Uncertainty analysis of extreme mooring loads associated with environmental contours and peak tension distributions

Marine Structures - Tập 89 - Trang 103369 - 2023
Yuliang Zhao1, Sheng Dong1
1College of Engineering, Ocean University of China, Qingdao, 266100, China

Tài liệu tham khảo

Vázquez-Hernández, 2011, Long-term response analysis of FPSO mooring systems, Appl Ocean Res, 33, 375, 10.1016/j.apor.2011.05.003 Winterstein, 1993 Saranyasoontorn, 2004, Efficient models for wind turbine extreme loads using inverse reliability, J Wind Eng Ind Aerod, 92, 789, 10.1016/j.jweia.2004.04.002 Haver, 2009, Environmental contour lines: a method for estimating long term extremes by a short term analysis, Transactions-Soc. Naval Architects Marine Eng., 116, 116 Baarholm, 2010, Combining contours of significant wave height and peak period with platform response distributions for predicting design response, Mar Struct, 23, 147, 10.1016/j.marstruc.2010.03.001 Haver, 1985, Wave climate off northern Norway, Appl Ocean Res, 7, 85, 10.1016/0141-1187(85)90038-0 DNV GL, 2017 Montes-Iturrizaga, 2015, Environmental contours using copulas, Appl Ocean Res, 52, 125, 10.1016/j.apor.2015.05.007 Montes-Iturrizaga, 2016, Multivariate environmental contours using C-vine copulas, Ocean Eng, 118, 68, 10.1016/j.oceaneng.2016.03.011 Haselsteiner, 2017 Wang, 2020, A novel environmental contour method for predicting long-term extreme wave conditions, Renew Energy, 162, 926, 10.1016/j.renene.2020.08.112 Dong, 2013, Bivariate maximum entropy distribution of significant wave height and peak period, Ocean Eng, 59, 86, 10.1016/j.oceaneng.2012.12.002 Huang, 2021, Joint distribution of significant wave height and zero-up-crossing wave period using mixture copula method, Ocean Eng, 219, 10.1016/j.oceaneng.2020.108305 Mackay, 2021, Marginal and total exceedance probabilities of environmental contours, Mar Struct, 75, 10.1016/j.marstruc.2020.102863 Chai, 2018, Environmental contours based on inverse SORM, Mar Struct, 60, 34, 10.1016/j.marstruc.2018.03.007 Huseby, 2013, A new approach to environmental contours for ocean engineering applications based on direct Monte Carlo simulations, Ocean Eng, 60, 124, 10.1016/j.oceaneng.2012.12.034 Haselsteiner, 2017, Deriving environmental contours from highest density regions, Coast Eng, 123, 42, 10.1016/j.coastaleng.2017.03.002 Silva-González, 2013, Development of environmental contours using Nataf distribution model, Ocean Eng, 58, 27, 10.1016/j.oceaneng.2012.08.008 Manuel, 2018, Alternative approaches to develop environmental contours from metocean data, J. Ocean Eng. Marine Energy, 4, 293, 10.1007/s40722-018-0123-0 Vanem, 2015, Alternative environmental contours for marine structural design-a comparison study, J Offshore Mech Arctic Eng, 137, 10.1115/1.4031063 Vanem, 2017, A comparison study on the estimation of extreme structural response from different environmental contour methods, Mar Struct, 56, 137, 10.1016/j.marstruc.2017.07.002 Vanem, 2020, Comparing different contour methods with response-based methods for extreme ship response analysis, Mar Struct, 69, 10.1016/j.marstruc.2019.102680 Ross, 2020, On environmental contours for marine and coastal design, Ocean Eng, 195, 10.1016/j.oceaneng.2019.106194 Silva-González, 2015, The effect of some uncertainties associated to the environmental contour lines definition on the extreme response of an FPSO under hurricane conditions, Appl Ocean Res, 53, 190, 10.1016/j.apor.2015.09.005 Montes-Iturrizaga, 2017, Assessment of uncertainty in environmental contours due to parametric uncertainty in