Model prediction of fatigue damage on offshore steel risers due to wave loading using FEA and ANN: A case of Forcados Offshore, Nigeria
1 Marine Engineering Department, Federal University of Petroleum Resources, Effurun, Delta State Nigeria.
2 Center for Maritime and Offshore Studies, Federal University of Petroleum Resources, Effurun Nigeria.
Research Article
International Journal of Engineering Research Updates, 2023, 04(01), 020–033.
Article DOI: 10.53430/ijeru.2023.4.1.0014
Publication history:
Received on 06 January 2023; revised on 15 February 2023; accepted on 17 February 2023
Abstract:
This study aims at providing a model prediction technique for the fatigue life of offshore steel risers using a hybrid of finite element analysis and the artificial neural network (FEA-ANN) model. A 200 days’ environmental load from Forcados sea state in West Africa offshore was used in training the FEA-ANN model to predict fatigue. The prediction result showed that the mean square error (MSE) was 0.3329 and the analysis from the regression was 0.9999. The result from the training showed a high performance and the regression analysis of the model was seen to be good.
Keywords:
Offshore riser; Fatigue; Wave Load; Finite element analysis; Artificial neural network
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0