Modelica

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 66 articles )

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  1. Magnusson, Fredrik; Åkesson, Johan: Symbolic elimination in dynamic optimization based on block-triangular ordering (2018)
  2. Westman, Jonas; Nyberg, Mattias: Conditions of contracts for separating responsibilities in heterogeneous systems (2018)
  3. Chen, Mingshuai; Ravn, Anders P.; Wang, Shuling; Yang, Mengfei; Zhan, Naijun: A two-way path between formal and informal design of embedded systems (2017)
  4. Kohlhase, Michael; Koprucki, Thomas; Müller, Dennis; Tabelow, Karsten: Mathematical models as research data via flexiformal theory graphs (2017)
  5. M. N. Gevorkyan, A. V. Demidova, A. V. Korolkova, D. S. Kulyabov, L. A. Sevastianov: The Stochastic Processes Generation in OpenModelica (2017) arXiv
  6. Peleš, Slaven; Klus, Stefan: Sparse automatic differentiation for complex networks of differential-algebraic equations using abstract elementary algebra (2017)
  7. Minopoli, Stefano; Frehse, Goran: From simulation models to hybrid automata using urgency and relaxation (2016)
  8. Scholz, Lena; Steinbrecher, Andreas: Regularization of DAEs based on the signature method (2016)
  9. Andersson, C., Führer, C., Åkesson, J.: Assimulo: A unified framework for ODE solvers (2015)
  10. Elsheikh, Atiyah: An equation-based algorithmic differentiation technique for differential algebraic equations (2015)
  11. Hannemann-Tamás, Ralf; Muñoz, Diego A.; Marquardt, Wolfgang: Adjoint sensitivity analysis for nonsmooth differential-algebraic equation systems (2015)
  12. Zhu, Longfei; Xu, Qiwen; He, Jifeng; Zhu, Huibiao: A formal model for a hybrid programming language (2015)
  13. Acuña, Oscar; Martin-Villalba, Carla; Urquia, Alfonso: Virtual lab in Modelica of a cement clinker cooler for operator training (2014)
  14. Althoff, Matthias; Krogh, Bruce H.: Reachability analysis of nonlinear differential-algebraic systems (2014)
  15. Åström, Karl J.; Kumar, P. R.: Control: a perspective (2014)
  16. Dasgupta, Gautam: Locking-free compressible quadrilateral finite elements: Poisson’s ratio-dependent vector interpolants (2014)
  17. Mehlhase, Alexandra: A Python framework to create and simulate models with variable structure in common simulation environments (2014)
  18. Minopoli, Stefano; Frehse, Goran: Non-convex invariants and urgency conditions on linear hybrid automata (2014)
  19. Kirches, Christian; Leyffer, Sven: TACO: a toolkit for AMPL control optimization (2013)
  20. Zhou, Yuchen; Baras, John S.: CPS modeling integration hub and design space exploration with application to microrobotics (2013)

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