Tackling Highly Constrained Design Problems: Efficient Optimisation of a Highly Loaded Transonic Compressor

Abstract

Turbomachinery components are designed to achieve high performances while being exposed to a complex flow environment with varying operating conditions. Whereas the purpose of a new design optimisation is straightforward — obtaining a better design than the already existing one — the actual process itself remains a challenging task, permanently confronted to the dual need to reduce the cycle time and to further integrate complexity and multiple physics. The extensive use of numerical simulations has contributed in a significant way to the design of state-of-the-art blade geometries. To deal with expensive high-fidelity computations, surrogate-based optimisation (SBO) has become an established and recognised approach. In order to be useful within an industrial context, it is crucial that this SBO process is capable of efficiently handling high-dimensional design spaces as well as managing highly constrained design problems. This work presents innovative auto-adaptive surrogates, exploiting a blend of interpolation/regression and classification, implemented in the integrated optimisation platform Minamo. As a demonstrator based on NASA Rotor 37, an aero-mechanical multi-point optimisation has been performed. For a design space with 60 parameters, significant performance gains have been obtained (+4% after 250 evaluations or less than a fortnight’s runtime) while considering over 30 constraints. The proposed SBO approach offers therefore many opportunities for turbomachinery applications tackling highly constrained design problems. Despite the unavoidable curse of dimensionality, the proposed approach is able to efficiently achieve reliable results at a cost that is in line with industrial needs and it provides a conclusive asset in the frame of design specifications evolving along the design cycle.