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 53 articles )

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  1. 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)
  2. Mancini, Toni; Mari, Federico; Massini, Annalisa; Melatti, Igor; Tronci, Enrico: On checking equivalence of simulation scripts (2021)
  3. 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)
  4. Baharev, Ali; Neumaier, Arnold; Schichl, Hermann: A manifold-based approach to sparse global constraint satisfaction problems (2019)
  5. Estévez Schwarz, Diana; Lamour, René: A new approach for computing consistent initial values and Taylor coefficients for DAEs using projector-based constrained optimization (2018)
  6. Kunkel, Peter; Mehrmann, Volker: Regular solutions of DAE hybrid systems and regularization techniques (2018)
  7. Magnusson, Fredrik; Åkesson, Johan: Symbolic elimination in dynamic optimization based on block-triangular ordering (2018)
  8. Mahdi Ghazaei Ardakani, M.; Magnusson, Fredrik: Ball-and-finger system: modeling and optimal trajectories (2018)
  9. Baharev, Ali; Domes, Ferenc; Neumaier, Arnold: A robust approach for finding all well-separated solutions of sparse systems of nonlinear equations (2017)
  10. McKenzie, Ross; Pryce, John: Structural analysis based dummy derivative selection for differential algebraic equations (2017)
  11. M. N. Gevorkyan, A. V. Demidova, A. V. Korolkova, D. S. Kulyabov, L. A. Sevastianov: The Stochastic Processes Generation in OpenModelica (2017) arXiv
  12. Ha, Phi; Mehrmann, Volker: Analysis and numerical solution of linear delay differential-algebraic equations (2016)
  13. Sanz, Victorino; Urquia, Alfonso; Leva, Alberto: \textitCellularAutomataLib2: improving the support for cellular automata modelling in Modelica (2016)
  14. Andersson, C., Führer, C., Åkesson, J.: Assimulo: A unified framework for ODE solvers (2015) not zbMATH
  15. Elsheikh, Atiyah: An equation-based algorithmic differentiation technique for differential algebraic equations (2015)
  16. Pryce, John D.; Nedialkov, Nedialko S.; Tan, Guangning: DAESA -- a Matlab tool for structural analysis of differential-algebraic equations: theory (2015)
  17. Abdelati, Mohamed; Felgner, Felix; Frey, Georg: A component-oriented model for wastewater pumping plants (2014)
  18. Acuña, Oscar; Martin-Villalba, Carla; Urquia, Alfonso: Virtual lab in Modelica of a cement clinker cooler for operator training (2014)
  19. Ferretti, Gianni; Leva, Alberto; Scaglioni, Bruno: Object-oriented modelling of general flexible multibody systems (2014)
  20. Mehlhase, Alexandra: A Python framework to create and simulate models with variable structure in common simulation environments (2014)

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