The presented work is part of a research project aimed towards multi-disciplinary robust shape optimization of low pressure turbine (LPT) vane clusters. Multi-disciplinary analysis for vane cluster optimization is used to evaluate design constraints, involving 3D aerodynamic Navier-Stokes simulation, transient thermal analysis, structural analysis and life prediction. The expense of these simulations combined with high-dimensional design space, makes the application of gradient-based or stochastic optimizers inefficient. To overcome these issues, a surrogate-based optimization approach is proposed here. High quality surrogate models are required for accurate description of the constraints with life prediction. Adaptive Global Surrogate-Based Optimizer, based on Gaussian-Process (GP) surrogate models and Expected Improvement infill criteria is employed, which allows to efficiently increase the surrogate quality while approaching the optimal solution at the same time. Additional techniques are introduced to deal with the geometry rebuild failure, as some combinations of the design parameters may produce infeasible geometry. The adaptive optimization method is successfully applied to the multi-disciplinary problem for the vane cluster shape optimization. The comparison of the method performance with a gradient-based optimizer indicates that a much lower number of true simulations is needed by the proposed method to find an optimal design. Successful optimization results shows the ability of the method to handle simulation crashes, caused by geometry rebuild failure.

Low pressure turbine (LPT) rotor discs undergo high thermal and mechanical loads during normal aircraft missions. Therefore, to meet the minimum requirement for life, temperatures and stresses in the disk need to be maintained within certain limits. This is achieved by carefully designing the disk shape and the cooling system. The complexity of this multi-physics problem together with a large number of design parameters require the use of numerical optimization methods for the Secondary Air System (SAS) design. Moreover, possible variations in the boundary conditions due to ambient parameters (e.g. temperatures, pressures) and manufacturing tolerances of the SAS components should be taken into account within the system design and optimization phase. In this paper an application of robust optimization methods for the design of a LPT secondary air system is proposed. The objective is to increase the engine efficiency by minimizing the amount of cooling flow, which is needed to guarantee a minimum required number of life cycles and to keep maximal temperatures within the limits. In order to predict the disks life accurately, transient thermal-structural analysis is used, which is computationally demanding. For this reason, optimization should be performed with a very limited amount of system evaluations. The dimension of the parameter space is reduced through the application of global sensitivity analysis methods by selecting the parameters that most affect the results. Optimization methods are sped up by the use of surrogate models, created over the reduced parameter space, which approximate the objective function and the constraints.

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Multidisciplinary design optimization has great potential to support the turbomachinery development process by improving designs at reduced time and cost. As part of the industrial compressor design process, we seek for a rotor blade geometry that minimizes stresses without impairing the aerodynamic performance. However, the presence of structural mechanics, aerodynamics, and their interdisciplinary coupling poses challenges concerning computational effort and organizational integration. In order to reduce both computation times and the required exchange between disciplinary design teams, we propose an inter- instead of multidisciplinary design optimization approach tailored to the studied optimization problem. This involves a distinction between main and side discipline. The main discipline, structural mechanics, is computed by accurate high-fidelity finite element models. The side discipline, aerodynamics, is represented by efficient low-fidelity models, using Kriging and proper-orthogonal decomposition to approximate constraints and the gas load field as coupling variable. The proposed approach is shown to yield a valid blade design with reasonable computational effort for training the aerodynamic low-fidelity models and significantly reduced optimization times compared to a high-fidelity multidisciplinary design optimization. Especially for expensive side disciplines like aerodynamics, the multi-fidelity interdisciplinary design optimization has the potential to consider the effects of all involved disciplines at little additional cost and organizational complexity, while keeping the focus on the main discipline.