Efficient surrogate-based robust design optimization method

Reduction of emissions, weight, and vibrations while increasing efficiency are goals for the design of advanced civil aircraft engines in order to fulfil the next generation of emission norms, increase flight comfort and reduce operational costs. A popular approach consists of careful optimization of existing architectures, when each component of a jet engine being improved. Improvement and automation of the multi-disciplinary simulation process combined with optimization and optimization under uncertainty techniques should bring considerable reduction of development time, while improving the design. This motivated the development and implementation of efficient optimization and optimization under uncertainty methods, capable of solving expensive engineering problems within a very limited amount of system evaluations. In this work, robustness of a design objective together with reliability with respect to design constraints are requested simultaneously in order to consider the design as robust, leading to the concept of Reliability-Based Robust Design Optimization. Special attention is paid to efficient adaptive surrogate-based methods, aimed at solving computationally demanding engineering problems. Synergy of developed computational simulation process and proposed surrogate-based methods enables a multi-disciplinary reliability-based robust design optimization of the low pressure turbine vane cluster.