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Examines in Marine Biology & Oceanography

Fast Prediction for Damaged Ship Roll Motion in Waves by an Aggregation Model based on GA

  • Open or CloseXinran Liu and Tingqiu Li*

    School of Naval Architecture, Ocean and Energy Power Engineering, China

    *Corresponding author:Tingqiu Li, School of Naval Architecture, Ocean and Energy Power Engineering, 1040 Peace Avenue, Wuhan 430063, China

Submission: January 19, 2023;Published: January 31, 2023

DOI: 10.31031/EIMBO.2023.05.000609

ISSN : 2578-031X
Volume5 Issue3


In this paper, we discuss one typical case of the standard damaged DTMB5415 using CFD (Computational Fluid Dynamics) with a fast multigrid technique combined with a regression aggregation model, which could achieve fast prediction of the damaged ship roll motion in waves. The relevant regression aggregation model consists of a Back Propagation Neural Network (BPNN), Radial Basis Function (RBF), Support Vector Regression (SVR), and the Kriging model. By constructing the regression aggregation model with the two-parameter input (the environmental variables like wave steepness δ and period T) and one output (the system’s dynamic response), the predicted value is well compared with the CFD value that can verify the universality and feasibility of the aggregation model. In particular, the Genetic Algorithm (GA) can coordinate the generalization performance of every single model and has universality while guaranteeing high prediction accuracy. This study provides a reference for selecting the prediction models of ship motion response.

Keywords: CFD; Fast multigrid technique; Regression aggregation model; Fast prediction of damaged ship motions in waves

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