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.
Research article
| 
11 May 2022
Research article |
| 11 May 2022
| 11 May 2022
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
Related authors
Jason M. Jonkman, Emmanuel S. P. Branlard, and John P. Jasa
Wind Energ. Sci., 7, 559–571, https://doi.org/10.5194/wes-7-559-2022, https://doi.org/10.5194/wes-7-559-2022, 2022
Short summary
Short summary
This paper summarizes efforts done to understand the impact of design parameter variations in the physical system (e.g., mass, stiffness, geometry, aerodynamic, and hydrodynamic coefficients) on the linearized system using OpenFAST in support of the development of the WEIS toolset to enable controls co-design of floating offshore wind turbines.
Ethan Young, Jeffery Allen, John Jasa, Garrett Barter, and Ryan King
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2022-7, https://doi.org/10.5194/wes-2022-7, 2022
Preprint withdrawn
Short summary
Short summary
In this study, we present ways to measure the phenomenon of wind plant blockage, or the the velocity slowdown upstream from a farm, and carry out turbine layout optimizations to reduce this effect. We find that farm-wide measurements provide a better characterization of blockage compared to more localized measurements and that, in the absence of any constraint on total power output, layouts which minimize the effect of blockage are frequently characterized by streamwise alignment of turbines.
Cory Frontin, Jeff Allen, Christopher J. Bay, Jared Thomas, Ethan Young, and Pietro Bortolotti
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-103, https://doi.org/10.5194/wes-2025-103, 2025
Preprint under review for WES
Short summary
Short summary
Wind farms produce energy and to do so have to occupy a non-trivial amount of space. Understanding how much energy a proposed wind farm will make (and at what cost) is technically challenging, especially when turbines are packed closely together. Plus, there's a key tradeoff in how much space a farm occupies and how cheap the energy it can produce might be: less space means more costly energy. This work shows an novel way to run computational simulations efficiently to understand that tradeoff.
Regis Thedin, Garrett Barter, Jason Jonkman, Rafael Mudafort, Christopher J. Bay, Kelsey Shaler, and Jasper Kreeft
Wind Energ. Sci., 10, 1033–1053, https://doi.org/10.5194/wes-10-1033-2025, https://doi.org/10.5194/wes-10-1033-2025, 2025
Short summary
Short summary
We investigate asymmetries in terms of power performance and fatigue loading on a five-turbine wind farm subject to wake steering strategies. Both the yaw misalignment angle and the wind direction were varied from negative to positive. We highlight conditions in which fatigue loading is lower while still maintaining good power gains and show that a partial wake is the source of the asymmetries observed. We provide recommendations in terms of yaw misalignment angles for a given wind direction.
Pietro Bortolotti, Lee Jay Fingersh, Nicholas Hamilton, Arlinda Huskey, Chris Ivanov, Mark Iverson, Jonathan Keller, Scott Lambert, Jason Roadman, Derek Slaughter, Syhoune Thao, and Consuelo Wells
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-8, https://doi.org/10.5194/wes-2025-8, 2025
Revised manuscript accepted for WES
Short summary
Short summary
This study compares a wind turbine with blades behind the tower (downwind) to the traditional upwind design. Testing a 1.5 MW turbine at NREL’s Flatirons Campus, we measured performance, loads, and noise. Numerical models matched well with observations. The downwind setup showed higher fatigue loads and sound variations but also an unexpected power improvement. Downwind rotors might be a valid alternative for future floating offshore wind applications.
Katarzyna Patryniak, Maurizio Collu, Jason Jonkman, Matthew Hall, Garrett Barter, Daniel Zalkind, and Andrea Coraddu
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-167, https://doi.org/10.5194/wes-2024-167, 2025
Revised manuscript accepted for WES
Short summary
Short summary
This paper studies the Instantaneous Centre of Rotation (ICR) of Floating Offshore Wind Turbines (FOWTs). We present a method for computing the ICR and examine the correlations between the external loading, design features, ICR statistics, motions, and loads. We demonstrate how to apply the new insights to successfully modify the designs of the spar and semisubmersible FOWTs to reduce the loads in the moorings, the tower, and the blades, improving the ultimate strength and fatigue properties.
