• 한국해양공학회지
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• 제32권5호
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• pp.310-323
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• 2018

In order to protect lives and prevent large-scale injuries in the event of a fire on a ship or an offshore plant, most classification societies are strengthening their fire resistance designs of relevant cargo holds and accommodation compartments to keep flames from being transferred from a fire point to other compartments. Particularly in critical compartments, where flames should not propagate for a certain period of time, such as the A60 class division, both the airtightness and fire-resistant design of a piece passing through a bulkhead are subject to the Safety of Life at Sea Convention (SOLAS) issued by the International Maritime Organization (IMO). In order to verify the suitability of a fire-resistant design for such a penetrating piece, the fire test procedure prescribed by the Maritime Safety Committee (MSC) must be carried out. However, a numerical simulation should first be conducted to minimize the time and cost of the fire resistance test. In this study, transient thermal analyses based on the finite element method were applied to investigate the heat transfer characteristics of a bulkhead penetration piece for the A60 class compartment. In order to determine a rational bulkhead penetration piece design, the transient heat transfer characteristics according to the variation of design parameters such as the diameter, length, and material were reviewed. The verification of the design specification based on a numerical analysis of the transient heat transfer performed in this study will be discussed in the following research paper for the actual fire protection test of the A60 class bulkhead penetration piece.

• 한국해양공학회지
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• 제33권4호
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• pp.340-349
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• 2019

In the case of a fire accident on a ship or an offshore plant, the design of the bulkhead penetration piece must be verified via a fire test procedure (FTP), as specified by the Maritime Safety Committee (MSC). The purpose of this study is to verify both the numerical analysis results and the design specifications for penetration pieces that could be applied to the A60 class bulkhead division. In this study, the FTP was carried out in accordance with the test procedure prescribed in the MSC regulation. In order to review the fire resistance performance according to the material type, bulkhead penetration pieces for the FTP were made from brass, carbon steel for machine structures (S45C), and austenite stainless steel (SUS316). In addition, spray-type insulation and mechanical fastener-type insulation were applied to investigate the fire resistance performance according to the type of insulation. To verify the heat transfer numerical analysis results for the A60 class bulkhead penetrating piece from this test study, the design specifications of the penetrating piece material and the insulation type applicable to a ship and an offshore plant were identified.

• 한국기계가공학회지
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• 제20권6호
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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.