Abstract
Marine umbilical is one of the key equipment for subsea oil and gas exploitation,which is usually integrated by a great number of different functional components with multi-layers.The layout of these components directly affects manufacturing,operation and storage performances of the umbilical.For the multi-layer cross-sectional layout design of the umbilical,a quantifiable multi-objective optimization model is established according to the operation and storage requirements.Considering the manufacturing factors,the multi-layering strategy based on contact point identification is introduced for a great number of functional components.Then,the GA-GLM global optimization algorithm is pro-posed combining the genetic algorithm and the generalized multiplier method,and the selection operator of the genetic algorithm is improved based on the steepest descent method.Genetic algorithm is used to find the optimal solution in the global space,which can converge from any initial layout to the feasible layout solution.The feasible layout solution is taken as the initial value of the generalized multiplier method for fast and accurate solution.Finally,taking umbilicals with a great number of components as examples,the results show that the cross-sectional performance of the umbilical obtained by optimization algorithm is better and the solution efficiency is higher.Meanwhile,the multi-layering strategy is effective and feasible.The design method proposed in this paper can quickly obtain the optimal multi-layer cross-sectional layout,which replaces the manual design,and provides useful reference and guid-ance for the umbilical industry.
基金项目
National Natural Science Foundation of China(52001088)
National Natural Science Foundation of China(52271269)
National Natural Science Foundation of China(U1906233)
Natural Science Foundation of Heilongjiang Province(LH2021E050)
State Key Laboratory of Ocean Engineering(GKZD010084)
Liaoning Province's Xing Liao Talents Program(XLYC2002108)
Dalian City Supports Innovation and Entrepreneurship Projects for High-Level Talents(2021RD16)
NETL
NSTL
维普
万方数据