• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.2
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• pp.377-386
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• 2021

The A60 class deck penetration piece is a fire-resistant system installed on a horizontal compartment to prevent flame spreading and protect lives in fire accidents in ships and offshore plants. This study deals with approximate optimization using discrete variables for the fire resistance design of an A60 class deck penetration piece using different surrogate models and a genetic algorithm. Transient heat transfer analysis was performed to evaluate the fire resistance design of the A60 class deck penetration piece. For the approximate optimization of the piece, the length, diameter, material type, and insulation density were applied to discrete design variables, and temperature, productivity, and cost constraints were considered. The approximate optimum design problem based on the surrogate models was formulated such that the discrete design variables were determined by minimizing the weight of the piece subjected to the constraints. The surrogate models used in the approximate optimization were the response surface model, Kriging model, and radial basis function-based neural network. The approximate optimization results were compared with the actual analysis results in terms of approximate accuracy. The radial basis function-based neural network showed the most accurate optimum design results for the fire resistance design of the A60 class deck penetration piece.