No abstract

This paper presents a methodology for building multi-fidelity surrogate models based on Non-Intrusive Proper Orthogonal Decomposition. The proposed strategy aims at fusing multiple fidelity levels of simulation to improve the quality of surrogate models exploited in automated optimization loops of industrial-scale problems. A proof of concept is then given on a mathematical toy example which illustrates the ability of the proposed method to significantly reduce the overall computation cost. A 3D industrial study of a 1.5 stage booster is then presented to address the scaling capability of the proposed methodology.

No abstract