This invention proposes a method and
system for reducing the order of FPSO models by integrating time-recurrent neural networks and
fuzzy logic, belonging to the field of
marine engineering structural analysis and
intelligent modeling technology. Addressing the problem of large
degrees of freedom and low computational efficiency in FPSO finite element models, this invention extracts key
degrees of freedom from the high-dimensional finite
element model to construct low-dimensional feature vectors. It then uses a
fuzzy logic system to perform regularized modeling of the dominant mechanical properties of the structure, achieving initial
order reduction. Furthermore, a time-
recurrent neural network is introduced to compensate for high-order residual effects that are difficult to accurately characterize using the
fuzzy logic model, ultimately resulting in a low-dimensional, high-fidelity reduced-order model. The proposed method significantly reduces the degree of freedom and parameter dimension of the finite
element model while maintaining the main mechanical properties of the structure, effectively reducing computational complexity and improving the model's applicability and practical
engineering value in applications such as rapid finite
element analysis, online structural response prediction, and
engineering decision support.