Multi-scale stochastic dynamic response analysis of offshore risers with lognormal uncertainties

Pinghe Ni, Yong Xia, Jun Li, Hong Hao, Kaiming Bi, Haoran Zuo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

Abstract

This paper presents a multi-scale stochastic dynamic analysis method for offshore structures. The uncertainties in the structural material parameters, such as mass density and Young's modulus, are considered. They are assumed to be lognormal distributions and represented by using the Karhunen–Loeve (KL) and Polynomial Chaos (PC) expansions. Since the variance of the output responses is unknown, the output vibration response is represented by using PC expansion. The multi-scale stochastic analysis is conducted with PC expansions of different orders representing responses at different DOFs defined as three categories, namely, important, less important and the least important ones. Iterated Order Reduced (IOR) model reduction technique is employed to remove the PC coefficients of slave DOFs. Two numerical examples are taken to verify the accuracy and efficiency of the proposed method for the multi-scale stochastic dynamic response analysis of offshore risers. The response statistics such as mean value and variance can be obtained from the proposed method. The results are compared with those from Monte Carlo Simulation (MCS) and Stochastic Finite Element Method (SFEM). Results demonstrate that the computational demand for uncertainty evaluation is greatly reduced, and the accuracy of the results is maintained.

Original languageEnglish
Article number106333
JournalOcean Engineering
Volume189
DOIs
Publication statusPublished - 1 Oct 2019

Keywords

  • Dynamic response analysis
  • KL expansion
  • Model reduction
  • Multi-scale analysis
  • PC expansion
  • Stochastic

ASJC Scopus subject areas

  • Environmental Engineering
  • Ocean Engineering

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