Metamodeling-based parametric optimization of a bio-inspired Savonius-type hydrokinetic turbine

Hashem, Islam; Zhu, Baoshan

doi:10.1016/j.renene.2021.08.087

Cited by 30 publications

(4 citation statements)

References 39 publications

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“…Mosbahi et al [14] studied how to improve the efficiency of the Savonius runner by changing the blade shape, and used CFD to simulate the different blade shapes, and optimized the power coefficient according to the tip speed ratio and the blade shape. Hashem et al [15] took a pair of swimming koi carp as inspiration and carried out a CFD-based optimization design for the Savonius turbine, mainly aiming at the blade overlap ratio and clearance ratio to enhance the power coefficient on the meta-model. Wang et al [16] optimized the design of the Savonius wind turbine with a double-sided contour, using sliding network technology to build CFD models, and optimized the double-sided shape of the runner based on the Kriging-PSO optimization model.…”

Section: E Pmentioning

confidence: 99%

Optimization of Blade Geometry of Savonius Hydrokinetic Turbine Based on Genetic Algorithm

Lu,

Zhang,

Guang

et al. 2023

ENERGY

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Savonius hydrokinetic turbine is a kind of turbine set which is suitable for low-velocity conditions. Unlike conventional turbines, Savonius turbines employ S-shaped blades and have simple internal structures. Therefore, there is a large space for optimizing the blade geometry. In this study, computational fluid dynamics (CFD) numerical simulation and genetic algorithm (GA) were used for the optimal design. The optimization strategies and methods were determined by comparing the results calculated by CFD with the experimental results. The weighted objective function was constructed with the maximum power coefficient C p and the high-power coefficient range R under multiple working conditions. GA helps to find the optimal individual of the objective function. Compared the optimal scheme with the initial scheme, the overlap ratio β increased from 0.2 to 0.202, and the clearance ratio ε increased from 0 to 0.179, the blade circumferential angle γ increased from 0°to 27°, the blade shape extended more towards the spindle. The overall power of Savonius turbines was maintained at a high level over 22%, R also increased from 0.73 to 1.02. In comparison with the initial scheme, the energy loss of the optimal scheme at high blade tip speed is greatly reduced, and this reduction is closely related to the optimization of blade geometry. As R becomes larger, Savonius turbines can adapt to the overall working conditions and meet the needs of its work in low flow rate conditions. The results of this paper can be used as a reference for the hydrodynamic optimization of Savonius turbine runners.

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Section: E Pmentioning

confidence: 99%

Optimization of Blade Geometry of Savonius Hydrokinetic Turbine Based on Genetic Algorithm

Lu,

Zhang,

Guang

et al. 2023

ENERGY

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“…The optimal airfoil shape showed a significant delay in the dynamic stall angle when compared to the baseline airfoil. Hashem and Zhu (2021) applied metamodeling-based parametric optimization to a Savonius-type hydrokinetic turbine inspired by Koi fish. The design parameters were the overlap ratio and the gap ratio.…”

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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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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“…However, it needs more investigation to optimize the inclination angle to minimize the effect of loading force on the shaft. ± ∘ Hashem and Zhu [21] performed numerical analysis on proposed bio inspired SHT similar to that of a "Koi Carp" sh type blade pro le. It was found that for the given TSR of 1.1, the proposed bio inspired turbine yielded the C p as 0.2521 which was 17.6% more than the conventional SHT.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Slot Parameters on the Performance of Savonius Hydrokinetic Turbine: A CFD Study

Kumar,

Kumar

2023

Preprint

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Hydrokinetic energy sources are considered as strong alternative to provide clean, affordable and green energy to future generation. There are different types of turbines available to harness this kind of energy resource. Among all the turbines, Savonius hydrokinetic turbines are acknowledged as the most suitable rotor for generating power from flowing water streams such as rivers and canal. The objective of this research paper is to improve the performance of Savonius hydrokinetic turbine using slotted blades. Under the present study, four different slot parameters i.e., slot shapes, slot position, slot gaps and slot shape factor are considered. The water velocity was kept constant as 1 m/s in this study. The fluid flow distributions that occur all around the rotor have been studied and discussed. Based on the investigations, Savonius hydrokinetic turbine having divergent slot has outperformed the all-other shapes of slots for the entire range of tip speed ratio considered. The highest power coefficient of 0.2942 corresponds to the tip speed ratio value of 0.9 has been achieved for divergent slot Savonius rotor at slot position of 5% having slot gap of 3mm and slot shape factor of 0.2.

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Metamodeling-based parametric optimization of a bio-inspired Savonius-type hydrokinetic turbine

Cited by 30 publications

References 39 publications

Optimization of Blade Geometry of Savonius Hydrokinetic Turbine Based on Genetic Algorithm

Optimization of Blade Geometry of Savonius Hydrokinetic Turbine Based on Genetic Algorithm

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

Investigation of Slot Parameters on the Performance of Savonius Hydrokinetic Turbine: A CFD Study

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