Parallel Processing for Nuclear Safety Simulation

  • A. Y. Allidina
  • M. G. Singh
  • B. Daniels
Part of the Advances in Nuclear Science and Technology book series (ANST, volume 17)


With the advances made in computer technology, there has been much interest in the use of this technology for System Design, Reliability and Safety aspects. One general application area is in the nuclear industry. A particular problem in the nuclear industry (and other industries) is to simulate system models sufficiently fast in order to, for example, provide real-time predictive information to plant operators. However, in order that the models might represent the corresponding plants in a realistic way, these models are invariably complex. This makes simulation rather difficult and very often much slower than real-time if standard solution techniques are used together with modest present-day computer technology. Given this problem, it is clearly desirable to investigate ways of improving the simulation speed without dramatic increases in cost. In this chapter, we are concerned with the above objective. The eventual aim of the research work reported here is to investigate the application of the developed techniques to the Nuclear code RELAPV [Allidina (Editor) 1984].


Local Problem Parallel Processing Time Level Discretisation Scheme Nuclear Industry 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. [1]
    Allidina A.Y. (ed,): ‘Development of hierarchical techniques for the simulation of large scale systems with particular application to the nuclear industry’, EEC Project Phase 1 Report, May, 1984.Google Scholar
  2. [2]
    Allidina A.Y., Lei S. and Wang L.: “Hierarchical simulation techniques for ODE Systems”, Distributed Simulation 85, San Diego, California, Jan. 1985.Google Scholar
  3. [3]
    Arnold C.P., Michael I.P. and Michael B.D.: ‘An efficient parallel algorithm for the solution of large sparse linear matrix equations’. IEEE Trans, on Computers, Vol. C. 32, No. 3 (March 1983).Google Scholar
  4. [4]
    Barlow R.H. and Evans D.J.: ‘Parallel algorithms for the iterative solution of Linear Systems’, The Computer Journal, Vol. 25, No. 1982.Google Scholar
  5. [5]
    Brasch F.M. Jr., Van Ness J.E. and Kang S.C.: ‘Design of Multiprocessors structures for simulation of power- system dynamics’, Report, Electric Power Research Institute, Palo Alto, California 94304, U.S.A.Google Scholar
  6. [6]
    Burks A.W.: ‘Programming and structure changes in parallel computers’, Proceedings CONPAR 81, Springer- Verlag, Lecture Notes in Computer Science, Vol. Ill, edited by W. Handler, 1981.Google Scholar
  7. [7]
    Evans D.J. and Haghighi R.S.: ‘Parallel iterative methods for solving linear equations’. Intern. J. Computer Math., Vol. II, pp 247-285, 1982.MathSciNetCrossRefGoogle Scholar
  8. [8]
    Findeisen W., Bailey F.N., Bryds M., Malinowski K., Tatjewski P. and Wozniak A.: ‘Control and coordination in hierarchical systems’. International Series on Applied Systems Analysis, Vol. 9, John Wiley & Sons, 1980.Google Scholar
  9. [9]
    Franklin, M.A.: ‘Parallel solution of ordinary differential equations’, IEEE Trans, on Computers, Vol. C-27, No. 5, May 1978.Google Scholar
  10. [10]
    Halada L.: ‘A parallel algorithm for solving band systems and matrix inversion’, Proc. CONPAR 81, Springer- Verlag, Lecture Notes in Computer Science, Vol. Ill, edited by W. Handler, 1981.Google Scholar
  11. [12]
    Himmelblau D.M. (editor): ‘Decomposition of large scale systems’. Collection of articles on decomposition and coordination techniques). American Elsevier, New York 1973.Google Scholar
  12. [12]
    Katz. I.N., Franklin M.A. and Sen A.: ‘Optimally stable parallel predictors for Adams-Moulton correctors’, Comp. and Maths with Appls., Vol 3, pp. 217 - 233, 1977.MathSciNetMATHCrossRefGoogle Scholar
  13. Lasdon L.S.: ‘Optimisation Theory for Large Systems’, MacMillan, London, 1970.Google Scholar
  14. [14]
    Malinowski K., Allidina A.Y., Singh M.G. and Crorkin W.: “Decomposition-coordination techniques for parallel simulation - Part 1”, Control Systems Centre Report No. 599, UMIST, Manchester, March 1984.Google Scholar
  15. [15]
    Miranker W.L. and Liniger W.: ‘Parallel methods for the numerical integration of ordinary differential equations’, Math. Comput. Vol. 21, pp 303–320, 1967.MathSciNetGoogle Scholar
  16. [16]
    RELAPV Code Manuals Vols. 1,2 and 3, U.S. Department of Energy, 1982.Google Scholar
  17. [17]
    Schendel U.: ‘On basic concepts in parallel numerical mathematics’, Proc. CONPAR 81, Springer-Verlag, Lecture Notes in Computer Science, Vol. Ill, edited by W. Handler, 1981.Google Scholar
  18. [18]
    Singh M.G. and Titli A.: ‘Systems: Decomposition, Optimisation and Control’, Pergamon Press, Oxford 1978.MATHGoogle Scholar
  19. [19]
    Singh M.G., Allidina A.Y. and Malinowski K.: ‘Hierarchical simulation techniques’, MECO 83, Athens, July, 1983.Google Scholar
  20. [20]
    Travassos R. and Kaufman H.: ‘Parallel algorithms for solving non-linear two-point boundary-value problems which arise in optimal control’, Journal of Optimisation Theory and Applications, Vol. 30, No. 1, January, 1980.Google Scholar
  21. [21]
    Wismer D.A.: ‘Distributed multilevel systems’, in Optimisation Methods for Large-Scale Systems, edited by D.A. Wismer, McGraw-Hill, New York, 1971.Google Scholar
  22. [22]
    Worland P.B.: ‘Parallel methods for the numerical solution of ordinary differential equations’, IEEE Trans, on Computers, October, 1976.Google Scholar

Copyright information

© Plenum Press, New York 1986

Authors and Affiliations

  • A. Y. Allidina
    • 1
  • M. G. Singh
    • 1
  • B. Daniels
    • 2
  1. 1.Control Systems CentreUniversity of Manchester Institute of Science and TechnologyUK
  2. 2.Systems Reliability ServiceUnited Kingdom Atomic Energy AuthorityUK

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