28th AIAA/CEAS Aeroacoustics 2022 Conference 2022
DOI: 10.2514/6.2022-3073
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Optimisation of Propellers with Noise-Based Constraints Including a Deep Learning Method for Aerofoil Prediction

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Cited by 6 publications
(4 citation statements)
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“…At more stringent (lower) SPL constraints there is a greater difference between the optimal propellers with a fixed profile compared to a profile that was able to change as a design variable in the optimisation. More details on these optimisation studies can be found in [5].…”
Section: A Optimisation With Tonal Acoustic Constraintsmentioning
confidence: 99%
See 1 more Smart Citation
“…At more stringent (lower) SPL constraints there is a greater difference between the optimal propellers with a fixed profile compared to a profile that was able to change as a design variable in the optimisation. More details on these optimisation studies can be found in [5].…”
Section: A Optimisation With Tonal Acoustic Constraintsmentioning
confidence: 99%
“…They were able to include most aspects of the propeller geometry in the optimisations performed, however the sectional shape of the blade remained fixed. A previous study by the current authors [5] used BEMT and the Hanson acoustic method for a gradient based optimisation framework that included the aerofoil section by using a data-driven method to predict aerofoil performance and adjoint. In that method only the propeller tone was predicted and used as a constraint.…”
Section: Introductionmentioning
confidence: 99%
“…Due to the recent developments of MAVs, MAV rotor optimizations have been carried out [64][65][66][67][68][69][70][71]. Serré et al [67] used BEMT simulations coupled with the Farassat formulation-1A and semi-empirical broadband noise models to predict hover efficiency, tonal noise, and broadband noise, respectively.…”
Section: Introductionmentioning
confidence: 99%
“…Since BEMT simulations (used in [67][68][69]) do not predict the aerodynamic blade vortex interaction and the blade-wake interaction, the need for more accurate optimizations has pushed the authors to perform multi-objective optimizations using middle fidelity, but with a relatively fast, steady non-linear vortex lattice method (S-NVLM) code [20,73] coupled to a Farassat formulation-1A code. Furthermore, the optimization process includes manufacturing and operational constraints which render present results more useful in practice.…”
Section: Introductionmentioning
confidence: 99%