Kenneth Brown, Pietro Bortolotti, Emmanuel Branlard, Mayank Chetan, Scott Dana, Nathaniel deVelder, Paula Doubrawa, Nicholas Hamilton, Hristo Ivanov, Jason Jonkman, Christopher Kelley, and Daniel Zalkind
Wind Energ. Sci., 9, 1791–1810, https://doi.org/10.5194/wes-9-1791-2024, https://doi.org/10.5194/wes-9-1791-2024, 2024
Short summary
Short summary
This paper presents a study of the popular wind turbine design tool OpenFAST. We compare simulation results to measurements obtained from a 2.8 MW land-based wind turbine. Measured wind conditions were used to generate turbulent flow fields through several techniques. We show that successful validation of the tool is not strongly dependent on the inflow generation technique used for mean quantities of interest. The type of inflow assimilation method has a larger effect on fatigue quantities.
Julian Quick, Ryan N. King, Garrett Barter, and Peter E. Hamlington
Wind Energ. Sci., 7, 1941–1955, https://doi.org/10.5194/wes-7-1941-2022, https://doi.org/10.5194/wes-7-1941-2022, 2022
Short summary
Short summary
Wake steering is an emerging wind power plant control strategy where upstream turbines are intentionally yawed out of alignment with the incoming wind, thereby steering wakes away from downstream turbines. Trade-offs between the gains in power production and fatigue loads induced by this control strategy are the subject of continuing investigation. In this study, we present an optimization approach for efficiently exploring the trade-offs between power and loading during wake steering.
Michael J. LoCascio, Christopher J. Bay, Majid Bastankhah, Garrett E. Barter, Paul A. Fleming, and Luis A. Martínez-Tossas
Wind Energ. Sci., 7, 1137–1151, https://doi.org/10.5194/wes-7-1137-2022, https://doi.org/10.5194/wes-7-1137-2022, 2022
Short summary
Short summary
This work introduces the FLOW Estimation and Rose Superposition (FLOWERS) wind turbine wake model. This model analytically integrates the wake over wind directions to provide a time-averaged flow field. This new formulation is used to perform layout optimization. The FLOWERS model provides a smooth flow field over an entire wind plant at fraction of the computational cost of the standard numerical integration approach.
Jason M. Jonkman, Emmanuel S. P. Branlard, and John P. Jasa
Wind Energ. Sci., 7, 559–571, https://doi.org/10.5194/wes-7-559-2022, https://doi.org/10.5194/wes-7-559-2022, 2022
Short summary
Short summary
This paper summarizes efforts done to understand the impact of design parameter variations in the physical system (e.g., mass, stiffness, geometry, aerodynamic, and hydrodynamic coefficients) on the linearized system using OpenFAST in support of the development of the WEIS toolset to enable controls co-design of floating offshore wind turbines.
Ethan Young, Jeffery Allen, John Jasa, Garrett Barter, and Ryan King
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2022-7, https://doi.org/10.5194/wes-2022-7, 2022
Preprint withdrawn
Short summary
Short summary
In this study, we present ways to measure the phenomenon of wind plant blockage, or the the velocity slowdown upstream from a farm, and carry out turbine layout optimizations to reduce this effect. We find that farm-wide measurements provide a better characterization of blockage compared to more localized measurements and that, in the absence of any constraint on total power output, layouts which minimize the effect of blockage are frequently characterized by streamwise alignment of turbines.
Nikhar J. Abbas, Daniel S. Zalkind, Lucy Pao, and Alan Wright
Wind Energ. Sci., 7, 53–73, https://doi.org/10.5194/wes-7-53-2022, https://doi.org/10.5194/wes-7-53-2022, 2022
Short summary
Short summary
The publication of the Reference Open-Source Controller (ROSCO) provides a controller and generic controller tuning process to the wind energy research community that can perform comparably or better than existing reference wind turbine controllers and includes features that are consistent with industry standards. Notably, ROSCO provides the first known open-source controller with features that specifically address floating offshore wind turbine control.
