*High-fidelity* *numerical* *simulations have already contributed in a significant way to the emergence of state-of-the-art* *turbomachinery components* *but are* *still sparingly exploited* *due to their cost. It* *is hence still relevant to continue the investigations on low-fidelity tools and their combination with higher fidelity methodologies especially when breakthrough technologies are studied. Multi-fidelity approaches are key* *in* *supporting* *turbomachinery engineers to quickly decide on the relevance of an innovative* *concept,* *and* *to* *accelerate design and optimisation processes.*

*This paper describes a low-noise propeller design methodology developed during the IRON project (Clean Sky 2 initiative, Regional Aircraft Innovative Aircraft Development Platform). This methodology is driven by Calypso, a platform developed by Cenaero, which integrates machine learning strategies and multi-fidelity aerodynamic and acoustic solvers. Two key elements of this framework are detailed and validated in this paper. The first one is a low-fidelity tool that offers a fast estimation of the steady aerodynamic performance of a propeller blade using a lifting-surface method. This methodology is assessed against the results of high-fidelity RANS simulations. The second one is a far-field tonal noise prediction solver, relying among others on an acoustic propagation performed in the time domain using Farassat’s formulation 1A of the Ffowcs-Williams and Hawkings equation. Noise source definition can be either obtained after a CFD computation or after the aforementioned low-fidelity aerodynamic evaluation.*

*The low-fidelity and high-fidelity aero-acoustic methodologies have been applied on a database of 600 samples. It is shown how the data mining strategies available within Calypso help to astutely combine both levels of fidelity within an optimisation process in order to quickly and efficiently explore a large design space, and to identify the key design parameters as early as possible in the design process.*


Multidisciplinary Design of a Low-Noise Propeller: Part I \u2014 Multi-Fidelity Design Methodology Leveraging on Acoustic Drivers

Division / Sponsor:
International Gas Turbine Institute (IGTI)

Paper Proceedings: https://asmedigitalcollection.asme.org/GT/GT2020/volume/84058

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