Response Surface Optimisation of Vertical Axis Wind Turbine at low wind speeds

Elsakka, Mohamed; Ingham, D.B.; Ma, Lin; Pourkashanian, Mohamed; Moustafa, Gamal Hafez; Elhenawy, Yasser

doi:10.1016/j.egyr.2022.08.222

Cited by 25 publications

(16 citation statements)

References 37 publications

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“…A twodimensional model tends to simplify the tridimensional structures that are characteristic of turbulent flows and does not consider the dissipative tridimensional vortices that are formed at the edges of the blades. As the works of Gosselin et al (2016) and Elsakka et al (2022) concluded, the lower the turbine's aspect ratio, the higher the tendency of the 2D simulations to overpredict the Cp, with up to 41% of overprediction observed by Elsakka et al (2022) for a turbine with aspect ratio equal to 1, close to the current turbine's aspect ratio of 1.2. Corroborating to that is the fact that the difference in Cp increases as the wind velocity increases and the flow becomes even more turbulent.…”

Section: Cfd Model Validationmentioning

confidence: 86%

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Surrogate-Based Design Optimization of a H-Darrieus Wind Turbine Comparing Classical Response Surface, Artificial Neural Networks, and Kriging

2023

JAFM

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Clean energy sources like wind energy have been receiving much attention, and great emphasis has been given to the design and optimization of horizontal axis wind turbines, but just as important are the vertical axis wind turbines that can be used for generating energy for small businesses, houses, and buildings. This article sought to study the optimal geometrical parameters of a H-Darrieus vertical axis wind turbine using surrogate-based optimization with three different types of surrogate models and compared them. Airfoil chord and thickness were chosen as the design variables and respective ranges set at 0.32-0.6 m and 0.04-0.16 m. All evaluations are carried out for a tip-speed ratio of 1.5. Three different surrogate models were used and compared, namely a quadratic polynomial response surface, an artificial neural network based on radial basis functions called Extreme Learning Machine and a Kriging interpolator. Surrogates were constructed based on an initial sample data distributed according to a full factorial design. A test set was designed to evaluate the accuracy of the surrogates. Both training and testing data sets were generated using 2D CFD modeling to reduce computational cost. From the test set, Extreme Learning Machine surrogate showed the smallest RMSE of 11.24%, followed by Kriging, at 17.64%, and Response Surface of 22.17%. For the optimal designs the same pattern ensued, with optimal power coefficient overestimated by 8.7% for the response surface surrogate, followed by 3.12% and 2.17% for the Kriging interpolator and the Extreme Learning Machine, respectively. Power coefficient curves comparing the three optimal geometries from each surrogate were calculated and plotted. Optimal turbine obtained from Kriging surrogate optimization process resulted in a 7.92% increase in the Cp, whilst Extreme Learning Machine and Response Surface resulted in a 7.86% and 4.29% increase, respectively, all when compared to baseline CFD model. Concluding guidelines are that the quadratic polynomial response surface may not be the best alternative when dealing with complex non-linear relationships as typically present in VAWT simulations. Superior techniques such as Extreme Learning Machine and Kriging could be more suitable for this application.

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Section: Cfd Model Validationmentioning

confidence: 86%

“…An overall increase of 12.7% in the power coefficient was observed with the optimal model, compared to the reference model. Elsakka et al (2022) aimed at implementing a response surface optimization (RSO) methodology for a VAWT operating at low wind speed conditions. Solidity, TSR and pitch angle were chosen as design parameters.…”

Section: Introductionmentioning

confidence: 99%

Surrogate-Based Design Optimization of a H-Darrieus Wind Turbine Comparing Classical Response Surface, Artificial Neural Networks, and Kriging

2023

JAFM

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“…The Response Surface is built using the updated DoE table, which illustrates the connection between the input and output parameters. The GDO method is used to determine optimal design options that meet both the target function and the design limitations [8]. In this study, the objective function is chosen to maximize the efficiency of the blade in order to increase energy production.…”

Section: The Rso Workflowmentioning

confidence: 99%

Design optimization of three‐dimensional geometry of a micro horizontal axis wind turbine blade using the response surface method

Bekkai,

Laouar,

Mdouki

2024

Proc Appl Math and Mech

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This study presents an aerodynamic design optimization of a micro micro‐horizontal‐axis wind turbine (HAWT). To obtain an optimal design, it is essential to understand the design parameters and select the responsible factors that affect the blade efficiency. The aim of this work is to redesign a 3D micro‐HAWT to improve aerodynamic performance, through improving the distribution of chord length and twist angle along the blade. Performance analysis and flow visualization of the initial design and the optimal design were carried out using CFD analyzer. In the blade optimization design, eight significant input parameters were selected, five to characterize the chord length distribution and three to represent the twist angle along the blade. To maximize the efficiency, design points that are created by Design of Experiment (DoE) are evaluated through (Multi‐Objective‐Genetic Algorithm) MOGA method. The results showed a reduction on the separation effect area on the optimal blade surface compared to the initial one. The use of response surface optimization (RSO), when combined with CFD simulation, has proven beneficial in selecting the optimal HAWT design. Finally, the open source software (Qblade) was used to investigate and to compare the performance of the initial and optimum design. An efficiency enhancement of approximately 3.6% is achieved at tip speed ration TSR = 3.4.

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“…al. [14] used response surface optimization together with the multi-objective Genetic Algorithm (MOGA) in a CFD simulation to find optimal turbine solidity of 0.31 and blade pitch angle of 3.6. The 2D results show a minimal improvement of about 2.9% over the baseline turbine but when comparing 3D simulations, the added parameter of aspect ratio resulted in a significantly higher percentage improvement of about 34.5% with the winglet design increasing turbine efficiency even higher especially at lower AR values.…”

Section: Literature Reviewmentioning

confidence: 99%

Aerodynamic Shape Optimization of a NACA0018 Airfoil Using Adjoint Method and Gradient-Based Optimizer

2023

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The purpose of this study is to demonstrate the suitability and efficacy of the adjoint method on the aerodynamic shape optimization on a simple symmetrical airfoil NACA0018 at low Reynolds number for wind turbine application. The adjoint method has been used in many pressure-based numerical simulations with various degrees of success leading to optimized geometries in their respective uses. ANSYS Fluent code was used in this simulation. Lift to drag ratio was defined as the observables for which adjoint sensitivities were formulated. The objective function of the optimization was set to maximize the lift to drag ratio of the airfoil by 20%. The optimization regime showed significant increase in lift and drag ratio from the initial baseline NACA0018 value of 0.0211 up to 3.66 for the optimal NACAOpt. The results demonstrate the potential of the adjoint solver paired together with the gradient-based optimizer to improve the geometry for shape optimization in many CFD applications.

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Response Surface Optimisation of Vertical Axis Wind Turbine at low wind speeds

Cited by 25 publications

References 37 publications

Surrogate-Based Design Optimization of a H-Darrieus Wind Turbine Comparing Classical Response Surface, Artificial Neural Networks, and Kriging

Surrogate-Based Design Optimization of a H-Darrieus Wind Turbine Comparing Classical Response Surface, Artificial Neural Networks, and Kriging

Design optimization of three‐dimensional geometry of a micro horizontal axis wind turbine blade using the response surface method

Aerodynamic Shape Optimization of a NACA0018 Airfoil Using Adjoint Method and Gradient-Based Optimizer

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