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.
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.
the Creative Commons Attribution 4.0 License.
Effectively using multifidelity optimization for wind turbine design
National Renewable Energy Laboratory, Boulder, CO, United States
Pietro Bortolotti
National Renewable Energy Laboratory, Boulder, CO, United States
Daniel Zalkind
National Renewable Energy Laboratory, Boulder, CO, United States
Garrett Barter
National Renewable Energy Laboratory, Boulder, CO, United States
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Cited
16 citations as recorded by crossref.
- Multi-fidelity surrogate modeling for the optimization of vertical-axis hydrokinetic turbines via Bayesian methods O. Susam & Ö. Gören https://doi.org/10.1007/s40722-025-00465-y
- Advancing wind turbines through control co-design: An integrative review S. Bayat et al. https://doi.org/10.1016/j.apenergy.2026.127951
- SMT 2.0: A Surrogate Modeling Toolbox with a focus on hierarchical and mixed variables Gaussian processes P. Saves et al. https://doi.org/10.1016/j.advengsoft.2023.103571
- Aerodynamic Design of Wind Turbine Blades Using Multi-Fidelity Analysis and Surrogate Models R. Cardamone et al. https://doi.org/10.3390/ijtpp10030016
- Multiobjective Optimization of Composite Wind Turbine Blade M. Jureczko & M. Mrówka https://doi.org/10.3390/ma15134649
- Control co-design of a floating offshore wind turbine N. Abbas et al. https://doi.org/10.1016/j.apenergy.2023.122036
- Analyzing the impact of aeroelastic model fidelity on control co-design optimization of floating offshore wind turbines R. Behrens de Luna et al. https://doi.org/10.5194/wes-10-3045-2025
- Multi-fidelity gradient-based wind turbine main-shaft assembly optimization with analytical bearing fatigue V. Gupta & A. Nejad https://doi.org/10.1088/1742-6596/3224/5/052023
- Considerations for the global commercialization of floating offshore wind energy A. Robertson et al. https://doi.org/10.1038/s44359-025-00093-7
- Control Co-Design Studies for a 22 MW Semisubmersible Floating Wind Turbine Platform D. Zalkind & P. Bortolotti https://doi.org/10.1088/1742-6596/2767/8/082020
- Structural Design Optimization of Tidal Current Turbine Blades Based on Structural Safety Factors H. Jeong & C. Yang https://doi.org/10.1109/ACCESS.2024.3514102
- A low-fidelity model for the dynamic analysis of full-lattice wind support structures M. Vergassola et al. https://doi.org/10.1016/j.marstruc.2023.103506
- An optimization framework for wind farm layout design using CFD-based Kriging model Z. Wang et al. https://doi.org/10.1016/j.oceaneng.2023.116644
- Stiffness-proportional foundation damping to linearise soil-monopile interaction models for wind turbines A. Tombari et al. https://doi.org/10.1016/j.soildyn.2025.109387
- A multi-fidelity framework for power prediction of wind farm under yaw misalignment Y. Tu et al. https://doi.org/10.1016/j.apenergy.2024.124600
- Non-myopic multipoint multifidelity Bayesian framework for multidisciplinary design F. Di Fiore & L. Mainini https://doi.org/10.1038/s41598-023-48757-3
16 citations as recorded by crossref.
- Multi-fidelity surrogate modeling for the optimization of vertical-axis hydrokinetic turbines via Bayesian methods O. Susam & Ö. Gören https://doi.org/10.1007/s40722-025-00465-y
- Advancing wind turbines through control co-design: An integrative review S. Bayat et al. https://doi.org/10.1016/j.apenergy.2026.127951
- SMT 2.0: A Surrogate Modeling Toolbox with a focus on hierarchical and mixed variables Gaussian processes P. Saves et al. https://doi.org/10.1016/j.advengsoft.2023.103571
- Aerodynamic Design of Wind Turbine Blades Using Multi-Fidelity Analysis and Surrogate Models R. Cardamone et al. https://doi.org/10.3390/ijtpp10030016
- Multiobjective Optimization of Composite Wind Turbine Blade M. Jureczko & M. Mrówka https://doi.org/10.3390/ma15134649
- Control co-design of a floating offshore wind turbine N. Abbas et al. https://doi.org/10.1016/j.apenergy.2023.122036
- Analyzing the impact of aeroelastic model fidelity on control co-design optimization of floating offshore wind turbines R. Behrens de Luna et al. https://doi.org/10.5194/wes-10-3045-2025
- Multi-fidelity gradient-based wind turbine main-shaft assembly optimization with analytical bearing fatigue V. Gupta & A. Nejad https://doi.org/10.1088/1742-6596/3224/5/052023
- Considerations for the global commercialization of floating offshore wind energy A. Robertson et al. https://doi.org/10.1038/s44359-025-00093-7
- Control Co-Design Studies for a 22 MW Semisubmersible Floating Wind Turbine Platform D. Zalkind & P. Bortolotti https://doi.org/10.1088/1742-6596/2767/8/082020
- Structural Design Optimization of Tidal Current Turbine Blades Based on Structural Safety Factors H. Jeong & C. Yang https://doi.org/10.1109/ACCESS.2024.3514102
- A low-fidelity model for the dynamic analysis of full-lattice wind support structures M. Vergassola et al. https://doi.org/10.1016/j.marstruc.2023.103506
- An optimization framework for wind farm layout design using CFD-based Kriging model Z. Wang et al. https://doi.org/10.1016/j.oceaneng.2023.116644
- Stiffness-proportional foundation damping to linearise soil-monopile interaction models for wind turbines A. Tombari et al. https://doi.org/10.1016/j.soildyn.2025.109387
- A multi-fidelity framework for power prediction of wind farm under yaw misalignment Y. Tu et al. https://doi.org/10.1016/j.apenergy.2024.124600
- Non-myopic multipoint multifidelity Bayesian framework for multidisciplinary design F. Di Fiore & L. Mainini https://doi.org/10.1038/s41598-023-48757-3
Saved (final revised paper)
Latest update: 15 Jun 2026
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.
Using highly accurate simulations within a design cycle is prohibitively computationally...
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