Journal of the Korean Society of Manufacturing Process Engineers (한국기계가공학회지)

The Korean Society of Manufacturing Process Engineers (한국기계가공학회)
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Approximate Optimization with Discrete Variables of Fire Resistance Design of A60 Class Bulkhead Penetration Piece Based on Multi-island Genetic Algorithm

다중 섬 유전자 알고리즘 기반 A60 급 격벽 관통 관의 방화설계에 대한 이산변수 근사최적화

  • Park, Woo-Chang (Department of Naval Architecture & Ocean Engineering, Mokpo National University) ;
  • Song, Chang Yong (Department of Naval Architecture & Ocean Engineering, Mokpo National University)
  • 박우창 (목포대학교 조선해양공학과) ;
  • 송창용 (목포대학교 조선해양공학과)
  • Received : 2021.02.18
  • Accepted : 2021.04.13
  • Published : 2021.06.30
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Abstract

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.

Keywords

Acknowledgement

본 연구는 중소벤처기업부에서 지원하는 2020년도 산학연 Collabo R&D사업(No. S2910737)과 교육부의 재원으로 한국연구재단의 지원을 받아 수행된 지자체-대학 협력기반 지역혁신 사업의 연구결과입니다.

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