Logic-Based Hierarchies for Modeling Behavior of Complex Dynamic Systems with Applications

  • Y.-S. Hu
  • M. Modarres
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 38)


Most complex systems are best represented in the form of a hierarchy. The Goal Tree Success Tree and Master Logic Diagram (GTST-MLD) are proven powerful hierarchic methods to represent complex snap-shot of plant knowledge. To represent dynamic behaviors of complex systems, fuzzy logic is applied to replace binary logic to extend the power of GTST-MLD. Such a fuzzy-logic-based hierarchy is called Dynamic Master Logic Diagram (DMLD). This chapter discusses comparison of the use of GTST-DMLD when applied as a modeling tool for systems whose relationships are modeled by either physical, binary logical or fuzzy logical relationships. This is shown by applying GTST-DMLD to the Direct Containment Heating (DCH) phenomenon at pressurized water reactors which is an important safety issue being addressed by the nuclear industry.


Fuzzy Logic Nuclear Power Plant Nuclear Plant Fuzzy Logic Model Nuclear Regulatory Commission 
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  1. Chang, Y.-J., Hu, Y.-S. and Chang, S.-K., Apply a Fuzzy Hierarchy Model for Semiconductor Fabrication Process Supervising, Semi Technical Symposium, Zelenograd, Moscow, Russia, May 17–19, 1999.Google Scholar
  2. Chen, L-W. and Modarres, M., A Hierarchical Decision Process for Fault Administration, Computers and Chemical Eng. J., 1992, Vol. 16, No. 5 425–448.Google Scholar
  3. Chung, D., Modarres, M. and Hunt, N., GOTRES: An Expert System for Fault Detection and Analysis. Reliability Eng. , and Syst. Safety J., 1989, Vol.24 276–289.Google Scholar
  4. Courtois, P., On Time and Space Decomposition of Complex Structures, Communications of the ACM, 1985, Vol. 28 (6) 596.Google Scholar
  5. Dezfuli, H. Modarres, M. and Meyer, J., Application of REVEAL_WTM To Risk-Based Configuration Control, Reliability Eng. and Safe. J., 1994, Vol. 44 243–263.Google Scholar
  6. Hadavi, H., Modarres, M. and Fakory, R., Assessment of Maintenance Effectiveness. In Proceedings of the 6 th SMC Simulation Multi-conference, New Orleans, April 1996. 18.Google Scholar
  7. Hu, Y-S. and Modarres, M., An Introduction to Dynamic MPLD Model, Proceeding of the PSAM-II Conference, San Diego, CA, 1994.Google Scholar
  8. Hu, Y S. and Modarres, M., Time-Dependent System Knowledge Representation Based on Dynamic MPLD, Control Engineering Practice J., 1996, Vol. 4, No 1 8948.Google Scholar
  9. Hu, Y.-S. and Modarres, M., Evaluating System Behavior through Dynamic Master Logic Diagram (DMLD) Modeling, Reliability Engineering and System Safety J., 1999, Vol. 64 pp. 241–269.Google Scholar
  10. Hu, Y.-S. and Modarres, M., Automate Hierarchy-Based Dynamic System Behavior Estimation: Method and Tool, PSA’99, Washington D.C., August, 1999.Google Scholar
  11. Hunt, R.N. and Modarres, M., Integrated Economic Risk Management in a Nuclear Power Plant, 1984 Annual Meeting of Soc. for Risk Analysis, 1984.Google Scholar
  12. Hunt, R. N. and Modarres, M., Use of Goal Tree Methodology to Evaluate Institutional Practices and Their Effect on Power Plant Hardware Performance. ANS/ENS Topical Meeting on Probabilistic Safety Methods and Applications, San Francisco, Feb. 1985. 17.Google Scholar
  13. Kim, I., Modarres, M. and Hunt, N., A Model-based Approach to On-line Process Disturbance Management: the Applications. Reliability Eng., and Syst. Safety J., 1990, 28, 185–239. 19.Google Scholar
