Reliability-based design optimization of offshore wind turbine support structures using RBF surrogate model

Abstract

An efficient reliability-based design optimization method for the support structures of monopile offshore wind turbines is proposed herein. First, parametric finite element analysis (FEA) models of the support structure are established by considering stochastic variables. Subsequently, a surrogate model is constructed using a radial basis function (RBF) neural network to replace the time-consuming FEA. The uncertainties of loads, material properties, key sizes of structural components, and soil properties are considered. The uncertainty of soil properties is characterized by the variabilities of the unit weight, friction angle, and elastic modulus of soil. Structure reliability is determined via Monte Carlo simulation, and five limit states are considered, i.e., structural stresses, tower top displacements, mudline rotation, buckling, and natural frequency. Based on the RBF surrogate model and particle swarm optimization algorithm, an optimal design is established to minimize the volume. Results show that the proposed method can yield an optimal design that satisfies the target reliability and that the constructed RBF surrogate model significantly improves the optimization efficiency. Furthermore, the uncertainty of soil parameters significantly affects the optimization results, and increasing the monopile diameter is a cost-effective approach to cope with the uncertainty of soil parameters.

Graphical abstract

Keywords

reliability-based design optimization / offshore wind turbine / parametric finite element analysis / RBF surrogate model / uncertain soil parameter

References

[1] WilliamsRZhaoFLeeJ. Global Offshore Wind Report 2022. Global Wind Energy Council Technical Report. 2022

[2] Ma H, Chen C. Scour protection assessment of monopile foundation design for offshore wind turbines. Ocean Engineering, 2021, 231: 109083

[3] HamiltonBBattenbergLBieleckiMBlochCDeckerTFrantzisLPaidipatiJWicklessAZhaoF. Offshore Wind Market and Economic Analysis: Annual Market Assessment. Navigant Consulting Inc. Technical Report. 2013

[4] Gentils T, Wang L, Kolios A. Integrated structural optimisation of offshore wind turbine support structures based on finite element analysis and genetic algorithm. Applied Energy, 2017, 199: 187–204

[5] Tian X, Sun X, Liu G, Deng W, Wang H, Li Z, Li D. Optimization design of the jacket support structure for offshore wind turbine using topology optimization method. Ocean Engineering, 2022, 243: 110084

[6] Kim H G, Kim B J. Design optimization of conical concrete support structure for offshore wind turbine. Energies, 2020, 13(18): 4876

[7] O’Leary K, Pakrashi V, Kelliher D. Optimization of composite material tower for offshore wind turbine structures. Renewable Energy, 2019, 140: 928–942

[8] Ghasemi H, Brighenti R, Zhuang X, Muthu J, Rabczuk T. Optimal fiber content and distribution in fiber-reinforced solids using a reliability and NURBS based sequential optimization approach. Structural and Multidisciplinary Optimization, 2015, 51(1): 99–112

[9] Wang L, Kolios A, Liu X, Venetsanos D, Cai R. Reliability of offshore wind turbine support structures: A state-of-the-art review. Renewable & Sustainable Energy Reviews, 2022, 161: 112250

[10] Ghasemi H, Kerfriden P, Bordas S P A, Muthu J, Zi G, Rabczuk T. Probabilistic multiconstraints optimization of cooling channels in ceramic matrix composites. Composites. Part B, Engineering, 2015, 81: 107–119

[11] Lee Y S, Choi B L, Lee J H, Kim S Y, Han S. Reliability-based design optimization of monopile transition piece for offshore wind turbine system. Renewable Energy, 2014, 71: 729–741

[12] Yang H, Zhu Y, Lu Q, Zhang J. Dynamic reliability based design optimization of the tripod sub-structure of offshore wind turbines. Renewable Energy, 2015, 78: 16–25

[13] Kamel A, Dammak K, Yangui M, Hami A E, Jdidia M B, Hammami L, Haddar M. A Reliability optimization of a coupled soil structure interaction applied to an offshore wind turbine. Applied Ocean Research, 2021, 113: 102641

