Multidisciplinary Optimization in Turbomachinery Design

This paper investigates particular aspects of performing multidisciplinary optimizations in different turbomachinery design steps. The differences in the optimization approaches and used methods in preliminary and final design steps are shown. An optimization environment is developed, which supports multidisciplinary turbomachinery design. The general concept and components are explained. Some of the implemented methods and tools, which are used particularly in multidisciplinary optimization, are presented. Examples show the potential of the proposed methods. Pareto-optimization enables multi-objective optimizations, very important in preliminary design steps. Various objectives are optimized concurrently. An entire set of Pareto-optimal solutions, design variants, can be computed. Response surfaces promise to accelerate the entire design process. They are able to approximate computational expensive solvers within a fraction of their original computing time. Two different schemes, polynomial approximations and neural networks, are presented. Using different examples, both methods are compared. In final turbomachinery design steps, 3D blade design optimizations need quickly converging optimization algorithms treating one single aerodynamic objective. The other involved disciplines lead to further constraints. Extensions for using the optimization environment in such 3D blade optimizations are presented.