Journal of the Korean Institute of Gas (한국가스학회지)

The Korean Institute of GAS (한국가스학회)
권호 표지
DOI QR코드

DOI QR Code

Cellular Automata Simulation System for Emergency Response to the Dispersion of Accidental Chemical Releases

사고로 인한 유해화학물질 누출확산의 대응을 위한 Cellular Automata기반의 시뮬레이션 시스템

  • Shin, Insup Paul (Korea International School, Seongnam) ;
  • Kim, Chang Won (Department of Chemical Engineering, Myongji University) ;
  • Kwak, Dongho (Department of Chemical Engineering, Myongji University) ;
  • Yoon, En Sup (Engineering Development Research Center, Seoul National University) ;
  • Kim, Tae-Ok (Department of Chemical Engineering, Myongji University)
  • Received : 2018.11.14
  • Accepted : 2018.12.22
  • Published : 2018.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Cellular automata have been applied to simulations in many fields such as astrophysics, social phenomena, fire spread, and evacuation. Using cellular automata, this study develops a model for consequence analysis of the dispersion of hazardous chemicals, which is required for risk assessments of and emergency responses for frequent chemical accidents. Unlike in cases of detailed plant safety design, real-time accident responses require fast and iterative calculations to reduce the uncertainty of the distribution of damage within the affected area. EPA ALOHA and KORA of National Institute of Chemical Safety have been popular choices for these analyses. However, this study proposes an initiative to supplement the model and code continuously and is different in its development of free software, specialized for small and medium enterprises. Compared to the full-scale computational fluid dynamics (CFD), which requires large amounts of computation time, the relative accuracy loss is compromised, and the convenience of the general user is improved. Using Python open-source libraries as well as meteorological information linkage, it is made possible to expand and update the functions continuously. Users can easily obtain the results by simply inputting the layout of the plant and the materials used. Accuracy is verified against full-scale CFD simulations, and it will be distributed as open source software, supporting GPU-accelerated computing for fast computation.

Cellular automata는 천체물리, 사회현상, 화재 확산 및 피난 등 많은 분야의 시뮬레이션에 활용되고 있다. 본 연구는 빈번히 발생하고 있는 화학사고에 대비한, 위험성평가 및 비상대응계획 작성시 요구되는 화학물질 확산 시뮬레이션을 위한 보급용 모델을 cellular automata를 기반으로 개발하였다. 상세한 플랜트 안전설계용과는 달리, 실시간 사고대응을 위해선 빠른 계산과 더불어 피해영역 분포의 불확실성을 줄이기 위한 반복 계산이 요구된다. EPA ALOHA, 화학물질안전원 KORA 등이 있지만, 지속적인 모델과 코드의 보완이 가능하고, 중소기업용의 무료 S/W개발에 본 연구의 차별성이 있다. 계산시간이 많이 요구되는 full-scale CFD에 비해 상대적인 정확도의 손실은 감수하고, 특히 일반 사용자의 편리성을 도모하였다. 기상청 기상정보 연계를 비롯해, Python open-source 라이브러리들을 활용해, 기능 확장 및 지속적인 update가 가능하며, 사용자는 해당 플랜트의 지형도와 사용 물질의 입력만으로 쉽게 결과를 얻을 수 있다. Full-scale CFD 시뮬레이션과 대비해 정확도를 확인하였으며, 빠른 계산을 위해 GPU를 활용하는 open source software로 배포될 예정이다.

Keywords

GSGSBE_2018_v22n6_136_f0001.png 이미지

Fig. 1. An example of an LNG spill and vapor dispersion [2].

GSGSBE_2018_v22n6_136_f0002.png 이미지

Fig. 3. KORA framework of National Institute of Chemical Safety [5].

GSGSBE_2018_v22n6_136_f0003.png 이미지

Fig. 2. An ALOHA threat zone estimate displayed on a MARPLOT map [7].

GSGSBE_2018_v22n6_136_f0004.png 이미지

Fig. 4. Estimation of real-time gas dispersion, integrated with surrogate prediction models (adapted from [10])

GSGSBE_2018_v22n6_136_f0005.png 이미지

Fig. 5. Basic principle of cellular automata simulation (adapted from [13])

GSGSBE_2018_v22n6_136_f0006.png 이미지

Fig. 6. An agent-based, forest fire simulation on NetLogo [11]

GSGSBE_2018_v22n6_136_f0007.png 이미지

Fig. 7. Proposed dispersion simulation, based on cellular automata implemented in Python

GSGSBE_2018_v22n6_136_f0008.png 이미지

Fig. 8. Popular scientific Python libraries [1]

GSGSBE_2018_v22n6_136_f0009.png 이미지

Fig. 9. Flowchart of the implemented system

GSGSBE_2018_v22n6_136_f0010.png 이미지

Fig. 10. Comparison of a simulation result against Gumi chemical leak (2012)

GSGSBE_2018_v22n6_136_f0011.png 이미지

Fig. 11. Part of the open source OSSCA implmented in Python

References

  1. Bowne-Anderson, H., The Case for Python in Scientific Computing, https://www.datacamp.com/community/blog/python-scientific-computing-case (2017)
  2. Gexcon, Safety Studies: LNG Spill & Vapor Dispersion, https://www.gexconus.com/safety-studies/lng-spill-vapor-dispersion.html
  3. Guttag, J. V., Introduction to Computation and Programming Using Python, 2nd Ed., MIT Press (2016)
  4. Kim, J. H. et al., Comparison Study for Impact Range of Prediction Models Through Case Study about Gumi Hydrogen Fluoride Accident. Korean Chem. Eng. Res., 55(1), 48-53 (2017) https://doi.org/10.9713/KCER.2017.55.1.48
  5. National Institute of Chemical Safety, Korea Off-site Risk Assessment Supporting Tool (KORA) User's Guide, Ver. 3 (2018)
  6. Newman, D., A Program to Model Gas Diffusion using Cellular Automata, B.S. Thesis, University of Leeds (2006)
  7. NOAA, ALOHA, https://response.restoration. noaa.gov/oil-and-chemical-spills/chemical-spills/response-tools/aloha.html
  8. Python Software Foundation, https://www.python.org/
  9. Versteeg, H. and Malalasekera, W., An Introduction to Computational Fluid Dynamics: The finite volume method, 2nd Ed., Pearson (2007)
  10. Wang, B. et al., The real-time estimation of hazardous gas dispersion by the integration of gas detectors, neural network and gas dispersion models. Journal of Hazardous Materials, 300, 433-442 (2015) https://doi.org/10.1016/j.jhazmat.2015.07.028
  11. Wilensky, U. and Rand, W., An Introduction to Agent-Based Modeling, MIT Press (2015)
  12. Wolf-Gladrow, D. A., Lattice-Gas Cellular Automata and Lattice Boltzmann Models, Springer (2000)
  13. Zheng, Z. et al., Forest fire spread simulating model using cellular automaton with extreme learning machine. Ecological Modelling, 348, 33-43 (2017) https://doi.org/10.1016/j.ecolmodel.2016.12.022