The present contribution proposes a Reduced Order Model based multi-fidelity optimization methodology for the design of highly loaded blades in low pressure compressors. Environmental, as well as, economical limitations applied to engine manufacturers make the design of modern turbofans an extremely complex task. A smart compromise has to be found to guarantee both a high efficiency and a high average stage loading imposed for mass reduction constraints, while satisfying stability requirements.
The design of compressor blades, usually involves at the same time a dedicated parametrization set-up in highdimensional space and high-fidelity simulations capturing, at least, efficiency and stability as most impacting phenomena. Despite recent advances in the high-performance computing area, introducing high-fidelity simulations into automated optimization, or even surrogate assisted optimization, loops still stands as a endeavor for engineers. In this framework, the proposed methodology is based on multi-fidelity surrogate models capable of representing the physics at hand in reduced spaces inferred from both precise, albeit costly, high-fidelity simulations and abundant, yet less accurate lower-fidelity data. Finally, we investigate the coupling of the proposed hierarchised multi-fidelity non-intrusive Proper Orthogonal Decomposition based surrogates with an evolutionary algorithm to reduce the number of high-fidelity simulation calls towards the targeted optimum.
Peering inside a cough or sneeze to explain enhanced airborne transmission under dry weather
