Layout study of wave energy converter arrays by an artificial neural network and adaptive genetic algorithm

Zhu, Kai; Shi, Hongda; Han, Meng; Cao, Feifei

doi:10.1016/j.oceaneng.2022.112072

Cited by 23 publications

(6 citation statements)

References 40 publications

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“…To overcome, an adaptive genetic algorithm (AGA) is used to adjust the crossover and mutation probability through the fitness value. 42 The AGA can ensure good global search ability in the early stages of evolution. As the population continues to evolve, the global search ability decreases and the local search ability increases, which can help to find the optimal solution faster.…”

Section: Experiments and Parameter Identificationmentioning

confidence: 99%

State of Charge Estimation of Supercapacitor Under Different Temperatures Using Particle Filter Algorithm Based on Fractional-Order Model

Liu,

Xu,

Zhang

et al. 2023

J. Electrochem. Soc.

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With the rise of new energy vehicles, supercapacitors (SCs) have been used as energy storage components for new energy vehicles due to their high-power density and good low-temperature performance. Accurate modeling and state of charge estimation of SC can ensure the safe operation of new energy vehicles. In order to explore the low-temperature performance of supercapacitors, this paper proposes a dual ZARC fractional-order circuit model to simulate the dynamic characteristics of SC. Using adaptive genetic algorithm for SC parameter identification, the model terminal voltage error is less than 6.5 mV. In addition, the SOC of SC at different temperatures and working conditions is estimated by using the fractional-order particle filter (FOPF) method and compared with the fractional-order extended Kalman filter (FOEKF). The experimental results show that the FOPF method has high estimation accuracy and robustness. Under the temperature of minus 40℃, the maximum mean absolute error and maximum root-mean-square deviation of SOC estimation under different working conditions are less than 2%, showing good low-temperature performance.

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Section: Experiments and Parameter Identificationmentioning

confidence: 99%

State of Charge Estimation of Supercapacitor Under Different Temperatures Using Particle Filter Algorithm Based on Fractional-Order Model

Liu,

Xu,

Zhang

et al. 2023

J. Electrochem. Soc.

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“…For such a condition, d = 14, 11, or 10 m may be appropriate, given that the maximum pitching motion occurs in the range of 0.2 to 0.3 rad/s, whereas the maximum vertical motion occurs in the range of 0.3 to 0.4 rad/s. This is important for engineering applications, but multi-parameter optimisation is necessary 44,45 and is briefly analysed here in the frequency domain.…”

Section: A Effect Of Base Column Submergence Depthmentioning

confidence: 99%

“…The base column radius, R (n) 2 , is also an important parameter to consider. As discussed in Section VI A, for ω > 0.3 rad/s, values of d = 14, 11, or 10 m are recommended choices, although the optimal value of d depends on multiple parameters and optimization algorithms 45 . In this subsection, the effect of the base column radius, R (n) 2 , is investigated separately for d = 14 and 10 m. The comparison reveals a similar trend.…”

Section: B Effect Of Base Column Radiusmentioning

confidence: 99%

Wave diffraction and radiation from a semi-submersible floating foundation for wind turbines: A semi-analytical study

Michele

Greaves

2023

Physics of Fluids

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Many marine structures are composed of vertical axisymmetric floats. In this paper, a semi-analytical model based on the linear potential flow theory and an eigenfunction expansion method is developed to study wave diffraction and radiation by an array of cylindrical structures. Each structure can be formed by three coaxial cylinders of different dimensions. Based on the semi-analytical model, a constrained matrix equation of motion is presented and solved to evaluate the performance of multiple interconnected cylindrical structures. In order to verify the accuracy of the semi-analytical model, a typical OC4-DeepCwind floating offshore wind turbine (FOWT) is selected for validation. The validated model is, then, applied to study the effect of base column submergence depth, radius, and thickness on the motion response of a semi-submersible platform. Although the results of this study are of significance for the selection of the optimum semi-submersible FOWT for specific locations (with specific prevailing wave directions), the focus of this work was placed on the semi-analytical model itself, which is efficient in modeling the interaction of the wave field and can be used in future FOWT projects.

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“…They found that the optimal range of design variables for submergence depth was 0.055-0.067, the distance between plates was 0.064-0.080, and porosity was 0.117-0.173. Zhu et al [38] investigated the total output power due to changes in the configuration of the array of wave energy converters. Using the ANN method, they trained a prediction model for the desired response.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Optimization of Hydraulic and Thermal Performance of a Tesla Valve Using a Numerical Method and Artificial Neural Network

Vaferi,

Vajdi,

Shadian

et al. 2023

Entropy

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The Tesla valve is a non-moving check valve used in various industries to control fluid flow. It is a passive flow control device that does not require external power to operate. Due to its unique geometry, it causes more pressure drop in the reverse direction than in the forward direction. This device’s optimal performance in heat transfer applications has led to the use of Tesla valve designs in heat sinks and heat exchangers. This study investigated a Tesla valve with unconventional geometry through numerical analysis. Two geometrical parameters and inlet velocity were selected as input variables. Also, the pressure drop ratio (PDR) and temperature difference ratio (TDR) parameters were chosen as the investigated responses. By leveraging numerical data, artificial neural networks were trained to construct precise prediction models for responses. The optimal designs of the Tesla valve for different conditions were then reported using the genetic algorithm method and prediction models. The results indicated that the coefficient of determination for both prediction models was above 0.99, demonstrating high accuracy. The most optimal PDR value was 4.581, indicating that the pressure drop in the reverse flow direction is 358.1% higher than in the forward flow direction. The best TDR response value was found to be 1.862.

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Layout study of wave energy converter arrays by an artificial neural network and adaptive genetic algorithm

Cited by 23 publications

References 40 publications

State of Charge Estimation of Supercapacitor Under Different Temperatures Using Particle Filter Algorithm Based on Fractional-Order Model

State of Charge Estimation of Supercapacitor Under Different Temperatures Using Particle Filter Algorithm Based on Fractional-Order Model

Wave diffraction and radiation from a semi-submersible floating foundation for wind turbines: A semi-analytical study

Modeling and Optimization of Hydraulic and Thermal Performance of a Tesla Valve Using a Numerical Method and Artificial Neural Network

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