Preprints
https://doi.org/10.5194/wes-2023-10
https://doi.org/10.5194/wes-2023-10
21 Feb 2023
 | 21 Feb 2023
Status: this preprint was under review for the journal WES but the revision was not accepted.

Aeroelastic Tailoring of Wind Turbine Rotors Using High-Fidelity Multidisciplinary Design Optimization

Marco Mangano, Sicheng He, Yingqian Liao, Denis-Gabriel Caprace, Andrew Ning, and Joaquim R. R. A. Martins

Abstract. Reducing the cost of energy from wind power is a critical step towards decarbonizing the electric grid and mitigating climate change effects. Computational models are crucial in understanding the complex, multiphysics interactions of modern, highly flexible wind turbine rotors. When coupled with numerical optimization, such models provide an efficient way to explore the design space. We propose a high-fidelity aerostructural framework that couples computational fluid dynamics with 5 computational structural mechanics to analyze and optimize wind turbines. The framework uses a gradient-based optimization strategy with gradients efficiently computed using a coupled-adjoint approach. We optimize a benchmark utility-scale wind turbine rotor and explore its trade-offs between steady-state aerodynamic efficiency and structural weight. The optimizations account for a representative below-rated operating condition and use more than 100 structural and geometric design variables. The monolithic approach we propose is compared with a loosely-coupled optimization strategy used for the structural sizing 10 of the baseline rotor layout. We then discuss specific optimized blade features and a broader design trade space exploration between extracted torque and rotor mass. Optimized layouts increase the torque by up to 14 % and reduce the mass by up to 9 % or reduce the mass by up to 27 % with the same torque output compared to the baseline blade. The results demonstrate the benefits of optimizing a tightly-coupled aerostructural model and reveal additional insights that high-fidelity analysis provide to complement conventional design approaches.

Marco Mangano, Sicheng He, Yingqian Liao, Denis-Gabriel Caprace, Andrew Ning, and Joaquim R. R. A. Martins

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on wes-2023-10', Georg Raimund Pirrung, 28 Feb 2023
    • AC1: 'Reply on CC1', Marco Mangano, 17 Apr 2023
  • RC1: 'Comment on wes-2023-10', Anonymous Referee #1, 09 Mar 2023
    • AC2: 'Reply on RC1', Marco Mangano, 18 Apr 2023
  • RC2: 'Comment on wes-2023-10', Pietro Bortolotti, 18 Mar 2023
    • AC3: 'Reply on RC2', Marco Mangano, 18 Apr 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on wes-2023-10', Georg Raimund Pirrung, 28 Feb 2023
    • AC1: 'Reply on CC1', Marco Mangano, 17 Apr 2023
  • RC1: 'Comment on wes-2023-10', Anonymous Referee #1, 09 Mar 2023
    • AC2: 'Reply on RC1', Marco Mangano, 18 Apr 2023
  • RC2: 'Comment on wes-2023-10', Pietro Bortolotti, 18 Mar 2023
    • AC3: 'Reply on RC2', Marco Mangano, 18 Apr 2023
Marco Mangano, Sicheng He, Yingqian Liao, Denis-Gabriel Caprace, Andrew Ning, and Joaquim R. R. A. Martins
Marco Mangano, Sicheng He, Yingqian Liao, Denis-Gabriel Caprace, Andrew Ning, and Joaquim R. R. A. Martins

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Short summary
High-fidelity MDO enables more effective system design than conventional approaches. MDO can shorten the wind turbine design cycle and reduce the cost of energy. We present a first-of-its-kind high-fidelity aerostructural optimization study of a turbine rotor using a coupled CFD-CSM solver. We simultaneously improve the rotor aerodynamic efficiency and reduce the mass of a rotor of a 10 MW wind turbine using 100+ design variables. We discuss the results with unprecedented detail.
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