Effectively using multifidelity optimization  for wind turbine design

Jasa, John; Bortolotti, Pietro; Zalkind, Daniel; Barter, Garrett

doi:https://doi.org/10.5194/wes-7-991-2022

Articles | Volume 7, issue 3

https://doi.org/10.5194/wes-7-991-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/wes-7-991-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 7, issue 3

Research article

|

11 May 2022

Research article |

| 11 May 2022

Effectively using multifidelity optimization for wind turbine design

John Jasa, Pietro Bortolotti, Daniel Zalkind, and Garrett Barter

Model code and software

Data and models for "Effectively using multifidelity optimization for wind turbine design" J. Jasa, P. Bortolotti, D. Zalkind, and G. Barter https://doi.org/10.5281/zenodo.6109699

ROSCO, Version 2.1.1 NREL https://github.com/NREL/rosco

FLORIS, Version 2.2.5 NREL https://github.com/NREL/floris

OpenFAST, v2.5.0 NREL https://github.com/OpenFAST/openfast

WEIS, v1.0 NREL https://github.com/WISDEM/WEIS

WISDEM, v3.2.0 NREL https://github.com/WISDEM/WISDEM

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 at a lower cost than high-fidelity optimization.