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Monte Carlo simulation approach to determination of oil spill domains at port and sea water areas

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Monte Carlo simulation method of oil spill domains determination based on the probabilistic approach to the solution of this problem is proposed. A semi-Markov model of the process of changing hydro-meteorological conditions is constructed and its parameters are defined. The general stochastic model of oil spill domain movement for various hydro-meteorological conditions is described. Monte Carlo simulation procedure is created and applied to generating the process of changing hydro-meteorological conditions and the prediction of the oil spill domain movement impacted by these changes conditions.
Twórcy
  • Gdynia Maritime University, Gdynia, Poland
  • Gdynia Maritime University, Gdynia, Poland
Bibliografia
  • 1. Al-Rabeh A. H., Cekirge H. M. & Gunay N. A. 1989. Stochastic simulation model of oil spill fate and transport, Applied Mathematical Modelling, p. 322-329.
  • 2. Bogalecka, M. & Kołowrocki, K. 2018. Minimization of critical infrastructure accident losses of chemical releases impacted by climate-weather change, Proc. International Conference on Industrial Engineering and Engineering Management - IEEM, Bangkok, Thailand.
  • 3. Chen, H, Li, D. & Li, X. 2007. Mathematical modeling of oil spill on the sea and application of the modeling in Daya Bay, Journal of Hydrodynamics, Ser. B: Elsevier, (19)3, 282291.
  • 4. Dąbrowska, E. 2019. Monte Carlo simulation approach to reliability analysis of complex systems, PhD Thesis, System Research Institute, Polish Academy of Science, Warsaw, Poland (under examination).
  • 5. Dąbrowska, E. & Kołowrocki, K. 2019A. Modelling, identification and prediction of oil spill domains at port and sea water areas, Journal of Polish Safety and Reliability Association, Summer Safety and Reliability Seminars, 10(1), 43-58.
  • 6. Dąbrowska, E. & Kołowrocki, K. 2019B. Probabilistic Approach to Determination of Oil Spill Domains at Port and Sea Water Areas, Proc. of TransNav Conference 2019
  • 7. Dąbrowska, E. & Soszyńska-Budny, J. 2018. Monte Carlo simulation forecasting of maritime ferry safety and resilience, Proc. International Conference on Industrial Engineering and Engineering Management - IEEM, Bangkok, Thailand
  • 8. Fay J. A. 1971. Physical Processes in the Spread of Oil on a Water Surface. Proceedings of Joint Conference on Prevention and Control of Oil Spills, sponsored by American Petroleum Industry, Environmental Protection Agency, and United States Coast Guard
  • 9. Fingas, M. 2016. Oil Spill Science and Technology, 2nd Edition, Elsevier
  • 10. Grabski, F., Jaźwiński, J. .2009. Funkcje o losowych argumentach w zagadnieniach niezawodności, bezpieczeństwa i logistyki. Wydawnictwa Komunikacji i Łączności: Warszawa.
  • 11. Guze, S., Kolowrocki, K. & Mazurek, J. 2017. Modelling spread limitations of oil spills at sea. Proc. The 17th Conference of the Applied Stochastic Models and Data Analysis – ASMDA, London, UK
  • 12. Guze S., Mazurek J. & Smolarek L. 2016. Use of random walk in two-dimensional lattice graphs to describe influence of wind and sea currents on oil slick movement. Journal of KONES Powertrain and Transport, Vol. 23, No. 2.
  • 13. Huang J. C. 1983. A review of the state-of-the-art of oil spill fate/behavior models. International Oil Spill Conference Proceedings: February 1983, Vol. 1983, No. 1, p. 313-322.
  • 14. Kim, T., Yang , C.-S., Ouchi, K. & Oh, Y. 2013. Application of the method of moment and Monte-Carlo simulation to extract oil spill areas from synthetic aperture radar images, OCEANS - San Diego, 1-4.
  • 15. Kołowrocki, K. 2014. Reliability of large and complex systems, Elsevier.
  • 16. Kołowrocki, K. & Soszyńska-Budny, J. 2011. Reliability and Safety of Complex Technical Systems and Processes: Modelling - Identification - Prediction - Optimization, London, Dordrecht, Heildeberg, New York: Springer.
  • 17. Kuligowska, E. 2017. Monte Carlo Simulation of Complex System Reliability Evaluation. Chapter in Monograph: Current research in mathematical and computer sciences. Red. A. Lecko, Wydawnictwo UWM: Olsztyn, 139-152.
  • 18. Kuligowska, E. 2018. Monte Carlo simulation of climateweather change process at maritime ferry operating area, Technical Sciences, University of Warmia and Mazury in Olsztyn, 1(21), 5-17.
  • 19. Law, A. M. & Kelton, W. D. 2000. Simulation Modeling and Analysis, 3rd ed., McGraw Hill.
  • 20. NOAA. Trajectory Analysis Handbook. NOAA Hazardous Material Response Division. Seattle: WA, http://www.response.restoration.noaa.gov/.
  • 21. Rao, M. S., & Naikan, V. N. A. 2016. Review of simulation approaches in reliability and availability modeling, International Journal of Performability Engineering, 12 (4), 369-388.
  • 22. Zio, E. & Marseguerra, M. 2002. Basics of the Monte Carlo Method with Application to System Reliability, LiLoLe.
  • 23. Spaulding M. L. 1988. A state-of-the-art review of oil spill trajectory and fate modeling. Oil and Chemical Pollution, Vol. 4, Issue 1, 39-55.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e5519f11-7a17-4744-b0cd-f8b1b4c0f7bf
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