  14. Modarres, M., Application of the Master Plant Logic Diagram in PSAs During Design, ANS Topical Meeting on Risk Management-Expanding Horizons, Boston, 1992.Google Scholar
  15. Modarres, M. and Cadman, T., A Method of Alarm System Analysis for Process Plants. Computers and Chemical Eng. J., 1986, 10, 557–565. 21.Google Scholar
  16. Modarres, M., Chen, L. and Danner, M., A Knowledge-based Approach to Root-cause Failure Analysis. Proc. of the Expert Systems Applications for the Electric Power Research Industry Conf, Orlando, FL, June 1989.Google Scholar
  17. Modarres, M., Roush, M. L. and Hunt, R. N., Application of Goal Trees in Reliability Allocations for Systems and Components of Nuclear Power Plants. 12th INTER-RAM Conf, Baltimore, 1985.Google Scholar
  18. Ni, T. and Modarres, M., Using Dynamic Master Logic Diagram for Component Partial Failure Analysis. Proc. of the ASME Pressure Vessels, and Piping, and ICPVT-8, Montreal, Canada, 1996.Google Scholar
  19. Nordvik, J-P., Mitchison, N. and Wilikens, M., The Role of the Goal Tree-Success Tree Model in the Real-time Supervision of Hazardous Plant. Proc. of the First International Workshop on Functional Modeling of Complex Technical Systems, Ispra, Italy, May 12–14, 1993, 127–141. 31.Google Scholar
  20. Nordvik, J-P., Atkinson, M. and Carpignano, A., Advances with STARS: Applications to Safety Problems. Proc. of the Fourth International Workshop on Functional Modeling of Complex Technical Systems, Athens, Greece, May 19–20, 1996, 91–100. 32.Google Scholar
  21. NUREG-1150 (1990) Severe Accident Risks: An Assessment for Five U.S. Nuclear Power Plants, U.S. Nuclear Regulatory Commission.Google Scholar
  22. O’Brien, J., Marksberry, D. and Modarres, M., Reactor Safety Assessment System (RSAS) Development and Lessons Learned. Second OECD Specialist Meeting on Operator Aids for Severe Accident Management, Lyon, France, Sept. 1997. 20.Google Scholar
  23. Piltch, M., Yan, H and Theofanous, T. (1994), “The Probability of Containment Failure by Direct Containment Heating in Zion”, NUREG/CR-6075, U.S. Nuclear Regulatory Commission.Google Scholar
  24. Piltch. M. (1995), The Probability of Containment Failure by Direct Containment Heating in Surry, NUREG/CR-6109, U.S. Regulatory Commission.Google Scholar
  25. Roush, M. L., Modarres, M. and Hunt, R. N., Application of Goal Trees to Evaluation of the Impact of Information upon Plant Availability. ANS/ENS Topical Meeting on Probabilistic Safety Methods and Applications, San Francisco, CA, Feb. 1985. 16.Google Scholar
  26. Sebo, D. Marksberry, D. And Modarres, M., RSAS: A Reactor Safety Assessment System, Proc. Of the 7th Power Plant Dynamics, Control and Testing Symposium, Knoxville, 1989.Google Scholar
  27. Simon, H., The sciences of the artificial, The MIT Press, 217–221, Cambridge, MA, 1982.Google Scholar
  28. Takagi, T. And Sugeno, M., Fuzzy Identification of Systems and Its Application to Modeling and Control, IEEE Trans. On Systems, Mans, and Cybern., Vol. SMC-15(1) 116–132.Google Scholar
  29. Zamanali, J. H. Modarres, M. and Wang, J., Application of Master Plant Logic Diagram (MPLD) PC-based Program in Probabilistic Risk Assessment. Proc. of the Int. Conf. on Probabilistic Safety Assessment and Management (PSAM),Beverly Hills, 1991.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Y.-S. Hu
    • 1
  • M. Modarres
    • 1
  1. 1.Center for Technology Risk StudiesUniversity of MarylandCollege ParkUSA

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