• International Journal of Naval Architecture and Ocean Engineering
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• v.7 no.4
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• pp.750-769
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• 2015

The geometry of engineering systems affects their performances. For this reason, the shape of engineering systems needs to be optimized in the initial design stage. However, engineering system design problems consist of multi-objective optimization and the performance analysis using commercial code or numerical analysis is generally time-consuming. To solve these problems, many engineers perform the optimization using the approximation model (response surface). The Response Surface Method (RSM) is generally used to predict the system performance in engineering research field, but RSM presents some prediction errors for highly nonlinear systems. The major objective of this research is to establish an optimal design method for multi-objective problems and confirm its applicability. The proposed process 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 approximation model is generated using the Backpropagation Artificial Neural Network (BPANN) which is considered as Neuro-Response Surface Method (NRSM). The optimization is done for the generated response surface by non-dominated sorting genetic algorithm-II (NSGA-II). Through case studies of marine system and ship structure (substructure of floating offshore wind turbine considering hydrodynamics performances and bulk carrier bottom stiffened panels considering structure performance), we have confirmed the applicability of the proposed method for multi-objective side constraint optimization problems.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.4
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• pp.286-294
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• 2016

In the initial design stage, the geometry of systems needs to be optimized regarding its performance. However, performance analysis is very time-consuming. Therefore, optimization becomes difficult/impossible problems because we need to evaluate the system performance for alternative design cases. To overcome this problem, many researchers perform prediction of system performance using the approximation model. The response surface method (RSM) is typically used to predict the system performance in the various research fields, but it presents prediction errors for highly nonlinear systems. The major objective of this paper is to propose a proper prediction method for marine system problems. Case studies of marine systems (the substructure of a floating offshore wind turbine considering hydrodynamic performance and bulk carrier bottom stiffened panels considering structure performance) verify that the proposed method is applicable to performance prediction in marine systems.