Advertisement

Process-oriented approach into Rao X simulation modeling system

  • Olga V. ZudinaEmail author
Conference paper
Part of the Mechanisms and Machine Science book series (Mechan. Machine Science, volume 73)

Abstract

The aim of the project is to implement a modern and convenient way of visual model programming with the implementation of process-oriented approach of discrete simulation modeling and integration of developed subsystem into Rao X system. The result of the development is an integrated subsystem of process approach which represents visual programming software for processoriented approach simulation models with a possibility of modeling output. During the process of development there was conducted a research, which became a basis for accepting the decision about the implementation of present set of structures. In addition a review of graphic libraries was conducted. The system is efficient due to the integration into Rao X system with a possibility of interaction with other approaches (event and activity scanning approaches) to discrete simulation modeling. This system can be used for educational process and as a base for developments.

Keywords

simulation modeling process-oriented approach model transaction graphics library 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Emel’yanov V.V., Yasinovskiy S.I.: Imitatsionnoe modelirovanie sistem [Systems simulation modeling], Moscow, BMSTU, 2009. 583 p.Google Scholar
  2. 2.
    Berchun Yu.V., Burkov P.V., Zudina O.V., Pospolita N.V., Semenov I.I.: ICOM-approach implication for construction of systems simulation models, In: Engineering herald, No. 8 (2016) p. 1001-1007.Google Scholar
  3. 3.
    Romanov A.N., Tarakanov P.V., Shashurin G.V., Berchun Yu.V., Rezchikova L.A., Sokol’nikov P.S.: Fatigue crack propagation modeling of hydrogenating high-strength steels, In: Engineering and Automation Problems, No. 4 (2014) p. 87-93.Google Scholar
  4. 4.
    Pritsker A.: Introduction to simulation and SLAM II: Systems Pub Corp, 1986. 305 p.Google Scholar
  5. 5.
    Karpenko A.P., Moor D.A., Mukhlisullina D.T.: Multicriteria optimization based on neural network, fuzzy and neuro-fuzzy approximation of decision maker’s utility function, In: Optical memory & neural networks (information optics), No. 1 (2012) p. 1-10.Google Scholar
  6. 6.
    Graphical Editing Framework (2017). Available at: https://eclipse.org/gef/ (accessed 20 March 2018).

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.CAD/CAE Department, Faculty of Robotics and Integrated AutomationMoskovskij Gosudarstvennyj Tehniceskij Universitet im. N.E. BaumanaMoscowRussia

Personalised recommendations