Datas for optimization

To resolve the long-standing conflict between accuracy and efficiency in designing mooring systems for deep-water cylindrical FPSO (Floating Production Storage and Offloading), an intelligent design framework that integrates high-fidelity numerical simulation, a BP neural network surrogate model, and a global optimization algorithm was proposed and validated. The method established and verified a high-precision coupled dynamic model using OrcaFlex and analyzed the impact of key parameters through 720 sets of simulations. Based on these simulation data, a BP neural network surrogate model was further designed and trained, with design variables (such as segmented line length, number of cables, mooring radius, etc.) as inputs and key responses (such as platform displacement, line tension, etc.) as outputs. With the goal of minimizing the total material usage and in accordance with relevant industry standards as constraints, global optimization was carried out using the Levenberg-Marquardt algorithm. The results show that the neural network model has a high prediction accuracy (coefficient of determination R2>0.98). The optimal solution obtained is a 3×3 mooring layout with a mooring radius of 1670.50 m. Under the premise of satisfying the API specification through calculation verification, the maximum platform displacements in the intact and single-cable failure conditions are 80.61 m and 85.23 m respectively, and the maximum mooring tensions are 13507 kN and 15455 kN respectively, all of which meet the safety thresholds. This framework ultimately achieves a 49% reduction in material usage and cost savings of $1,095,238, while improving computational efficiency by a factor of 124.