Cargo ship aft panel stresses prediction by deconvolution |
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| Affiliation: | 1. Shanghai Engineering Research Center of Marine Renewable Energy, College of Engineering Science and Technology, Shanghai Ocean University, Shanghai, China;2. University of Stavanger, Norway;1. Ocean College, Zhejiang University, Zhoushan, 316021, Zhejiang, China;2. Ocean Academy, Zhejiang University, Zhoushan, 316021, Zhejiang, China;1. National Engineering Research Center for Port Hydraulic Construction Technology, Tianjin Research Institute for Water Transport Engineering, M.O.T, Tianjin, 300456, China;2. State Key Laboratory of Ocean Engineering, Shanghai JiaoTong University, Shanghai, 200240, China;3. Key Laboratory for Damage Diagnosis of Engineering Structures of Hunan Province, College of Civil Engineering, Hunan University, Changsha, 410082, China;4. College of Civil Engineering and Architecture, Hainan University, Haikou, 570208, China;1. Centre for Future Materials (CFM), School of Civil Engineering and Surveying, University of Southern Queensland, Toowoomba, 4350, Australia;2. University of Sherbrooke, Department of Civil Engineering, Sherbrooke, Quebec, Canada;3. Boating Infrastructure Unit, Department of Transport and Main Roads, Brisbane, 4000, Australia;4. Centre for Future Materials, University of Southern Queensland, Springfield Central, Queensland, 4300, Australia;1. DLR Institute for Maritime Energy Systems, Geesthacht, Germany;2. Hamburg University of Technology, Hamburg, Germany;3. 50Hertz Transmission GmbH, Berlin, Germany;1. College of Safety and Ocean Engineering, China University of Petroleum-Beijing, 18 Fuxue Road, Changping, Beijing, 102249, PR China;2. Engineering& Design Institute of CNPC Offshore Engineering Company Limited, Beijing, 100028, PR China;3. Faculty of Materials and Chemical Engineering,Yibin University, Yibin 644000, Sichuan, PR China;1. Department of Ocean Space Operations and Construction Engineering, Norwegian University of Science and Technology, Norway;2. Department of Marine Technology, Norwegian University of Science and Technology, Trondheim 7049, Norway;3. Department of Engineering Sciences, University of Agder, N-4898, Grimstad, Norway;4. School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, China;5. Seacraft AS, 6010,.Ålesund, Norway |
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| Abstract: | This article introduces novel extreme value prediction method that can be used for a variety of offshore engineering applications. First, to demonstrate the novel method, fictitious data from a non-linear Duffing oscillator and measured wave heights were used as examples. The second incident included a container ship that experienced significant deck panel strains while traveling across the Atlantic Ocean in bad weather. The main concern for cargo ship transportation is potential loss of container owing to violent movements. It is challenging to model such a situation because waves and ship motions are both non-stationary and complicatedly nonlinear. Extreme motions greatly increase the role of nonlinearities, activating effects of second and higher order.Furthermore, due to the scaling and the choice of sea state, laboratory testing may also be called into doubt. Therefore, data collected from actual ships during difficult weather voyages offers a special perspective on the statistics of ship motions.This paper aims to highlight an alternative method of extrapolation that is based on intrinsic properties of the data set itself and does not assume any extrapolation functional class. Extreme value predictions typically originate from certain statistical distribution functional classes to fit the data and then extrapolate. Engineering design can make use of the unique extrapolation method that has been proposed. The proposed method's forecast accuracy has been verified in comparison to the Averaged Conditional Exceedance Rate (ACER) extrapolation method. |
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| Keywords: | Deconvolution Reliability Container vessel ACER method Ship panel stress Trans-Atlantic voyage |
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