are generated from medial surfaces that are extracted from solid component geometries in a semi-automatic manner. This approach is applied to a case study for optimizing the intercasing subsystem from the CRESCENDO whole engine model. The results show that the optimum design found by the multi-fidelity Kriging approach was on par with the optimum design found by a single-fidelity Kriging approach using only solid FEMs which is more than twice as expensive to run. The shell and solid FEMs were also shown to be well-correlated such that optimization studies employing only the shell FEMs by themselves could generate designs that are feasible with respect to the design constraints imposed on the solid model.