The Experts below are selected from a list of 4758 Experts worldwide ranked by ideXlab platform
Xu Jianan - One of the best experts on this subject based on the ideXlab platform.
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the Heave Motion estimation for active Heave compensation system in offshore crane
International Conference on Mechatronics and Automation, 2016Co-Authors: Ning Xianliang, Zhao Jiawen, Xu JiananAbstract:Active Heave compensation is widely applied to decouple lifting loads from the ship Motions due to wave excitation. This work presents a method of Heave velocity estimation in active Heave compensation system, which depends on the measured signals from an inertial measurement unit (IMU). The method is achieved by the Motion transformation from IMU to the top of offshore crane and a Heave filter which is a combination of a highpass filter and an integrator. Although the optimal cutoff frequency of the Heave filter can be derived by analysing the error sources, it is difficult to use in the practical applications. The suboptimal or applied cutoff frequency of the Heave filter in this work is determined by limiting the amplitude response error, and depends proportionally to the estimated dominant frequency of wave Motion by the FFT. Because the Heave filter introduces the phase error which impacts the estimation significantly, a lowpass filter is utilized in the modified Heave filter for phase correction, and the lowpass cutoff frequency depends on the applied cutoff frequency and the dominant frequency of wave Motion. Hence, all parameters for filtering process can be determined adaptively online. Active Heave compensation system is simulated in the active Heave compensation experimental facility and the compensation efficiency can reach 79.6%, verifying the feasibility of Heave Motion estimation.
Toal Daniel - One of the best experts on this subject based on the ideXlab platform.
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Neuro-Fuzzy dynamic position prediction for autonomous work-class ROV docking
'MDPI AG', 2020Co-Authors: Trslić Petar, Omerdić Edin, Dooly Gerard, Toal DanielAbstract:peer-reviewedThis paper presents a docking station Heave Motion prediction method for dynamic remotely operated vehicle (ROV) docking, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS). Due to the limited power onboard the subsea vehicle, high hydrodynamic drag forces, and inertia, work-class ROVs are often unable to match the Heave Motion of a docking station suspended from a surface vessel. Therefore, the docking relies entirely on the experience of the ROV pilot to estimate Heave Motion, and on human-in-the-loop ROV control. However, such an approach is not available for autonomous docking. To address this problem, an ANFIS-based method for prediction of a docking station Heave Motion is proposed and presented. The performance of the network was evaluated on real-world reference trajectories recorded during offshore trials in the North Atlantic Ocean during January 2019. The hardware used during the trials included a work-class ROV with a cage type TMS, deployed using an A-frame launch and recovery system
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Neuro-Fuzzy dynamic position prediction for autonomous work-class ROV docking
MDPI, 2020Co-Authors: Trslić Petar, Dooly Gerard, Omerdic Edin, Toal DanielAbstract:This paper presents a docking station Heave Motion prediction method for dynamic remotely operated vehicle (ROV) docking, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS). Due to the limited power onboard the subsea vehicle, high hydrodynamic drag forces, and inertia, work-class ROVs are often unable to match the Heave Motion of a docking station suspended from a surface vessel. Therefore, the docking relies entirely on the experience of the ROV pilot to estimate Heave Motion, and on human-in-the-loop ROV control. However, such an approach is not available for autonomous docking. To address this problem, an ANFIS-based method for prediction of a docking station Heave Motion is proposed and presented. The performance of the network was evaluated on real-world reference trajectories recorded during offshore trials in the North Atlantic Ocean during January 2019. The hardware used during the trials included a work-class ROV with a cage type TMS, deployed using an A-frame launch and recovery system
Ning Xianliang - One of the best experts on this subject based on the ideXlab platform.
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the Heave Motion estimation for active Heave compensation system in offshore crane
International Conference on Mechatronics and Automation, 2016Co-Authors: Ning Xianliang, Zhao Jiawen, Xu JiananAbstract:Active Heave compensation is widely applied to decouple lifting loads from the ship Motions due to wave excitation. This work presents a method of Heave velocity estimation in active Heave compensation system, which depends on the measured signals from an inertial measurement unit (IMU). The method is achieved by the Motion transformation from IMU to the top of offshore crane and a Heave filter which is a combination of a highpass filter and an integrator. Although the optimal cutoff frequency of the Heave filter can be derived by analysing the error sources, it is difficult to use in the practical applications. The suboptimal or applied cutoff frequency of the Heave filter in this work is determined by limiting the amplitude response error, and depends proportionally to the estimated dominant frequency of wave Motion by the FFT. Because the Heave filter introduces the phase error which impacts the estimation significantly, a lowpass filter is utilized in the modified Heave filter for phase correction, and the lowpass cutoff frequency depends on the applied cutoff frequency and the dominant frequency of wave Motion. Hence, all parameters for filtering process can be determined adaptively online. Active Heave compensation system is simulated in the active Heave compensation experimental facility and the compensation efficiency can reach 79.6%, verifying the feasibility of Heave Motion estimation.
Ilhyoung Cho - One of the best experts on this subject based on the ideXlab platform.
