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  1. Article ; Online: Dynamic Response Analysis of a Wavestar-Type Wave Energy Converter Using Augmented Formulation in Korean Nearshore Areas

    Sanghwan Heo / Weoncheol Koo

    Processes, Vol 9, Iss 1721, p

    2021  Volume 1721

    Abstract: Interest in water wave power generation, a promising source of renewable energy, is increasing. Numerous types of wave energy converters (WECs) have been designed to transform wave energy into electricity. In this study, we focus on heaving point ... ...

    Abstract Interest in water wave power generation, a promising source of renewable energy, is increasing. Numerous types of wave energy converters (WECs) have been designed to transform wave energy into electricity. In this study, we focus on heaving point absorbers (HPAs) of the Wavestar type, which consist of multiple floats connected to a bottom-fixed ocean structure by structural arms and hinges. Each float moves up and down due to wave forces and produces electricity using the hydraulic power take-off (PTO) system connected directly to the float. A numerical procedure using the three-dimensional augmented formulation was developed to calculate the rotational motion of the float. The frequency-dependent coefficients were calculated using the hydrodynamic solver WAMIT. The nonlinear Froude–Krylov and hydrostatic forces were considered. For the environmental conditions, the wave data of four nearshore areas in Korea, obtained from the Korea Meteorological Administration (KMA), were used. Under the given environmental conditions, Buan was found to be the most suitable area among the locations selected for installing a Wavestar-type WEC without considering installation and maintenance costs.
    Keywords dynamic response ; heaving point absorber ; augmented formulation ; wave energy converter ; Korean nearshore area ; weakly nonlinear ; Chemical technology ; TP1-1185 ; Chemistry ; QD1-999
    Subject code 690
    Language English
    Publishing date 2021-09-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Coupled Dynamic Analysis of a Bottom-Fixed Elastic Platform with Wave Energy Converters in Random Waves

    Sanghwan Heo / Weoncheol Koo / Moo-Hyun Kim

    Applied Sciences, Vol 12, Iss 15, p

    2022  Volume 7915

    Abstract: A Wavestar-type Wave Energy Converter (WEC) on an elastic foundation structure was investigated using an author-developed coupled dynamic analysis computer program. The program included an elastic foundation structure composed of beam elements, a multi- ... ...

    Abstract A Wavestar-type Wave Energy Converter (WEC) on an elastic foundation structure was investigated using an author-developed coupled dynamic analysis computer program. The program included an elastic foundation structure composed of beam elements, a multi-body dynamics model of the entire system, a hydrodynamics model of the dual-buoy, and fully coupled dynamics considering the interaction between the structure and WECs. The selected WEC models a heaving-point-absorber (HPA), one of the oscillating body systems which causes rotational motions of a connecting rod attached to the foundation structure. A rotational-damper-type hydraulic power take-off (PTO) system on the foundation structure produced electricity. The bottom-fixed foundation structure was modeled by three-dimensional beam elements, and the entire system, including HPA, was analyzed by multi-body dynamics. Random wave data at Buan, a nearshore region of Korea, collected by the Korea Meteorological Administration (KMA), was used as a demonstration study using the developed computer programs. Through the case study, the displacement and stress of the foundation structure were increased significantly by the dynamic coupling effects with the WECs, which underscores that the coupled dynamic analysis is essential for a reliable performance evaluation and the design of such a system.
    Keywords coupled dynamic analysis ; random wave ; multi-body dynamics/hydrodynamics ; elastic structure ; wave energy converter ; heaving point absorber ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
    Language English
    Publishing date 2022-08-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  3. Article ; Online: Mooring-Failure Monitoring of Submerged Floating Tunnel Using Deep Neural Network

    Do-Soo Kwon / Chungkuk Jin / MooHyun Kim / Weoncheol Koo

    Applied Sciences, Vol 10, Iss 6591, p

    2020  Volume 6591

    Abstract: This paper presents a machine learning method for detecting the mooring failures of SFT (submerged floating tunnel) based on DNN (deep neural network). The floater-mooring-coupled hydro-elastic time-domain numerical simulations are conducted under ... ...

    Abstract This paper presents a machine learning method for detecting the mooring failures of SFT (submerged floating tunnel) based on DNN (deep neural network). The floater-mooring-coupled hydro-elastic time-domain numerical simulations are conducted under various random wave excitations and failure/intact scenarios. Then, the big-data is collected at various locations of numerical motion sensors along the SFT to be used for the present DNN algorithm. In the input layer, tunnel motion-sensor signals and wave conditions are inputted while the output layer provides the probabilities of 21 failure scenarios. In the optimization stage, the numbers of hidden layers, neurons of each layer, and epochs for reliable performance are selected. Several activation functions and optimizers are also tested for the present DNN model, and Sigmoid function and Adamax are respectively adopted to enhance the classification accuracy. Moreover, a systematic sensitivity test with respect to the numbers and arrangements of sensors is performed to find the appropriate sensor combination to achieve target prediction accuracy. The technique of confusion matrix is used to represent the accuracy of the DNN algorithms for various cases, and the classification accuracy as high as 98.1% is obtained with seven sensors. The results of this study demonstrate that the DNN model can effectively monitor the mooring failures of SFTs utilizing real-time sensor signals.
    Keywords submerged floating tunnel ; deep neural network ; machine learning ; sensor optimization ; failure monitoring accuracy ; mooring line ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
    Subject code 620
    Language English
    Publishing date 2020-09-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Mathematical Modeling of Partial-Porous Circular Cylinders with Water Waves

    Min-Su Park / Weoncheol Koo

    Mathematical Problems in Engineering, Vol

    2015  Volume 2015

    Keywords Mathematics ; QA1-939 ; Science ; Q ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Technology ; T
    Language English
    Publishing date 2015-01-01T00:00:00Z
    Publisher Hindawi Publishing Corporation
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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