Surrogate-based design optimization of dual-rotor wind turbines using steady RANS equations

Thelen, Andrew S.

doi:10.31274/etd-180810-4828

Cited by 2 publications

(4 citation statements)

References 51 publications

(167 reference statements)

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“…A universal Kriging model from the Surfpack library [22,38] was selected to develop the surrogate function. Kriging models are well-suited for low-dimensionality problems (fewer than 15 variables) [37] and have been used previously for the optimization of wind and marine turbines [24,25].…”

Section: Surrogate Modelmentioning

confidence: 99%

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Surrogate-Based Optimization of Horizontal Axis Hydrokinetic Turbine Rotor Blades

Arán

Menéndez

2021

Energies

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A design method was developed for automated, systematic design of hydrokinetic turbine rotor blades. The method coupled a Computational Fluid Dynamics (CFD) solver to estimate the power output of a given turbine with a surrogate-based constrained optimization method. This allowed the characterization of the design space while minimizing the number of analyzed blade geometries and the associated computational effort. An initial blade geometry developed using a lifting line optimization method was selected as the base geometry to generate a turbine blade family by multiplying a series of geometric parameters with corresponding linear functions. A performance database was constructed for the turbine blade family with the CFD solver and used to build the surrogate function. The linear functions were then incorporated into a constrained nonlinear optimization algorithm to solve for the blade geometry with the highest efficiency. A constraint on the minimum pressure on the blade could be set to prevent cavitation inception.

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Section: Surrogate Modelmentioning

confidence: 99%

“…SBO has been used in the optimization of horizontal axis wind turbines [24] and marine turbines [25][26][27]. In general, previous research focuses on maximizing the power output of the turbine by varying a series of geometric parameters [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Surrogate-Based Optimization of Horizontal Axis Hydrokinetic Turbine Rotor Blades

Arán

Menéndez

2021

Energies

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“…This configuration, illustrated in Figure 1, is known as a dual-rotor wind turbine (DRWT). Notes: D m and D s are main and secondary rotor diameters while h is the height of the supporting tower [Thelen, 2016] Variable fidelity shape optimization mapping (SM) (Koziel et al, 2008;, adaptive response correction (ARC) , adaptive response prediction (ARP) , shape-preserving response prediction (SPRP) ) and multilevel optimization Tesfahunegn et al, 2015). These physics-based SBO methods are typically not as versatile as their data-driven alternatives, but can potentially offer equivalent optimization results at a fraction of the computational expense.…”

Section: Introductionmentioning

confidence: 99%

“…The optimization approach is demonstrated in three design spaces having two, three and eleven design variables. The overall performance, in terms of computational expense and quality of optimum, is compared to that of alternative surrogate-based approaches (kriging and SAO (Thelen, 2016;Thelen et al, 2016;Thelen et al, 2018)). Finally, two parametric studies are carried out with the 11-parameter case.…”

Section: Introductionmentioning

confidence: 99%

Variable-fidelity shape optimization of dual-rotor wind turbines

Thelen

Leifsson

Sharma

et al. 2018

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Purpose Dual-rotor wind turbines (DRWTs) are a novel type of wind turbines that can capture more power than their single-rotor counterparts. Because their surrounding flow fields are complex, evaluating a DRWT design requires accurate predictive simulations, which incur high computational costs. Currently, there does not exist a design optimization framework for DRWTs. Since the design optimization of DRWTs requires numerous model evaluations, the purpose of this paper is to identify computationally efficient design approaches. Design/methodology/approach Several algorithms are compared for the design optimization of DRWTs. The algorithms vary widely in approaches and include a direct derivative-free method, as well as three surrogate-based optimization methods, two approximation-based approaches and one variable-fidelity approach with coarse discretization low-fidelity models. Findings The proposed variable-fidelity method required significantly lower computational cost than the derivative-free and approximation-based methods. Large computational savings come from using the time-consuming high-fidelity simulations sparingly and performing the majority of the design space search using the fast variable-fidelity models. Originality/value Due the complex simulations and the large number of designable parameters, the design of DRWTs require the use of numerical optimization algorithms. This work presents a novel and efficient design optimization framework for DRWTs using computationally intensive simulations and variable-fidelity optimization techniques.

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Surrogate-based design optimization of dual-rotor wind turbines using steady RANS equations

Abstract: Case I using objective function 2 (Sec. 5.1.2) (stopping criteria: H (i) − H (i−1) < 0.01 or N f > 40). N c and N f are the number of low-and high-fidelity function calls, while N f eq is the number of equivalent high-fidelity function calls (in terms of computation time

Cited by 2 publications

References 51 publications

Surrogate-Based Optimization of Horizontal Axis Hydrokinetic Turbine Rotor Blades

Surrogate-Based Optimization of Horizontal Axis Hydrokinetic Turbine Rotor Blades

Variable-fidelity shape optimization of dual-rotor wind turbines

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