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Effect of Internal Fluid Resonance on the Performance of a Floating OWC Device
'The Korean Society of Ocean Engineers', 2021Co-Authors: Ilhyoung ChoAbstract:In the present study, the performance of a floating oscillating water column (OWC) device has been studied in regular waves. The OWC model has the shape of a hollow cylinder. The linear potential theory is assumed, and a matched eigenfunction expansion method(MEEM) is applied for solving the diffraction and radiation problems. The radiation problem involves the radiation of waves by the heaving Motion of a floating OWC device and the oscillating pressure in the air chamber. The characteristics of the exciting forces, hydrodynamic forces, flow rate, air pressure in the chamber, and Heave Motion response are investigated with various system parameters, such as the inner radius, draft of an OWC, and turbine constant. The efficiency of a floating OWC device is estimated in connection with the extracted wave power and capture width. Specifically, the piston-mode resonance in an internal fluid region plays an important role in the performance of a floating OWC device, along with the Heave Motion resonance. The developed prediction tool will help determine the various design parameters affecting the performance of a floating OWC device in waves
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Heave Motion response of a circular cylinder with the dual damping plates
Ocean Engineering, 2016Co-Authors: Hyeokjun Koh, Ilhyoung ChoAbstract:A damping plate attached to the floating structure has a distinct advantage in reducing the Motion response of a floating structure by increasing the added mass and damping. Analytical and experimental studies were carried out to investigate the Heave Motion response of a floating cylinder according to the characteristics of dual damping plates (DDPs), such as submergence depth and radius ratio. An analytical method using a Matched Eigenfunction Expansion Method (MEEM) was developed for solving the radiation problem by a heaving circular cylinder with DDPs in the context of linear potential theory. To confirm the present analytical solutions, a series of experiments for Heave Motion responses was conducted in a two-dimensional wave tank in regular waves with varying wave frequencies. The analytical results were in good agreement with the experimental results, and the Heave Motion response of the cylinder was decreased considerably within the region of Heave resonance frequency by installation of the proposed DDPs. By using the predictive tools requiring less calculation time, the effect of damping plates as Motion reduction devices for spar-type offshore platforms can be assessed for various combinations of parameters such as the number, size, and location of damping plates at the concept design stage.
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relative Heave Motion responses of floating dual buoy wave energy converter in waves
The Twenty-fifth International Ocean and Polar Engineering Conference, 2015Co-Authors: Jeong Rok Kim, Yoon Hyeok Bae, Ilhyoung ChoAbstract:The present study investigated the performance of a Wave Energy Converter (WEC) system using the relative Heave Motion of a dual-buoy. In order to increase the conversion efficiency of a dual-buoy WEC, a multiple-resonance concept was used, which is generated at the dual-buoy’s Heave natural frequency and piston-mode natural frequency. The matched eigenfunction expansion method (MEEM) was applied to obtain the analytical solution under the assumption of the linear potential theory. To verify the validity of the analytical solution, systematic model tests were carried out in a 2D wave tank. If the dual-buoy WEC is designed to be tuned at the Heave natural frequency and piston-mode natural frequency, it is possible to maintain a reasonably high relative Heave Motion response within a wide range of wave frequencies. This research will provide the key to the improvement in energy extraction efficiency.
Trslić Petar - One of the best experts on this subject based on the ideXlab platform.
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Neuro-Fuzzy dynamic position prediction for autonomous work-class ROV docking
'MDPI AG', 2020Co-Authors: Trslić Petar, Omerdić Edin, Dooly Gerard, Toal DanielAbstract:peer-reviewedThis paper presents a docking station Heave Motion prediction method for dynamic remotely operated vehicle (ROV) docking, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS). Due to the limited power onboard the subsea vehicle, high hydrodynamic drag forces, and inertia, work-class ROVs are often unable to match the Heave Motion of a docking station suspended from a surface vessel. Therefore, the docking relies entirely on the experience of the ROV pilot to estimate Heave Motion, and on human-in-the-loop ROV control. However, such an approach is not available for autonomous docking. To address this problem, an ANFIS-based method for prediction of a docking station Heave Motion is proposed and presented. The performance of the network was evaluated on real-world reference trajectories recorded during offshore trials in the North Atlantic Ocean during January 2019. The hardware used during the trials included a work-class ROV with a cage type TMS, deployed using an A-frame launch and recovery system
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Neuro-Fuzzy dynamic position prediction for autonomous work-class ROV docking
MDPI, 2020Co-Authors: Trslić Petar, Dooly Gerard, Omerdic Edin, Toal DanielAbstract:This paper presents a docking station Heave Motion prediction method for dynamic remotely operated vehicle (ROV) docking, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS). Due to the limited power onboard the subsea vehicle, high hydrodynamic drag forces, and inertia, work-class ROVs are often unable to match the Heave Motion of a docking station suspended from a surface vessel. Therefore, the docking relies entirely on the experience of the ROV pilot to estimate Heave Motion, and on human-in-the-loop ROV control. However, such an approach is not available for autonomous docking. To address this problem, an ANFIS-based method for prediction of a docking station Heave Motion is proposed and presented. The performance of the network was evaluated on real-world reference trajectories recorded during offshore trials in the North Atlantic Ocean during January 2019. The hardware used during the trials included a work-class ROV with a cage type TMS, deployed using an A-frame launch and recovery system
