Experimental and numerical comparisons of a dual-flap floating oscillating surge wave energy converter in regular waves

Ruehl, Kelley; Forbush, Dominic; Yu, Yi Hsiang; Tom, Nathan

doi:10.1016/j.oceaneng.2019.106575

Cited by 28 publications

(13 citation statements)

References 7 publications

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“…Similar to this, the torques with the same indexing were applied to the "o" point, respectively (Equation ( 6)). So, the fluid governing equations can be written as Equations (7) and (8) [29].…”

Section: Governing Equationsmentioning

confidence: 99%

“…Due to this fact, investors around the world need information about predicting the potential for power generation by WEC systems [7]. In these fields, especially in terms of utilizing the output power from WEC systems, several studies were performed through numerical simulations and experiments [8,9]. In this study, a new wave energy converter, named "Searaser", was chosen because of its lower price when compared…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning for Wave Energy Converter Modeling Using Long Short-Term Memory

et al. 2021

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Accurate forecasts of ocean waves energy can not only reduce costs for investment, but it is also essential for the management and operation of electrical power. This paper presents an innovative approach based on long short-term memory (LSTM) to predict the power generation of an economical wave energy converter named “Searaser”. The data for analysis is provided by collecting the experimental data from another study and the exerted data from a numerical simulation of Searaser. The simulation is performed with Flow-3D software, which has high capability in analyzing fluid–solid interactions. The lack of relation between wind speed and output power in previous studies needs to be investigated in this field. Therefore, in this study, wind speed and output power are related with an LSTM method. Moreover, it can be inferred that the LSTM network is able to predict power in terms of height more accurately and faster than the numerical solution in a field of predicting. The network output figures show a great agreement, and the root mean square is 0.49 in the mean value related to the accuracy of the LSTM method. Furthermore, the mathematical relation between the generated power and wave height was introduced by curve fitting of the power function to the result of the LSTM method.

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Section: Governing Equationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning for Wave Energy Converter Modeling Using Long Short-Term Memory

et al. 2021

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show abstract

“…During these phases, depending on the scope and economic resources available, a different type of PTO system can be used. At phase 1, a geometrical scale (λ = 1/s) within the range of λ = 1 : 25 − 100 can be adopted, e.g., [19][20][21]. At this stage, the main aim of the experimental work normally concerns a proof-of-concept, initial preliminary assessments on performances and numerical model validation.…”

Section: Experimental Scale Selectionmentioning

confidence: 99%

Wave Energy Converter Power Take-Off System Scaling and Physical Modelling

Giannini

Temiz

Rosa-Santos

et al. 2020

JMSE

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Absorbing wave power from oceans for producing a usable form of energy represents an attractive challenge, which for the most part concerns the development and integration, in a wave energy device, of a reliable, efficient and cost-effective power take-off mechanism. During the various stages of progress, for assessing a wave energy device, it is convenient to carry out experimental testing that, opportunely, takes into account the realistic behaviour of the power take-off mechanism at a small scale. To successfully replicate and assess the power take-off, good practices need to be implemented aiming to correctly scale and evaluate the power take-off mechanism and its behaviour. The present paper aims to explore and propose solutions that can be applied for reproducing and assessing the power take-off element during experimental studies, namely experimental set-ups enhancements, calibration practices, and error estimation methods. A series of recommendations on how to practically organize and carry out experiments were identified and three case studies are briefly covered. It was found that, despite specific options that can be strictly technology-dependent, various recommendations could be universally applicable.

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“…Due to the fact, investors around the world need to know information about predicting the potential for power generation by WEC systems [8]. In these fields, especially utilizing the output power from WEC systems several studies through numerical simulations and experimental were done [9][10][11][12]. In this study, a new wave energy converter named "Searaser" was chosen because of the lower price than similar types on the market and considered as clean electricity producer with no climate gas emissions involved [13].…”

Section: Introductionmentioning

confidence: 99%

Providing a Prediction Model to the Output Power of a Wave Energy Converter by Artificial Neural Network

Mousavi¹,

Ghasemi²,

M³

et al. 2021

Preprint

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The technologies pertaining to ocean waves has been continuously improving since its creation and the major focus is to come up with accurate predication of power generation from the oceans waves. The precise forecast can not only reduce costs for investment but also it is essential for management and operation of electrical power. The purpose of the current paper is to numerically investigate a new economical wave energy converter named “Searaser”. The simulation is done with Flow-3D software which has high capability in analyzing the fluid solid interactions. by collecting the experimental data of another study and the exerted data which is from numerical simulation, the wind speed and output power are related with a long short term memory (LSTM). By doing comparative analysis between these data, it can be concluded that the artificial intelligence method is so accurate and fast. The network output figures show a good agreement and the root mean square is 0.49 in mean value which was related to the accuracy of LSTM method. One of the novelties of this study is to provide a scatter plot that generates power in terms of wind speed. Due to the accumulation of data, the power can be predicted for other wind speeds by using the intermediate values. Furthermore, the mathematical relation between the generated power and wave height was introduced by curve fitting the power function to the result of LSTM method.

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Experimental and numerical comparisons of a dual-flap floating oscillating surge wave energy converter in regular waves

Cited by 28 publications

References 7 publications

Deep Learning for Wave Energy Converter Modeling Using Long Short-Term Memory

Deep Learning for Wave Energy Converter Modeling Using Long Short-Term Memory

Wave Energy Converter Power Take-Off System Scaling and Physical Modelling

Providing a Prediction Model to the Output Power of a Wave Energy Converter by Artificial Neural Network

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