Ernesto Camarena, Evan Anderson, Josh Paquette, Pietro Bortolotti, Roland Feil, and Nick Johnson
Wind Energ. Sci., 7, 19–35, https://doi.org/10.5194/wes-7-19-2022, https://doi.org/10.5194/wes-7-19-2022, 2022
Short summary
Short summary
The length of rotor blades of land-based wind turbines is currently constrained by logistics. Turbine manufacturers currently propose segmented solutions to overcome these limits, but blade joints come with extra masses and costs. This work investigates an alternative solution, namely the design of ultra-flexible blades that can be transported on rail via controlled bending. The results show that this is a promising pathway to further increasing the size of land-based wind turbines.
Pietro Bortolotti, Nick Johnson, Nikhar J. Abbas, Evan Anderson, Ernesto Camarena, and Joshua Paquette
Wind Energ. Sci., 6, 1277–1290, https://doi.org/10.5194/wes-6-1277-2021, https://doi.org/10.5194/wes-6-1277-2021, 2021
Short summary
Short summary
The length of rotor blades of land-based wind turbines is currently constrained by logistics. Turbine manufacturers currently propose segmented solutions to overcome these limits, but blade joints come with extra masses and costs. This work investigates an alternative solution, namely the design of ultra-flexible blades that can be transported on rail via controlled bending. The results show that this is a promising pathway for further increasing the size of land-based wind turbines.
Helena Canet, Pietro Bortolotti, and Carlo L. Bottasso
Wind Energ. Sci., 6, 601–626, https://doi.org/10.5194/wes-6-601-2021, https://doi.org/10.5194/wes-6-601-2021, 2021
Short summary
Short summary
The paper analyzes in detail the problem of scaling, considering both the steady-state and transient response cases, including the effects of aerodynamics, elasticity, inertia, gravity, and actuation. After a general theoretical analysis of the problem, the article considers two alternative ways of designing a scaled rotor. The two methods are then applied to the scaling of a 10 MW turbine of 180 m in diameter down to three different sizes (54, 27, and 2.8 m).
Cited articles
Abbas, N. J., Zalkind, D. S., Pao, L., and Wright, A.: A reference open-source controller for fixed and floating offshore wind turbines, Wind Energ. Sci., 7, 53–73, https://doi.org/10.5194/wes-7-53-2022, 2022. a
Abdallah, I., Lataniotis, C., and Sudret, B.: Parametric hierarchical kriging
for multi-fidelity aero-servo-elastic simulators – Application to extreme
loads on wind turbines, Probabil. Eng. Mech., 55, 67–77, 2019. a
Alexandrov, N. M., Dennis, J., Lewis, R. M., and Torczon, V.: A trust-region
framework for managing the use of approximation models in optimization,
Struct. Optimiz., 15, 16–23, 1998. a
Alexandrov, N. M., Lewis, R. M., Gumbert, C. R., Green, L. L., and Newman, P. A.: Approximation and model management in aerodynamic optimization with
variable-fidelity models, J. Aircraft, 38, 1093–1101, 2001. a
Allen, C., Viselli, A., Dagher, H., Goupee, A., Gaertner, E., Abbas, N., Hall, M., and Barter, G.: Definition of the UMaine VolturnUS-S Reference
Platform Developed for the IEA Wind 15-Megawatt Offshore Reference Wind
Turbine, Tech. Rep. NREL/TP-76773, International Energy Agency, https://doi.org/10.2172/1660012, 2020. a
Ashuri, T., Zaaijer, M. B., Martins, J. R. R. A., van Bussel, G. J. W., and van Kuik, G. A. M.: Multidisciplinary Design Optimization of Offshore Wind