[14] Phoon K K, Cao Z J, Ji J, Leung Y F, Najjar S, Shuku T, Tang C, Yin Z Y, Ikumasa Y, Ching J Y. Geotechnical uncertainty, modeling, and decision making. Soil and Foundation, 2022, 62(5): 101189

[15] Huang Y, He Z, Yashima A, Chen Z, Li C. Multi-objective optimization design of pile-anchor structures for slopes based on reliability theory considering the spatial variability of soil properties. Computers and Geotechnics, 2022, 147: 104751

[16] Gong W, Huang H, Juang C H, Wang L. Simplified-robust geotechnical design of soldier pile-anchor tieback shoring system for deep excavation. Marine Georesources and Geotechnology, 2017, 35(2): 157–169

[17] Haldar S, Sharma J, Basu D. Probabilistic analysis of monopile-supported offshore wind turbine in clay. Soil Dynamics and Earthquake Engineering, 2018, 105: 171–183

[18] Carswell W, Arwade S R, DeGroot D J, Lackner M A. Soil−structure reliability of offshore wind turbine monopile foundations. Wind Energy, 2015, 18(3): 483–498

[19] Oh K Y, Nam W. A fast Monte−Carlo method to predict failure probability of offshore wind turbine caused by stochastic variations in soil. Ocean Engineering, 2021, 223: 108635

[20] Rabiei M, Choobbasti A J. Innovative piled raft foundations design using artificial neural network. Frontiers of Structural and Civil Engineering, 2020, 14(1): 138–146

[21] Nariman N A, Ramadan A M, Mohammad I I. Application of coupled XFEM-BCQO in the structural optimization of a circular tunnel lining subjected to a ground motion. Frontiers of Structural and Civil Engineering, 2019, 13(6): 1495–1509

[22] Ni P, Li J, Hao H, Zhou H. Reliability based design optimization of bridges considering bridge-vehicle interaction by Kriging surrogate model. Engineering Structures, 2021, 246: 112989

[23] Liu X, Lu M, Chai Y, Tang J, Gao J. A comprehensive framework for HSPF hydrological parameter sensitivity, optimization and uncertainty evaluation based on SVM surrogate model—A case study in Qinglong River watershed, China. Environmental Modelling & Software, 2021, 143: 105126

[24] Ozcanan S, Atahan A O. RBF surrogate model and EN1317 collision safety-based optimization of two guardrails. Structural and Multidisciplinary Optimization, 2019, 60(1): 343–362

[25] Ji Y, Yang Z, Ran J, Li H. Multi-objective parameter optimization of turbine impeller based on RBF neural network and NSGA-II genetic algorithm. Energy Reports, 2021, 7: 584–593

[26] Xu K, Wang G, Zhang L, Wang L, Yun F, Sun W, Wang X, Chen X. Multi-objective optimization of jet pump based on RBF neural network model. Journal of Marine Science and Engineering, 2021, 9(2): 236

[27] Herbert-Acero J F, Probst O, Réthoré P E, Larsen G C, Castillo-Villar K K. A review of methodological approaches for the design and optimization of wind farms. Energies, 2014, 7(11): 6930–7016

[28] Chen J, Yang R, Ma R, Li J. Design optimization of wind turbine tower with lattice-tubular hybrid structure using particle swarm algorithm. Structural Design of Tall and Special Buildings, 2016, 25(15): 743–758

[29] Ma Y, Zhang A, Yang L, Hu C, Bai Y. Investigation on optimization design of offshore wind turbine blades based on particle swarm optimization. Energies, 2019, 12(10): 1972

[30] JonkmanJButterfieldSMusialWScottG. Definition of a 5-MW Reference Wind Turbine for Offshore System Development. National Renewable Energy Laboratory Technical Report (No. NREL/TP-500–38060). 2009