models of the dependence structure between metocean variables, Appl Ocean Res, 64, 86, 10.1016/j.apor.2017.02.006 Vanem, 2019, A simulation study on the uncertainty of environmental contours due to sampling variability for different estimation methods, Appl Ocean Res, 91, 10.1016/j.apor.2019.101870 Haselsteiner, 2020, Predicting wave heights for marine design by prioritizing extreme events in a global model, Renew Energy, 156, 1146, 10.1016/j.renene.2020.04.112 Raed, 2020, Uncertainty assessment for the extreme hydrodynamic response of a wind turbine semi-submersible platform using different environmental contour approaches, Ocean Eng, 195, 10.1016/j.oceaneng.2019.106719 Agarwal, 2009, Simulation of offshore wind turbine response for long-term extreme load prediction, Eng Struct, 31, 2236, 10.1016/j.engstruct.2009.04.002 Muliawan, 2013, Application of the contour line method for estimating extreme responses in the mooring lines of a two-body floating wave energy converter, J Offshore Mech Arctic Eng, 135, 10.1115/1.4024267 Sagrilo, 2011, A straightforward approach for using single time domain simulations to assess characteristic extreme responses, Ocean Eng, 38, 1464, 10.1016/j.oceaneng.2011.07.003 Ding, 2013, Comparison of statistical extrapolation method for the evaluation of long-term extreme response of wind turbine, Eng Struct, 57, 100, 10.1016/j.engstruct.2013.09.017 Ambühl, 2014, Extrapolation of extreme response for different mooring line systems of floating wave energy converters, Int. J. Marine Energy, 7, 1, 10.1016/j.ijome.2014.09.003 Li, 2018, Short-term extreme response and fatigue damage of an integrated offshore renewable energy system, Renew Energy, 126, 617, 10.1016/j.renene.2018.03.087 Zhao, 2021, Estimation of characteristic extreme response for mooring system in a complex ocean environment, Ocean Eng, 225, 10.1016/j.oceaneng.2021.108809 Karlsen, 2005, Statistical response predictions for a nonlinearly moored large volume structures in random seas, Appl Ocean Res, 27, 273, 10.1016/j.apor.2006.03.001 Xu, 2019, Effect of wave nonlinearity on fatigue damage and extreme responses of a semi-submersible floating wind turbine, Appl Ocean Res, 91, 10.1016/j.apor.2019.101879 2005 2015 Guo, 2016, Statistics analysis of ship response in extreme seas, Ocean Eng, 119, 154, 10.1016/j.oceaneng.2016.03.060 Hsu, 2017, Extreme mooring tensions due to snap loads on a floating offshore wind turbine system, Mar Struct, 55, 182, 10.1016/j.marstruc.2017.05.005 Cheng, 2020, Extreme responses and associated uncertainties for a long end-anchored floating bridge, Eng Struct, 219, 10.1016/j.engstruct.2020.110858 Stanisic, 2017 Stanisic, 2018, Design loads and long term distribution of mooring line response of a large weathervaning vessel in a tropical cyclone environment, Mar Struct, 61, 361, 10.1016/j.marstruc.2018.06.004 Xu, 2019, Estimation of short-term extreme responses of a semi-submersible moored by two hybrid mooring systems, Ocean Eng, 190, 10.1016/j.oceaneng.2019.106388 Cheng, 2016, Extreme response predictions for deepwater mooring system Wang, 2017, Optimal threshold selection in the POT method for extreme value prediction of the dynamic response of a Spar-type floating wind turbines, Ocean Eng, 134, 119, 10.1016/j.oceaneng.2017.02.029 Zhao, 2022, Design load estimation with IFORM-based models considering long-term extreme response for mooring systems, Ships Offshore Struct, 17, 541, 10.1080/17445302.2020.1838118 Naess, 2009, Estimation of extreme values from sampled time series, Struct Saf, 31, 325, 10.1016/j.strusafe.2008.06.021 