Modelling of a hydrokinetic energy converter for flow-induced vibration based on experimental data

Wu, Wenhua; Sun, Haiou; Lv, Baicheng; Bernitsas, Michael M.

doi:10.1016/j.oceaneng.2018.02.030

Cited by 21 publications

(9 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This surrogate model also can be considered as an effective substitute for experimental device and numerical software to predict the data for cases that have not be directly measured. Based on its modeling efficiency, predictive strength, and ability to explore unknown data, surrogate models have been utilized in many engineering fields [29,30,[32][33][34].…”

Section: Introductionmentioning

confidence: 99%

“…Surrogate-based models can be constructed correctly by utilizing the corresponding predictive method. Several predictive approaches, such as polynomial regression [29], kriging model [33] and artificial neural network [35], have been used to establish a surrogate model for accurate prediction. Nevertheless, some published studies illustrate that the polynomial modeling has low accuracy for highly nonlinear cases [29,33].…”

Section: Introductionmentioning

confidence: 99%

“…Several predictive approaches, such as polynomial regression [29], kriging model [33] and artificial neural network [35], have been used to establish a surrogate model for accurate prediction. Nevertheless, some published studies illustrate that the polynomial modeling has low accuracy for highly nonlinear cases [29,33]. Additionally, the Kriging model has relatively complex structure and is more difficult to build because it requires determining the maximum possible estimator and assuming the global function form [33].…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, some published studies illustrate that the polynomial modeling has low accuracy for highly nonlinear cases [29,33]. Additionally, the Kriging model has relatively complex structure and is more difficult to build because it requires determining the maximum possible estimator and assuming the global function form [33]. Artificial neural network methods, such as back propagation neural network (BPNN) and the radial basis function neural network (RBFNN) method, have been widely used because they can effectively deal with nonlinear forecasting problems [35][36][37].…”

Section: Introductionmentioning

confidence: 99%

“…Artificial neural network methods, such as back propagation neural network (BPNN) and the radial basis function neural network (RBFNN) method, have been widely used because they can effectively deal with nonlinear forecasting problems [35][36][37]. 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].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Modelling of a Flow-Induced Oscillation, Two-Cylinder, Hydrokinetic Energy Converter Based on Experimental Data

Sun

Bernitsas

et al. 2021

Energies

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%