Abstract
The geometry of systems including the marine engineering problems needs to be optimized in the initial design stage. However, the performance analysis using commercial code is generally time consuming. To solve this problem, many engineers perform the optimization process using the response surface method (RSM) to predict the system performance, but RSM presents some prediction errors for nonlinear systems. The major objective of this research is to establish an optimal design framework for marine systems. The framework is composed of three parts: definition of geometry, generation of response surface, and optimization process. To reduce the time for performance analysis and minimize the prediction errors, the response surface is generated using the back-propagation neural network (BPN) which is considered as neuro-response surface method (NRSM). The optimization process is done for the generated response surface by non-dominated sorting genetic algorithm-II (NSGA-II).A nonlinear mathematical function problem is used to compare the accuracy of response surface generated by RSM and NRSM. Through case study of substructure of floating offshore wind turbine considering hydrodynamic performance, we have confirmed the proposed framework applicability for marine system optimization. In the future, we will try to apply the optimization problem for marine systems using the constructed framework.
Published Version
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