Preprints
https://doi.org/10.5194/wes-2022-95
https://doi.org/10.5194/wes-2022-95
 
20 Oct 2022
20 Oct 2022
Status: this preprint is currently under review for the journal WES.

A Data-Driven Reduced Order Model for Rotor Optimization

Nicholas Peters1,2, Christopher Silva3, and John Ekaterinaris4 Nicholas Peters et al.
  • 1Aerospace Engineer, NASA Ames Research Center, Moffett Field, CA, USA
  • 2Ph.D. Candidate Aerospace Eng., Embry-Riddle Aeron. Univ., Daytona Beach, FL, USA
  • 3Aerospace Engineer, NASA Ames Research Center, Moffett Field, CA, USA
  • 4Prof. Aerospace Eng., Embry-Riddle Aeron. Univ., Daytona Beach, FL, USA

Abstract. For rotor design applications, such as wind turbine rotor or Urban Air Mobility (UAM) rotorcraft and flying car design, there is a significant challenge in quickly and accurately modeling rotors operating in complex turbulent flow fields. One potential path for deriving high-fidelity but low-cost rotor performance predictions is available through the application of data-driven surrogate modeling. In this study, an initial investigation is undertaken to apply a proper orthogonal decomposition (POD) based reduced order model (ROM) for predicting rotor distributed loads. The POD ROM was derived based on computational fluid dynamics (CFD) results and utilized to produce distributed pressure predictions on rotor blades subjected to topology change due to variations in twist and taper ratio. Rotor twist, θ, was varied between 0°, 10°, 20°, and 30° while taper ratio, λ, was varied as 1.0, 0.9, 0.8, and 0.7. For a demonstration of the approach, all rotors consisted of a single blade. The POD ROM was validated for three operation cases; a high pitch or a high thrust rotor in hover, a low pitch or a low thrust rotor in hover, and a rotor in forward flight at a low speed resembling wind turbine operation with wind shear. Results showed highly accurate distributed load predictions could be achieved and the resulting surrogate model can predict loads at a minimal computational cost. The computational cost for the hovering blade surface pressure prediction was reduced from 12 hours on 440 cores required for CFD to a fraction of a second on a single core required for POD. For rotor in forward flight cost was reduced from 20 hours on 440 cores to less than a second on a single core. The POD ROM was used to undergo a design optimization of the rotor such that figure of merit was maximized for hovering rotor cases and the lift to drag effective ratio was maximized in forward flight.

Nicholas Peters et al.

Status: open (until 17 Dec 2022)

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Nicholas Peters et al.

Nicholas Peters et al.

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Short summary
Wind turbines have increasingly been leveraged as a viable approach for obtaining renewable energy. As such, it is essential that engineers have a high-fidelity low-cost approach to model rotor load distributions. In this study, such an approach is proposed. This modeling approach was shown to make high-fidelity predictions at a low computational cost for rotor-distributed pressure loads as rotor geometry varied allowing for an optimization of the rotor to be completed.