eprintid: 9988
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/99/88
datestamp: 2023-11-09 16:36:38
lastmod: 2023-11-09 16:36:38
status_changed: 2023-11-09 16:30:18
type: conference_item
metadata_visibility: show
creators_name: Wong, E.W.C.
creators_name: Choi, H.S.
creators_name: Kim, D.K.
creators_name: Hashim, F.M.
title: Development of ANN Model for the Prediction of VIV Fatigue Damage of Top-tensioned Riser
ispublished: pub
keywords: Buoyancy; Environmental engineering; Forecasting; Marine risers; Modal analysis; Neural networks; Offshore oil well production, Computation time; Damage prediction models; Input parameter; Outer diameters; Prediction model; Top tensioned risers; Uniform currents; Vortex-induced vibration, Fatigue damage
note: cited By 4; Conference of 2018 International Conference on Civil, Offshore and Environmental Engineering 2018, ICCOEE 2018 ; Conference Date: 13 August 2018 Through 14 August 2018; Conference Code:140464
abstract: Marine riser experiences vortex-induced vibration (VIV) caused by current, leading to fatigue damage if VIV is not considered in design of riser. Estimation of VIV fatigue damage is essential in designing feasible and operable riser. A simplified approach for predicting fatigue damage is required to reduce the computation time to analyze the fatigue damage. This study aims to explore the applicability of artificial neural network (ANN) approach in developing top-tensioned riser fatigue damage prediction model. A total of 2100 riser model is generated with different combination of four main input parameters: riser outer diameter, wall thickness, top tension and uniform current velocity. The modal analysis is performed using OrcaFlex and VIV fatigue damage of the riser is computed using SHEAR7. The four input parameters and corresponding fatigue damage results make up the database for training a 2-layer neural network. Weight and bias values acquired from the training of ANN are used to develop the VIV fatigue damage prediction model of the riser. The results show ANN approach is suitable for prediction of the riser fatigue damage due to VIV. The proposed approach requires further refinements and extension to more input features to improve the accuracy and usefulness of the developed prediction model. © The Authors, published by EDP Sciences, 2018.
date: 2018
publisher: EDP Sciences
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055471826&doi=10.1051%2fmatecconf%2f201820301013&partnerID=40&md5=0521da50ed125539cc163138eb4a6230
id_number: 10.1051/matecconf/201820301013
full_text_status: none
publication: MATEC Web of Conferences
volume: 203
refereed: TRUE
issn: 2261236X
citation:   Wong, E.W.C. and Choi, H.S. and Kim, D.K. and Hashim, F.M.  (2018) Development of ANN Model for the Prediction of VIV Fatigue Damage of Top-tensioned Riser.  In: UNSPECIFIED.