Naess, 2010, Prediction of extreme response statistics of narrow-band random vibrations, J Eng Mech, 136, 290, 10.1061/(ASCE)0733-9399(2010)136:3(290) Chai, 2018, Probabilistic methods for estimation of the extreme value statistics of ship ice loads, Cold Reg Sci Technol, 146, 87, 10.1016/j.coldregions.2017.11.012 Sagrilo, 2012, On the extreme value analysis of the response of a turret moored FPSO, J Offshore Mech Arctic Eng, 134, 10.1115/1.4006759 Xu, 2020 Haselsteiner, 2021, A benchmarking exercise for environmental contours, Ocean Eng, 236, 10.1016/j.oceaneng.2021.109504 Vanem, 2016, Joint statistical models for significant wave height and wave period in a changing climate, Mar Struct, 49, 180, 10.1016/j.marstruc.2016.06.001 de Hauteclocque, 2022, Quantitative comparison of environmental contour approaches, Ocean Eng, 245, 10.1016/j.oceaneng.2021.110374 Jonathan, 2021, Uncertainties in return values from extreme value analysis of peaks over threshold using the generalized Pareto distribution, Ocean Eng, 220, 10.1016/j.oceaneng.2020.107725 Vanem, 2015, Uncertainties in extreme value modelling of wave data in a climate change perspective, J. Ocean Eng. Marine Energy, 1, 339, 10.1007/s40722-015-0025-3 Haselsteiner, 2019, A benchmark exercise on estimating extreme environmental conditions: methodology & baseline results Athanassoulis, 1994, Bivariate distributions with given marginals with an application to wave climate description, Appl Ocean Res, 16, 1, 10.1016/0141-1187(94)90010-8 Ferreira, 1999, Modelling the long-term distribution of significant wave height with the Beta and Gamma models, Ocean Eng, 26, 713, 10.1016/S0029-8018(98)00022-5 Petrov, 2019, Maximum entropy estimates of extreme significant wave heights from satellite altimeter data, Ocean Eng, 187, 10.1016/j.oceaneng.2019.106205 Moan, 2005, Uncertainty of wave-induced response of marine structures due to long-term variation of extratropical wave conditions, Mar Struct, 18, 359, 10.1016/j.marstruc.2005.11.001 Bai, 2020, Joint probability distribution of coastal winds and waves using a log-transformed kernel density estimation and mixed copula approach, Ocean Eng, 216, 10.1016/j.oceaneng.2020.107937 Eckert-Gallup, 2016, Kernel density estimation (kde) with adoptive bandwidth selection for environmental contours of extreme sea states Wang, 2022, An efficient method for predicting long term extreme design forces of wave energy converters, Appl Ocean Res, 121, 10.1016/j.apor.2022.103094 Li, 2016, Statistical analysis of wave climate data using mixed distributions and extreme wave prediction, Energies, 9, 396, 10.3390/en9060396 Al-Fawzan, 2000 Goda, 2010, Incorporating of Weibull distribution in L-moments method for regional frequency analysis of peak over threshold wave heights Li, 2015, Bootstrap method for characterizing the effect of uncertainty in shear strength parameters on slope reliability, Reliab Eng Syst Saf, 140, 99, 10.1016/j.ress.2015.03.034 Ragan, 2008, Statistical extrapolation methods for estimating wind turbine extreme loads, J Sol Energy Eng, 130, 10.1115/1.2931501 Dimitrov, 2016, Comparative analysis of methods for modelling the short-term probability distribution of extreme wind turbine loads, Wind Energy, 19, 717, 10.1002/we.1861 Liu, 2019, Design loads for a large wind turbine supported by a semi-submersible floating platform, Renew Energy, 138, 923, 10.1016/j.renene.2019.02.011 Moriarty, 2004 Chai, 2018, Short-term extreme ice loads prediction and fatigue damage evaluation for an icebreaker, Ships Offshore Struct, 13, 127, 10.1080/17445302.2018.1427316