[31] Carswell W, Arwade S R, Johansson J, DeGroot D J. Influence of foundation damping on offshore wind turbine monopile design loads. Marine Structures, 2022, 83: 103154

[32] Liang F, Yuan Z, Liang X, Zhang H. Seismic response of monopile-supported offshore wind turbines under combined wind, wave and hydrodynamic loads at scoured sites. Computers and Geotechnics, 2022, 144: 104640

[33] IEC61400-3:. Wind Turbines-Part 3: Design Requirements for Offshore Wind Turbines. Geneva: International Electrotechnical Commission, 2009

[34] Chew K H, Tai K, Ng E Y K, Muskulus M. Analytical gradient-based optimization of offshore wind turbine substructures under fatigue and extreme loads. Marine Structures, 2016, 47: 23–41

[35] JonkmanJ MBuhlJ M L. FAST User’s Guide Version 6.0. National Renewable Energy Laboratory Technical Report (No. NREL/EL-500-38230). 2005

[36] JonkmanB J. TurbSim User’s Guide Version 1.50. National Renewable Energy Laboratory Technical Report (No. NREL/TP-500-46198). 2009

[37] DNV-OS-J101: Design of Offshore Wind Turbine Structures. Oslo: Det Norske Veritas, 2014

[38] DNV-RP-C205: Environmental Conditions and Environmental Loads. Oslo: Det Norske Veritas, 2010

[39] Stewart G M, Robertson A, Jonkman J, Lackner M A. The creation of a comprehensive metocean data set for offshore wind turbine simulations. Wind Energy, 2016, 19(6): 1151–1159

[40] Yan Y, Yang Y, Bashir M, Li C, Wang J. Dynamic analysis of 10 MW offshore wind turbines with different support structures subjected to earthquake loadings. Renewable Energy, 2022, 193: 758–777

[41] Cao G, Chen Z, Wang C, Ding X. Dynamic responses of offshore wind turbine considering soil nonlinearity and wind-wave load combinations. Ocean Engineering, 2020, 217: 108155

[42] Chen S, Xing J, Yang L, Zhang H, Luan Y, Chen H, Liu H. Numerical modelling of new flap-gate type breakwater in regular and solitary waves using one-fluid formulation. Ocean Engineering, 2021, 240: 109967

[43] Yan Y, Li C, Li Z. Buckling analysis of a 10 MW offshore wind turbine subjected to wind-wave-earthquake loadings. Ocean Engineering, 2021, 236: 109452

[44] Jung S, Kim S R, Patil A, Hung L C. Effect of monopile foundation modeling on the structural response of a 5-MW offshore wind turbine tower. Ocean Engineering, 2015, 109: 479–488

[45] Kawa M, Baginska I, Wyjadlowski M. Reliability analysis of sheet pile wall in spatially variable soil including CPTu test results. Archives of Civil and Mechanical Engineering, 2019, 19(2): 598–613

[46] DamianiR RSongHRobertsonA NJonkmanJ M. Assessing the importance of nonlinearities in the development of a substructure model for the wind turbine CAE tool FAST. In: Proceedings of OMAE 2013—32nd International Conference on Ocean, Offshore and Arctic Engineering. New York: American Society of Mechanical Engineers, 2013, V008T09A093

[47] Al-Sanad S, Wang L, Parol J, Kolios A. Reliability-based design optimisation framework for wind turbine towers. Renewable Energy, 2021, 167: 942–953

[48] DNVGL-RP-C202: Buckling Strength of Shells. Oslo: Det Norske Veritas, 2017

[49] NORSOKN-004: Design of Steel Structures. Oslo: Norwegian Technology Standards Institution, 2013

Acknowledgements

This study was supported by the National Natural Science Foundation of China (Grant No. 12072104) and the National Key R&D Program of China (No. 2018YFC0406703).

Conflict of Interest

The authors declare that they have no conflict of interest.

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