Multi-Objective Optimization for Hydrodynamic Performance of A Semi-Submersible FOWT Platform Based on Multi-Fidelity Surrogate Models and NSGA-Ⅱ Algorithms  

作　　者：QIAO Dong-sheng MEI Hao-tian QIN Jian-min TANG Guo-qiang LU Lin OU Jin-ping 

机构地区：[1]State Key Laboratory of Coastal and Offshore Engineering,Dalian University of Technology,Dalian 116024,China [2]College of Civil Engineering and Architecture,Dalian University,Dalian 116022,China

出　　处：《China Ocean Engineering》2024年第6期932-942,共11页中国海洋工程(英文版)

基　　金：financially supported by the National Natural Science Foundation of China(Grant No.52371261);the Science and Technology Projects of Liaoning Province(Grant No.2023011352-JH1/110).

摘　　要：This study delineates the development of the optimization framework for the preliminary design phase of Floating Offshore Wind Turbines(FOWTs),and the central challenge addressed is the optimization of the FOWT platform dimensional parameters in relation to motion responses.Although the three-dimensional potential flow(TDPF)panel method is recognized for its precision in calculating FOWT motion responses,its computational intensity necessitates an alternative approach for efficiency.Herein,a novel application of varying fidelity frequency-domain computational strategies is introduced,which synthesizes the strip theory with the TDPF panel method to strike a balance between computational speed and accuracy.The Co-Kriging algorithm is employed to forge a surrogate model that amalgamates these computational strategies.Optimization objectives are centered on the platform’s motion response in heave and pitch directions under general sea conditions.The steel usage,the range of design variables,and geometric considerations are optimization constraints.The angle of the pontoons,the number of columns,the radius of the central column and the parameters of the mooring lines are optimization constants.This informed the structuring of a multi-objective optimization model utilizing the Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ)algorithm.For the case of the IEA UMaine VolturnUS-S Reference Platform,Pareto fronts are discerned based on the above framework and delineate the relationship between competing motion response objectives.The efficacy of final designs is substantiated through the time-domain calculation model,which ensures that the motion responses in extreme sea conditions are superior to those of the initial design.

关 键 词：semi-submersible FOWT platforms Co-Kriging neural network algorithm multi-fidelity surrogate model NSGA-II multi-objective algorithm Pareto optimization 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]