Preprints
https://doi.org/10.5194/wes-2021-56
https://doi.org/10.5194/wes-2021-56

  26 Jul 2021

26 Jul 2021

Review status: this preprint is currently under review for the journal WES.

Effectively using multifidelity optimization for wind turbine design

John Jasa, Pietro Bortolotti, Daniel Zalkind, and Garrett Barter John Jasa et al.
  • National Renewable Energy Laboratory, Boulder, CO

Abstract. Wind turbines are complex multidisciplinary systems that are challenging to design because of the tightly coupled interactions between different subsystems. Computational modeling attempts to resolve these couplings so we can efficiently explore new wind turbine systems early in the design process. Low-fidelity models are computationally efficient but make assumptions and simplifications that limit the accuracy of design studies, whereas high-fidelity models capture more of the actual physics but with increased computational cost. This paper details the use of multifidelity methods for optimizing wind turbine designs by using information from both low- and high-fidelity models to find an optimal solution at reduced cost. Specifically, a trust-region approach is used with a novel corrective function built from a nonlinear surrogate model. We find that for a diverse set of design problems—with examples given in rotor blade geometry design, wind turbine controller design, and wind power plant layout optimization—the multifidelity method finds the optimal design using 38 %–58 % the computational cost of the high-fidelity-only optimization. The success of the multifidelity method in disparate applications suggests that it could be more broadly applied to other wind energy or otherwise generic applications.

John Jasa et al.

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2021-56', Anonymous Referee #1, 04 Aug 2021 reply

John Jasa et al.

John Jasa et al.

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Short summary
Using highly accurate simulations within a design cycle is prohibitively computationally expensive. We implement and present a multifidelity optimization method and showcase its efficacy using three different case studies. We examine aerodynamic blade design, turbine controls tuning, and a wind plant layout problem. In each case, the multifidelity method finds an optimal design that performs better than those obtained using simplified models, but with less cost than high-fidelity optimization.