Turbines for Minimum Levelized Cost of Energy, Renew. Energy, 68, 893–905, https://doi.org/10.1016/j.renene.2014.02.045, 2014. a
Baker, N. F., Stanley, A. P., Thomas, J. J., Ning, A., and Dykes, K.: Best
practices for wake model and optimization algorithm selection in wind farm
layout optimization, in: AIAA Scitech 2019 Forum, 7–11 January 2019,
San Diego, California, p. 0540, https://doi.org/10.2514/6.2019-0540, 2019. a
Barlas, T., Ramos-García, N., Pirrung, G. R., and González Horcas, S.: Surrogate-based aeroelastic design optimization of tip extensions on a modern 10 MW wind turbine, Wind Energ. Sci., 6, 491–504, https://doi.org/10.5194/wes-6-491-2021, 2021. a
Bir, G. S.: User's Guide to MBC3: Multi-Blade Coordinate Transformation Code
for 3-Bladed Wind Turbine, Tech. Rep. NREL/TP-500-44327, National Renewable
Energy Laboratory, https://www.nrel.gov/docs/fy10osti/44327.pdf (last access: 26 April 2022), 2010. a
Bortolotti, P., Bottasso, C. L., and Croce, A.: Combined preliminary–detailed design of wind turbines, Wind Energ. Sci., 1, 71–88,
https://doi.org/10.5194/wes-1-71-2016, 2016. a
Bortolotti, P., Dixon, K., Gaertner, E., Rotondo, M., and Barter, G.: An
efficient approach to explore the solution space of a wind turbine rotor design process, J. Phys.: Conf. Ser., 1618, 042016, https://doi.org/10.1088/1742-6596/1618/4/042016, 2020. a
Bouhlel, M. A., Hwang, J. T., Bartoli, N., Lafage, R., Morlier, J., and
Martins, J. R. R. A.: A Python surrogate modeling framework with derivatives,
Adv. Eng. Softw., 135, 102662, https://doi.org/10.1016/j.advengsoft.2019.03.005, 2019. a
Churchfield, M., Wang, Q., Scholbrock, A., Herges, T., Mikkelsen, T., and
Sjöholm, M.: Using high-fidelity computational fluid dynamics to help
design a wind turbine wake measurement experiment, J. Phys.: Conf. Ser., 753, 032009, https://doi.org/10.1088/1742-6596/753/3/032009, 2016. a
Cressie, N.: Spatial prediction and ordinary kriging, Math. Geol., 20,
405–421, 1988. a
Fischer, G. R., Kipouros, T., and Savill, A. M.: Multi-objective optimisation
of horizontal axis wind turbine structure and energy production using aerofoil and blade properties as design variables, Renew. Energy, 62, 506–515, 2014. a
Fleming, P., Gebraad, P., van Wingerden, J.-W., Lee, S., Churchfield, M.,
Scholbrock, A., Michalakes, J., Johnson, K., and Moriarty, P.: SOWFA
super-controller: A high-fidelity tool for evaluating wind plant control
approaches, Tech. rep., NREL – National Renewable Energy Lab., Golden, CO,
USA, https://www.nrel.gov/docs/fy13osti/57175.pdf (last access: 26 April 2022), 2013. a
Forrester, A. I. and Keane, A. J.: Recent advances in surrogate-based
optimization, Prog. Aerosp. Sci., 45, 50–79, 2009. a
Forrester, A. I., Sóbester, A., and Keane, A. J.: Multi-fidelity
optimization via surrogate modelling, P. Roy. Soc. A, 463, 3251–3269, 2007. a
Fuglsang, P. and Madsen, H. A.: Optimization method for wind turbine rotors,
J. Wind Eng. Indust. Aerodynam., 80, 191–206, 1999. a
Gaertner, E., Rinker, J., Sethuraman, L., Zahle, F., Anderson, B., Barter, G., Abbas, N., Meng, F., Bortolotti, P., Skrzypinski, W., Scott, G., Feil, R., Bredmose, H., Dykes, K., Sheilds, M., Allen, C., and Viselli, A.: IEA Wind TCP Task 37: Definition of the IEA 15-Megawatt Offshore Reference Wind Turbine, Tech. Rep. NREL/TP-75698, International Energy Agency, https://doi.org/10.2172/1603478, 2020. a
