• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.6
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• pp.33-43
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• 2021

A60 class bulkhead penetration piece is a fire resistance system installed on a bulkhead compartment to protect lives and to prevent flame diffusion in a fire accident on a ship and offshore plant. This study focuses on the approximate optimization of the fire resistance design of the A60 class bulkhead penetration piece using a multi-island genetic algorithm. Transient heat transfer analysis was performed to evaluate the fire resistance design of the A60 class bulkhead penetration piece. For approximate optimization, the bulkhead penetration piece length, diameter, material type, and insulation density were considered discrete design variables; moreover, temperature, cost, and productivity were considered constraint functions. The approximate optimum design problem based on the meta-model was formulated by determining the discrete design variables by minimizing the weight of the A60 class bulkhead penetration piece subject to the constraint functions. The meta-models used for the approximate optimization were the Kriging model, response surface method, and radial basis function-based neural network. The results from the approximate optimization were compared to the actual results of the analysis to determine approximate accuracy. We conclude that the radial basis function-based neural network among the meta-models used in the approximate optimization generates the most accurate optimum design results for the fire resistance design of the A60 class bulkhead penetration piece.

• 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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.5
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• pp.513-521
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• 2012

The aim of this study was to determine the brittle fracture behavior of reactor pressure vessel steel by considering the temperature dependence of a damage model. A multi-island genetic algorithm was linked to a Weibull stress model, which is the model typically used for brittle fracture evaluation, to improve the calibration procedure. The improved calibration procedure and fracture toughness test data for SA508 carbon steel at the temperatures $-60^{\circ}C$, $-80^{\circ}C$, and $-100^{\circ}C$ were used to decide the damage parameters required for the brittle fracture evaluation. The model was found to show temperature dependence, similar to the case of NUREG/CR-6930. Finally, on the basis of the quantification of the difference between 2- and 3-parameter Weibull stress models, an engineering equation that can help obtain more realistic fracture behavior by using the simpler 2-parameter Weibull stress model was proposed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.1
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• pp.579-584
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• 2017

The damper oil seal of a high-speed railway vehicle is made from nitrile butadiene rubber (NBR) in order to prevent lubricant from leaking into the damper and to stop harmful contaminants from entering the external environment while in service. Oil leakage through the seal primarily occurs from fatigue failure of the damper. Cumulative damage of the seal occurs due to the contact force between the rod and the rubber during movement due to track irregularities and cants, among other factors. Thus, the design of the oil seal should minimize the maximum principal strain at weak points. In this study, the optimal cross section of the damper oil seal was found using the multi-island genetic algorithm method to improve the durability of the damper. The optimal shape of the oil seal was derived using process automation and design optimization software. Nonlinear material properties for finite element analysis (FEA) of the rubber were determined by Marlow's model. The nonlinear FEA confirmed that the maximum principal strain at the oil leakage point was decreased 24% between the initial design and the optimum design.