The Journal of the Korea Contents Association (한국콘텐츠학회논문지)

The Korea Contents Association (한국콘텐츠학회)
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Method for Importance based Streamline Generation on the Massive Fluid Dynamics Dataset

대용량 유동해석 데이터에서의 중요도 기반 스트림라인 생성 방법

  • 이중연 (한국과학기술정보연구원 국가슈퍼컴퓨팅본부) ;
  • 김민아 (한국과학기술정보연구원 국가슈퍼컴퓨팅본부) ;
  • 이세훈 (한국과학기술정보연구원 국가슈퍼컴퓨팅본부)
  • Received : 2018.06.01
  • Accepted : 2018.06.20
  • Published : 2018.06.28
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Abstract

Streamline generation is one of the most representative visualization methods to analyze the flow stream of fluid dynamics dataset. It is a challenging problem, however, to determine the seed locations for effective streamline visualization. Meanwhile, it needs much time to compute effective seed locations and streamlines on the massive flow dataset. In this paper, we propose not only an importance based method to determine seed locations for the effective streamline placements but also a parallel streamline visualization method on the distributed visualization system. Moreover, we introduce case studies on the real fluid dynamics dataset using GLOVE visualization system to evaluate the proposed method.

스트림라인 생성은 유동해석 데이터에서 유동의 흐름을 해석하기 위한 대표적인 가시화 기법이다. 그러나 효과적인 스트림라인 배치를 위한 씨드 포인트의 위치를 결정하는 것은 매우 어려운 문제이다. 한편, 대용량의 유동해석 데이터에서 씨드 포인트 결정과 스트림라인 생성 계산은 매우 오랜 시간을 필요로 한다. 본 논문에서는 효과적인 스트림라인 배치를 위해 유동해석 데이터의 중요도를 기반으로 한 씨드 포인트 결정 방법과 분산병렬 가시화 시스템 환경에서의 병렬 처리 기법을 제안한다. 또한, GLOVE 가시화 시스템에서 실제 유동해석 데이터를 이용한 구현 결과를 소개하고 이를 통해 본 논문의 제안 방법을 검증하고자 한다.

Keywords

References

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