Gavin, H. P.: Frame3DD. Static and Dynamic Structural Analysis of 2D and 3D Frames, version 0.20140514+, http://frame3dd.sourceforge.net/ (last access: 26 April 2022), 2014. a
Giguere, P. and Selig, M.: Blade geometry optimization for the design of wind
turbine rotors, in: 2000 ASME Wind Energy Symposium, January 2000, Reno, NV, USA, p. 45, https://doi.org/10.2514/6.2000-45, 2000. a
Gill, P. E., Murray, W., and Saunders, M. A.: SNOPT: An SQP algorithm for
large-scale constrained optimization, SIAM Rev., 47, 99–131, 2005. a
Gray, J. S., Hwang, J. T., Martins, J. R. R. A., Moore, K. T., and Naylor, B. A.: OpenMDAO: An open-source framework for multidisciplinary design, analysis, and optimization, Struc. Multidisciplin. Optimiz., 59, 1075–1104, https://doi.org/10.1007/s00158-019-02211-z, 2019. a
Jasa, J., Bortolotti, P., Zalkind, D., and Barter, G.: Data and models for “Effectively using multifidelity optimization for wind turbine design”
Zenodo [code], https://doi.org/10.5281/zenodo.6109699, 2022. a
Jones, D. R., Schonlau, M., and Welch, W. J.: Efficient global optimization of expensive black-box functions, J. Global Optimiz., 13, 455–492, 1998. a
Jonkman, J., Butterfield, S., Musial, W., and Scott, G.: Definition of a 5-MW
reference wind turbine for offshore system development, Tech. rep., NREL – National Renewable Energy Lab., Golden, CO, USA, https://doi.org/10.2172/947422, 2009. a
Jonkman, J. M. and Jonkman, B. J.: FAST modularization framework for wind
turbine simulation: full-system linearization, J. Phys.: Conf. Ser., 753, 082010, https://doi.org/10.1088/1742-6596/753/8/082010, 2016. a
Kennedy, M. C. and O'Hagan, A.: Predicting the output from a complex computer
code when fast approximations are available, Biometrika, 87, 1–13, 2000. a
Khan, S. A. and Rehman, S.: Iterative non-deterministic algorithms in on-shore wind farm design: A brief survey, Renew. Sustain. Energ. Rev., 19, 370–384, 2013. a
King, J., Fleming, P., King, R., Martínez-Tossas, L. A., Bay, C. J., Mudafort, R., and Simley, E.: Control-oriented model for secondary effects of wake steering, Wind Energ. Sci., 6, 701–714, https://doi.org/10.5194/wes-6-701-2021, 2021. a, b, c
Koziel, S. and Leifsson, L.: Surrogate-based modeling and optimization,
Springer, https://doi.org/10.1007/978-1-4614-7551-4, 2013. a
Lambe, A. B. and Martins, J. R. R. A.: Extensions to the Design Structure
Matrix for the Description of Multidisciplinary Design, Analysis, and
Optimization Processes, Struct. Multidisciplin. Optimiz., 46, 273–284, https://doi.org/10.1007/s00158-012-0763-y, 2012. a
Martínez-Tossas, L. A., Annoni, J., Fleming, P. A., and Churchfield, M. J.: The aerodynamics of the curled wake: a simplified model in view of flow control, Wind Energ. Sci., 4, 127–138, https://doi.org/10.5194/wes-4-127-2019, 2019. a
McWilliam, M. K., Zahle, F., Pavese, C., and Blasques, J. P.: Multi-fidelity
optimization of horizontal axis wind turbines, in: 35th Wind Energy Symposium, 9–13 January 2017, Grapevine, Texas, p. 1846,
https://doi.org/10.2514/6.2017-1846, 2017. a, b, c, d
McWilliam, M. K., Zahle, F., Dykes, K., Bortolotti, P., Ning, A., Gaertner, E., Macquart, T., Merz, K., and Ruiz, A. I.: IEA Wind Energy Task 37-System
Engineering-Aerodynamic Optimization Case Study, in: AIAA Scitech 2021 Forum,
p. 1412, https://doi.org/10.2514/6.2021-1412, 2021. a
Moriarty, P. J. and Hansen, A.: AeroDyn Theory Manual, Tech. Rep.
