Skip to main content

Study on Heterogeneous Model Framework Library for Complex System Modeling

  • Conference paper
  • First Online:
  • 896 Accesses

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 951))

Abstract

The simulation of heterogeneous model can meet the simulation of complex system. However, in the process of heterogeneous model integration, there are still some research difficulties and hot spots, such as improving model reuse ability, shortening modeling time cycle, and reducing modeling development cost. In this paper, a model framework library is established to provide services for modeling and simulation in various fields and meet the requirements of heterogeneous integration, model reuse, and model services. Firstly, the concept and structure of model framework library are introduced. Next, the process of extracting characteristics from heterogeneous models to form templates is introduced in detail. Finally, the templates is serialized as a model framework and stored in the model framework library.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Rodič, B.: Industry 4.0 and the new simulation modelling paradigm. Organizacija 50(3), 193 (2017)

    Article  Google Scholar 

  2. Shlyannikov, V., Yarullin, R., Yakovlev, M., Giannella, V., Citarella, R.: Mixed-mode crack growth simulation in aviation engine compressor disk. Eng. Fract. Mech. 246, 107617 (2021)

    Article  Google Scholar 

  3. Morrison, T.M., Pathmanathan, P., Adwan, M., Margerrison, E.: Advancing regulatory science with computational modeling for medical devices at the FDA’s office of science and engineering laboratories. Front. Med. 5, 241 (2018)

    Article  Google Scholar 

  4. Harish, V., Kumar, A.: A review on modeling and simulation of building energy systems. Renew. Sustain. Energy Rev. 56, 1272–1292 (2016)

    Article  Google Scholar 

  5. Zhang, L., Zhou, L., Ren, L., Laili, Y.: Modeling and simulation in intelligent manufacturing. Comput. Ind. 112, 103123 (2019)

    Article  Google Scholar 

  6. Aebersold, M.: The history of simulation and its impact on the future. AACN Adv. Crit. Care 27(1), 56–61 (2016)

    Article  Google Scholar 

  7. Wenhui, F., Jiahui, W.: Development and future trend of computer simulation and quantum computer simulation. J. Syst. Simul. 29(6), 1161 (2017)

    Google Scholar 

  8. Zhao, W., Peng, Y., Xie, F., Dai, Z.: Modeling and simulation of cloud computing: a review. In 2012 IEEE Asia Pacific Cloud Computing Congress (APCloudCC), pp. 20–24. IEEE (2012)

    Google Scholar 

  9. Li, F., Shi, Y.: Rapid performance gain through active model reuse. Nanjing University (2019)

    Google Scholar 

  10. Liu, Y., Zhang, L., Zhang, W., Hu, X.: An overview of simulation-oriented model reuse. In: Zhang, L., Song, X., Wu, Y. (eds.) AsiaSim/SCS AutumnSim - 2016. CCIS, vol. 646, pp. 48–56. Springer, Singapore (2016). https://doi.org/10.1007/978-981-10-2672-0_6

    Chapter  Google Scholar 

  11. Allegaert, E., Lemmens, Y., La Rocca, G.: Architecture-based design for multi-body simulation of complex systems. In 2017 IEEE International Systems Engineering Symposium (ISSE), pp. 1–7. IEEE (2017)

    Google Scholar 

  12. Petty, M.D., Weisel, E.W.: Model composition and reuse. In: Model Engineering for Simulation, pp. 57–85. Academic Press (2019)

    Google Scholar 

  13. Zhao, P., Cai, L.-W., Zhou, Z.-H.: Handling concept drift via model reuse. Mach. Learn. 109(3), 533–568 (2019). https://doi.org/10.1007/s10994-019-05835-w

    Article  MathSciNet  MATH  Google Scholar 

  14. Fujimoto, R., Bock, C., Chen, W., Page, E., Panchal, J.H. (eds.): Research Challenges in Modeling and Simulation for Engineering Complex Systems. SFMA, Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58544-4

    Book  Google Scholar 

  15. Balci, O., Arthur, J.D., Nance, R.E.: Accomplishing reuse with a simulation conceptual model. In: 2008 Winter Simulation Conference, pp. 959–965. IEEE (2008)

    Google Scholar 

  16. Robinson, S., Nance, R.E., Paul, R.J., Pidd, M., Taylor, S.J.: Simulation model reuse: definitions, benefits and obstacles. Simul. Model. Pract. Theor. 12(7–8), 479–494 (2004)

    Article  Google Scholar 

  17. Zhang, L., et al.: X language: an integrated intelligent modeling and simulation language for complex products. In: 2021 Annual Modeling and Simulation Conference (ANNSIM), pp. 1–11. IEEE (2021)

    Google Scholar 

  18. Cusumano, M.: Cloud computing and SaaS as new computing platforms. Commun. ACM 53(4), 27–29 (2010)

    Article  Google Scholar 

  19. Zhu, W., Guo, L.: Modeling and Simulation IN IMS Fusion of SE and Simulation. Tsinghua University Press, Beijing (2021)

    Google Scholar 

  20. Andriamamonjy, A., Saelens, D., Klein, R.: An automated IFC-based workflow for building energy performance simulation with Modelica. Autom. Constr. 91, 166–181 (2018)

    Article  Google Scholar 

  21. Zeigler, B.P.: DEVS and MBSE: a review. Int. J. Model. Simul. Sci. Comput. 13(02), 2230001 (2022)

    Article  Google Scholar 

  22. Antonova, V.M., Grechishkina, N.A., Kuznetsov, N.A.: Analysis of the modeling results for passenger traffic at an underground station using AnyLogic. J. Commun. Technol. Electron. 65(6), 712–715 (2020)

    Article  Google Scholar 

  23. Shen, N.: A feature quantification method based on binary tree oriented for constructional frameset of heterogeneous models. In: 6th International Conference on Mechanical, Control and Computer Engineering (2021)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chun Zhao .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dong, Q., Zhao, C., Tian, M. (2022). Study on Heterogeneous Model Framework Library for Complex System Modeling. In: Jia, Y., Zhang, W., Fu, Y., Zhao, S. (eds) Proceedings of 2022 Chinese Intelligent Systems Conference. CISC 2022. Lecture Notes in Electrical Engineering, vol 951. Springer, Singapore. https://doi.org/10.1007/978-981-19-6226-4_85

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-6226-4_85

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-6225-7

  • Online ISBN: 978-981-19-6226-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics