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Risk-based determination of design pressure of LNG fuel storage tanks based on dynamic process simulation combined with Monte Carlo method

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  • Noh, Yeelyong
  • Chang, Kwangpil
  • Seo, Yutaek
  • Chang, Daejun

Abstract

This study proposes a new methodology that combines dynamic process simulation (DPS) and Monte Carlo simulation (MCS) to determine the design pressure of fuel storage tanks on LNG-fueled ships. Because the pressure of such tanks varies with time, DPS is employed to predict the pressure profile. Though equipment failure and subsequent repair affect transient pressure development, it is difficult to implement these features directly in the process simulation due to the randomness of the failure. To predict the pressure behavior realistically, MCS is combined with DPS. In MCS, discrete events are generated to create a lifetime scenario for a system. The combination of MCS with long-term DPS reveals the frequency of the exceedance pressure. The exceedance curve of the pressure provides risk-based information for determining the design pressure based on risk acceptance criteria, which may vary with different points of view.

Suggested Citation

  • Noh, Yeelyong & Chang, Kwangpil & Seo, Yutaek & Chang, Daejun, 2014. "Risk-based determination of design pressure of LNG fuel storage tanks based on dynamic process simulation combined with Monte Carlo method," Reliability Engineering and System Safety, Elsevier, vol. 129(C), pages 76-82.
    • Handle: RePEc:eee:reensy:v:129:y:2014:i:c:p:76-82
    DOI: 10.1016/j.ress.2014.04.018
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    References listed on IDEAS

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    1. Kwang Pil Chang & Daejun Chang & Enrico Zio, 2010. "Application of Monte Carlo Simulation for the Estimation of Production Availability in Offshore Installations," Springer Series in Reliability Engineering, in: Javier Faulin & Angel A. Juan & Sebastián Martorell & José-Emmanuel Ramírez-Márquez (ed.), Simulation Methods for Reliability and Availability of Complex Systems, chapter 0, pages 233-252, Springer.
    2. Zio, E. & Marella, M. & Podofillini, L., 2007. "A Monte Carlo simulation approach to the availability assessment of multi-state systems with operational dependencies," Reliability Engineering and System Safety, Elsevier, vol. 92(7), pages 871-882.
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    Cited by:

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    2. Hou, Tianfeng & Nuyens, Dirk & Roels, Staf & Janssen, Hans, 2019. "Quasi-Monte Carlo based uncertainty analysis: Sampling efficiency and error estimation in engineering applications," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    3. Agnieszka A. Tubis & Emilia T. Skupień & Stefan Jankowski & Jacek Ryczyński, 2022. "Risk Assessment of Human Factors of Logistic Handling of Deliveries at an LNG Terminal," Energies, MDPI, vol. 15(8), pages 1-24, April.
    4. Medeiros, C.P. & Alencar, M.H. & de Almeida, A.T., 2017. "Multidimensional risk evaluation of natural gas pipelines based on a multicriteria decision model using visualization tools and statistical tests for global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 268-276.
    5. Wang, J.P. & Chang, Su-Chin, 2015. "Evidence in support of seismic hazard following Poisson distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 207-216.
    6. Seo, Suwon & Han, Sangheon & Lee, Sangick & Chang, Daejun, 2016. "A pump-free boosting system and its application to liquefied natural gas supply for large ships," Energy, Elsevier, vol. 105(C), pages 70-79.
    7. Wu, Shengnan & Zhang, Laibin & Barros, Anne & Zheng, Wenpei & Liu, Yiliu, 2018. "Performance analysis for subsea blind shear ram preventers subject to testing strategies," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 281-298.
    8. Penttinen, Jussi-Pekka & Niemi, Arto & Gutleber, Johannes & Koskinen, Kari T. & Coatanéa, Eric & Laitinen, Jouko, 2019. "An open modelling approach for availability and reliability of systems," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 387-399.

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