대한조선학회논문집 (Journal of the Society of Naval Architects of Korea)

  • 제59권6호
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  • Pages.393-399
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  • 2022
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  • 1225-1143(pISSN)
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  • 2287-7355(eISSN)
대한조선학회 (The Society of Naval Architects of Korea)
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그래프 신경망을 이용한 단순 선박 선형의 저항성능 시뮬레이션

Resistance Performance Simulation of Simple Ship Hull Using Graph Neural Network

  • 박태원 (동명대학교 조선해양시뮬레이션센터) ;
  • 김인섭 (한국해양교통안전공단 스마트안전연구실) ;
  • 이훈 ((주)토탈소프트뱅크 물류시스템연구소) ;
  • 박동우 (동명대학교 조선해양공학과)
  • TaeWon, Park (Shipbuilding & Marine Simulation Center, Tongmyong University) ;
  • Inseob, Kim (Smart Safety Research Department, Korea Maritime Transportation Safety Authority) ;
  • Hoon, Lee (Logistics System Institute, Total Soft Bank, Ltd.) ;
  • Dong-Woo, Park (School of Naval Architecture & Ocean Engineering, Tongmyong University)
  • 투고 : 2022.09.27
  • 심사 : 2022.11.01
  • 발행 : 2022.12.20
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초록

During the ship hull design process, resistance performance estimation is generally calculated by simulation using computational fluid dynamics. Since such hull resistance performance simulation requires a lot of time and computation resources, the time taken for simulation is reduced by CPU clusters having more than tens of cores in order to complete the hull design within the required deadline of the ship owner. In this paper, we propose a method for estimating resistance performance of ship hull by simulation using a graph neural network. This method converts the 3D geometric information of the hull mesh and the physical quantity of the surface into a mathematical graph, and is implemented as a deep learning model that predicts the future simulation state from the input state. The method proposed in the resistance performance experiment of simple hull showed an average error of about 3.5 % throughout the simulation.

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과제정보

이 연구는 산업통상자원부의 스마트특성화 기반구축사업 중 실물-가상연계 조선해양 기본설계 기술지원 사업 (P0021213) 과제 및 산업통상자원부와 한국산업기술진흥원의 지역혁신클러스터R&D 사업 (P0015330)의 지원을 받아 수행되었습니다.

참고문헌

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