• Journal of Ocean Engineering and Technology
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• v.35 no.2
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• pp.131-140
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• 2021

The paper deals with comparative study of various surrogate models based approximate optimization in the structural design of the passive type deck support frame under design load conditions. The passive type deck support frame was devised to facilitate both transportation and installation of 20,000 ton class topside. Structural analysis was performed using the finite element method to evaluate the strength performance of the passive type deck support frame in its initial design stage. In the structural analysis, the strength performances were evaluated for various design load conditions. The optimum design problem based on surrogate model was formulated such that thickness sizing variables of main structure members were determined by minimizing the weight of the passive type deck support frame subject to the strength performance constraints. The surrogate models used in the approximate optimization were response surface method, Kriging model, and Chebyshev orthogonal polynomials. In the context of numerical performances, the solution results from approximate optimization were compared to actual non-approximate optimization. The response surface method among the surrogate models used in the approximate optimization showed the most appropriate optimum design results for the structure design of the passive type deck support frame.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.161-171
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• 2021

This paper presents the findings of a comparative study on minimum weight design and sensitivity evaluation using different experimental design methods for the structural design of an active-type deck support frame (DSF) developed for the float-over installation of an of shore plant topside. The thickness sizing variables of the structural members of a passive-type DSF were considered the design factors, and the output responses were defined using the weight and strength performances. The design of the experimental methods applied in the comparative study of the minimum weight design and the sensitivity evaluation were the orthogonal array design, Box- Behnken design, and Latin hypercube design. A response surface method was generated for each design of the experiment to evaluate the approximation performance of the design space exploration according to the experimental design, and the accuracy characteristics of the approximation were reviewed. Regarding the minimum weight design, the design results, such as numerical costs and weight minimization, of the experimental design for the best design case, were evaluated. The Box- Behnken design method showed the optimum design results for the structural design of the passive-type DSF.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.5
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• pp.85-95
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• 2021

Structure design sensitivity was evaluated using the orthogonal array experimental method for passive-type deck support frame (DSF) developed for float-over installation of the offshore plant. Moreover, approximation characteristics were also reviewed based on various meta-models. The minimum weight design of the DSF is significantly important for securing both maneuvering performance and buoyancy of a ship equipped with the DSF and guaranteeing structural design safety. The performance strength of the passive type DSF was evaluated through structure analysis based on the finite element method. The thickness of main structure members was applied to design factors, and output responses were considered structure weight and strength performances. Quantitative effects on the output responses for each design factor were evaluated using the orthogonal array experimental method and analysis of variance. The optimum design case was also identified from the orthogonal array experiment results. Various meta-models, such as Chebyshev orthogonal polynomial, Kriging, response surface method, and radial basis function-based neural network, were generated from the orthogonal array experiment results. The results of the orthogonal array experiment were validated using the meta-modeling results. It was found that the radial basis function-based neural network among the meta-models could approximate the design space of the passive type DSF with the highest accuracy.