Dymola

Dymola, Dynamic Modeling Laboratory, is a complete tool for modeling and simulation of integrated and complex systems for use within automotive, aerospace, robotics, process and other applications. The Dymola environment uses the open Modelica® modeling language which means that users are free to create their own model libraries or modify the ready made model libraries to better match users unique modeling and simulation needs. The flexibility of Dymola makes it a versatile tool which is perfect for modeling and simulation of new alternative designs and technologies.


References in zbMATH (referenced in 58 articles )

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  1. Marzorati, Denise; Fernández, Joaquin; Kofman, Ernesto: Efficient connection processing in equation-based object-oriented models (2022)
  2. Dedè, Luca; Regazzoni, Francesco; Vergara, Christian; Zunino, Paolo; Guglielmo, Marco; Scrofani, Roberto; Fusini, Laura; Cogliati, Chiara; Pontone, Gianluca; Quarteroni, Alfio: Modeling the cardiac response to hemodynamic changes associated with COVID-19: a computational study (2021)
  3. Mancini, Toni; Mari, Federico; Massini, Annalisa; Melatti, Igor; Tronci, Enrico: On checking equivalence of simulation scripts (2021)
  4. Kučera, Erik; Haffner, Oto; Drahoš, Peter; Cigánek, Ján; Štefanovič, Juraj; Kozák, Štefan: New software tool for modelling and control of discrete-event and hybrid systems using Petri nets (2020)
  5. Zimmer, Dirk: Robust object-oriented formulation of directed thermofluid stream networks (2020)
  6. Baharev, Ali; Neumaier, Arnold; Schichl, Hermann: A manifold-based approach to sparse global constraint satisfaction problems (2019)
  7. Pollok, Alexander; Klöckner, Andreas; Zimmer, Dirk: Psychological aspects of equation-based modelling (2019)
  8. Estévez Schwarz, Diana; Lamour, René: A new approach for computing consistent initial values and Taylor coefficients for DAEs using projector-based constrained optimization (2018)
  9. Kunkel, Peter; Mehrmann, Volker: Regular solutions of DAE hybrid systems and regularization techniques (2018)
  10. Magnusson, Fredrik; Åkesson, Johan: Symbolic elimination in dynamic optimization based on block-triangular ordering (2018)
  11. Mahdi Ghazaei Ardakani, M.; Magnusson, Fredrik: Ball-and-finger system: modeling and optimal trajectories (2018)
  12. Baharev, Ali; Domes, Ferenc; Neumaier, Arnold: A robust approach for finding all well-separated solutions of sparse systems of nonlinear equations (2017)
  13. Laughman, Christopher R.; Qiao, Hongtao: On the influence of state selection on mass conservation in dynamic vapour compression cycle models (2017)
  14. McKenzie, Ross; Pryce, John: Structural analysis based dummy derivative selection for differential algebraic equations (2017)
  15. M. N. Gevorkyan, A. V. Demidova, A. V. Korolkova, D. S. Kulyabov, L. A. Sevastianov: The Stochastic Processes Generation in OpenModelica (2017) arXiv
  16. Reiner, Matthias J.; Zimmer, Dirk: Object-oriented modelling of wind turbines and its application for control design based on nonlinear dynamic inversion (2017)
  17. Ha, Phi; Mehrmann, Volker: Analysis and numerical solution of linear delay differential-algebraic equations (2016)
  18. Sanz, Victorino; Urquia, Alfonso; Leva, Alberto: \textitCellularAutomataLib2: improving the support for cellular automata modelling in Modelica (2016)
  19. Andersson, C., Führer, C., Åkesson, J.: Assimulo: A unified framework for ODE solvers (2015) not zbMATH
  20. Elsheikh, Atiyah: An equation-based algorithmic differentiation technique for differential algebraic equations (2015)

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