Fatigue life prediction of ship and offshore structures under wide-banded non-Gaussian random loadings: Part II: extension to wide-banded non-Gaussian random processes

Abstract

Ships and offshore structures are exposed to various cyclic loads such as winds, waves and currents in their lifetime. These loads can result in fatigue failures at hot spots, and to prevent such failures, accurate estimates of the fatigue life of hot spots should be performed in the design stage. The cycle distribution indicative of the probability distribution of cycles is an important factor in estimation of fatigue damage to mechanical systems in frequency-domain methods. It depends on not only the counting method but also the statistical properties of random processes. Many studies deriving both theoretical and approximate models of rainflow-counted cycle distributions in wide-banded non-Gaussian processes have been conducted. However, some of the existing models require multi-dimensional integrations with much computational time or yield inaccurate representations of rainflow-cycle distributions. Therefore, a new model providing accurate cycle distributions is needed for fatigue analysis of offshore structures under wide-banded non-Gaussian random loadings.This paper consists of two parts: Part I is devoted to the development of a new approximation model for the joint probability distribution of mean and amplitude of rainflow-counted cycles in wide-band Gaussian random processes. This probabilistic model derived in Gaussian random processes could be transformed to the corresponding non-Gaussian processes through non-linear transformation technique. Part II discusses the detailed procedure and accuracy of the proposed model in fatigue damage assessment of offshore structures under non-Gaussian random loading. Rainflow cycle distribution in wide-band non-Gaussian processes is derived using the Hermite transformation function. The accuracy of the proposed model is verified through two numerical examples, and compared to other existing models.