NREL/TP-500-36881, National Renewable Energy Laboratory,
https://www.nrel.gov/docs/fy05osti/36881.pdf (last access: 26 April 2022), 2005. a
Ning, A. and Petch, D.: Integrated design of downwind land-based wind turbines using analytic gradients, Wind Energy, 19, 2137–2152, https://doi.org/10.1002/we.1972, 2016. a
Ning, S. A.: A simple solution method for the blade element momentum equations with guaranteed convergence, Wind Energy, 17, 1327–1345,
https://doi.org/10.1002/we.1636, 2014. a
Ning, S. A., Damiani, R., and Moriarty, P. J.: Objectives and constraints for
wind turbine optimization, J. Solar Energ. Eng., 136, 041010, https://doi.org/10.1115/1.4027693, 2014. a
NREL: FLORIS, Version 2.2.5, GitHub [code], https://github.com/NREL/floris (last access: 26 April 2022), 2021a. a
NREL: OpenFAST, v2.5.0, GitHub [code], https://github.com/OpenFAST/openfast (last access: 26 April 2022), 2021b. a
NREL: WEIS, v1.0, GitHub [code], https://github.com/WISDEM/WEIS (last access: 26 April 2022), 2021c. a
NREL: WISDEM, v3.2.0, GitHub [code], https://github.com/WISDEM/WISDEM (last access: 26 April 2022), 2021d. a
Park, J. and Law, K. H.: A Bayesian optimization approach for wind farm power
maximization, in: Smart Sensor Phenomena, Technology, Networks, and Systems
Integration 2015, vol. 9436, International Society for Optics and Photonics, p. 943608, https://doi.org/10.1117/12.2084184, 2015. a
Peherstorfer, B., Willcox, K., and Gunzburger, M.: Survey of Multifidelity
Methods in Uncertainty Propagation, Inference, and Optimization, SIAM Rev., 60, 550–591, https://doi.org/10.1137/16M1082469, 2018. a, b
Poon, N. M. K. and Martins, J. R. R. A.: An Adaptive Approach to Constraint
Aggregation Using Adjoint Sensitivity Analysis, Struct. Multidisciplin. Optimiz., 34, 61–73, https://doi.org/10.1007/s00158-006-0061-7, 2007. a
Pourrajabian, A., Afshar, P. A. N., Ahmadizadeh, M., and Wood, D.:
Aero-structural design and optimization of a small wind turbine blade, Renew. Energy, 87, 837–848, 2016. a
Quick, J., Hamlington, P. E., King, R., and Sprague, M. A.: Multifidelity
uncertainty quantification with applications in wind turbine aerodynamics,
in: AIAA Scitech 2019 Forum, 7–11 January 2019, San Diego, California, p. 0542, https://doi.org/10.2514/6.2019-0542, 2019. a
Rahbari, O., Vafaeipour, M., Fazelpour, F., Feidt, M., and Rosen, M. A.:
Towards realistic designs of wind farm layouts: Application of a novel
placement selector approach, Energ. Convers. Manage., 81, 242–254, 2014. a
Robinson, T. D., Eldred, M. S., Willcox, K. E., and Haimes, R.: Surrogate-Based Optimization Using Multifidelity Models with Variable Parameterization and Corrected Space Mapping, AIAA J., 46, 2814–2822,
https://doi.org/10.2514/1.36043, 2008. a
Samorani, M.: The wind farm layout optimization problem, in: Handbook of wind
power systems, Springer, 21–38, https://doi.org/10.1007/978-3-642-41080-2_2, 2013.
a
Shaler, K., Branlard, E., and Platt, A.: OLAF User’s Guide and Theory
Manual, Tech. Rep. NREL/TP-5000-75959, National Renewable Energy Laboratory,
https://doi.org/10.2172/1659853, 2020. a
Sprague, M. A., Ananthan, S., Vijayakumar, G., and Robinson, M.: ExaWind: A
multifidelity modeling and simulation environment for wind energy, J. Phys.: Conf. Ser., 1452, 012071, https://doi.org/10.1088/1742-6596/1452/1/012071, 2020. a
Stanley, A. P. J. and Ning, A.: Massive simplification of the wind farm layout optimization problem, Wind Energ. Sci., 4, 663–676, https://doi.org/10.5194/wes-4-663-2019, 2019. a
Viana, F. A., Haftka, R. T., and Watson, L. T.: Efficient global optimization
algorithm assisted by multiple surrogate techniques, J. Global Optimiz., 56, 669–689, 2013. a
Yu, X., Zhang, W., Zang, H., and Yang, H.: Wind power interval forecasting
based on confidence interval optimization, Energies, 11, 3336, https://doi.org/10.3390/en11123336, 2018. a
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...
Altmetrics
Final-revised paper
Preprint