Abstract
The VIVACE Converter consists of cylindrical oscillators in tandem subjected to transverse flow-induced oscillations (FIOs) that can be improved by varying the system parameters for a given in-flow velocity: damping, stiffness, and in-flow center-to-center spacing. Compared to a single isolated cylinder, tandem cylinders can harness more hydrokinetic energy due to synergy in FIO. Experimental and numerical methods have been utilized to analyze the FIO and energy harnessing of VIVACE. A surrogate-based model of two tandem cylinders is developed to predict the power harvesting and corresponding efficiency by introducing a backpropagation neural network. It is then utilized to reduce excessive experimental or computational testing. The effects of spacing, damping, and stiffness on harvested power and efficiency of the established prediction-model are analyzed. At each selected flow velocity, optimization results of power harvesting using the prediction-model are calculated under different combinations of damping and stiffness. The main conclusions are: (1) The surrogate model, built on extensive experimental data for tandem cylinders, can predict the cylinder oscillatory response accurately. (2) Increasing the damping ratio range from 0–0.24 to 0–0.30 is beneficial for improving power efficiency, but has no significant effect on power harvesting. (3) In galloping, a spacing ratio of 1.57 has the highest optimal harnessed power and efficiency compared with other spacing values. (4) Two tandem cylinders can harness 2.01–4.67 times the optimal power of an isolated cylinder. In addition, the former can achieve 1.46–4.01 times the efficiency of the latter. (5) The surrogate model is an efficient predictive tool defining parameters of the Converter for improved energy acquisition.
Highlights
The present trend towards clean renewable energy is very strong [1,2,3]
In 2018, Wu et al established a model of Vortex-Induced Vibration for Aquatic Clean Energy (VIVACE) Converter with an isolated single cylinder by introducing the radial basis function neural network method based on the “Isight 5.0” [33]
The structure of back propagation neural network (BPNN) is simpler than that of radial basis function neural network (RBFNN) when solving the problems with the same precision requirements
Summary
The present trend towards clean renewable energy is very strong [1,2,3]. It is gaining momentum worldwide and it is unlikely to slow down despite the current low cost of fossil fuels. In 2018, Wu et al established a model of VIVACE Converter with an isolated single cylinder by introducing the radial basis function neural network method based on the “Isight 5.0” (an integration platform for a simulation-based design process) [33]. This model can reasonably predict the effects of flow velocity, spring stiffness, and damping ratio on the harnessed power and corresponding efficiency [33]. A tandem cylinder with reasonable combinations of various input parameters can harness more power at higher efficiency than that of the isolated single-cylinder due to the positive synergy between cylinders in close proximity
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