Modelica is a freely available, object-oriented language for modeling of large, complex, and heterogeneous systems. It is suited for multi-domain modeling, for example, mechatronic models in robotics, automotive and aerospace applications involving mechanical, electrical, hydraulic control and state machine subsystems, process oriented applications and generation and distribution of electric power. Models in Modelica are mathematically described by differential, algebraic and discrete equations. No particular variable needs to be solved for manually. A Modelica tool will have enough information to decide that automatically. Modelica is designed such that available, specialized algorithms can be utilized to enable efficient handling of large models having more than one hundred thousand equations. Modelica is suited and used for hardware-in-the-loop simulations and for embedded control systems.

References in zbMATH (referenced in 59 articles )

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  1. M. N. Gevorkyan, A. V. Demidova, A. V. Korolkova, D. S. Kulyabov, L. A. Sevastianov: The Stochastic Processes Generation in OpenModelica (2017) arXiv
  2. Minopoli, Stefano; Frehse, Goran: From simulation models to hybrid automata using urgency and relaxation (2016)
  3. Scholz, Lena; Steinbrecher, Andreas: Regularization of DAEs based on the signature method (2016)
  4. Elsheikh, Atiyah: An equation-based algorithmic differentiation technique for differential algebraic equations (2015)
  5. Hannemann-Tamás, Ralf; Muñoz, Diego A.; Marquardt, Wolfgang: Adjoint sensitivity analysis for nonsmooth differential-algebraic equation systems (2015)
  6. Zhu, Longfei; Xu, Qiwen; He, Jifeng; Zhu, Huibiao: A formal model for a hybrid programming language (2015)
  7. Acuña, Oscar; Martin-Villalba, Carla; Urquia, Alfonso: Virtual lab in Modelica of a cement clinker cooler for operator training (2014)
  8. Åström, Karl J.; Kumar, P.R.: Control: a perspective (2014)
  9. Dasgupta, Gautam: Locking-free compressible quadrilateral finite elements: Poisson’s ratio-dependent vector interpolants (2014)
  10. Mehlhase, Alexandra: A Python framework to create and simulate models with variable structure in common simulation environments (2014)
  11. Minopoli, Stefano; Frehse, Goran: Non-convex invariants and urgency conditions on linear hybrid automata (2014)
  12. Kirches, Christian; Leyffer, Sven: TACO: a toolkit for AMPL control optimization (2013)
  13. Zhou, Yuchen; Baras, John S.: CPS modeling integration hub and design space exploration with application to microrobotics (2013)
  14. Bonvini, Marco; Leva, Alberto: Object-oriented sub-zonal modelling for efficient energy-related building simulation (2011)
  15. González, Francisco; Naya, Miguel Ángel; Luaces, Alberto; González, Manuel: On the effect of multirate co-simulation techniques in the efficiency and accuracy of multibody system dynamics (2011)
  16. Navarro-López, Eva M.; Carter, Rebekah: Hybrid automata: an insight into the discrete abstraction of discontinuous systems (2011)
  17. Åkesson, Johan; Ekman, Torbjörn; Hedin, Görel: Implementation of a modelica compiler using JastAdd attribute grammars (2010)
  18. Bauer, Kerstin; Schneider, Klaus: From synchronous programs to symbolic representations of hybrid systems (2010)
  19. Bouskela, D.; Chip, V.; El Hefni, B.; Favennec, J.M.; Midou, M.; Ninet, J.: New method to assess tube support plate clogging phenomena in steam generators of nuclear power plants (2010)
  20. Foeken, M.J.; Voskuijl, M.: Knowledge-based simulation model generation for control law design applied to a quadrotor UAV (2010)

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