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 40 articles )

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  1. Magnusson, Fredrik; Åkesson, Johan: Symbolic elimination in dynamic optimization based on block-triangular ordering (2018)
  2. Baharev, Ali; Domes, Ferenc; Neumaier, Arnold: A robust approach for finding all well-separated solutions of sparse systems of nonlinear equations (2017)
  3. McKenzie, Ross; Pryce, John: Structural analysis based dummy derivative selection for differential algebraic equations (2017)
  4. M. N. Gevorkyan, A. V. Demidova, A. V. Korolkova, D. S. Kulyabov, L. A. Sevastianov: The Stochastic Processes Generation in OpenModelica (2017) arXiv
  5. Ha, Phi; Mehrmann, Volker: Analysis and numerical solution of linear delay differential-algebraic equations (2016)
  6. Sanz, Victorino; Urquia, Alfonso; Leva, Alberto: \itCellularAutomataLib2: improving the support for cellular automata modelling in Modelica (2016)
  7. Andersson, C., Führer, C., Åkesson, J.: Assimulo: A unified framework for ODE solvers (2015)
  8. Elsheikh, Atiyah: An equation-based algorithmic differentiation technique for differential algebraic equations (2015)
  9. Pryce, John D.; Nedialkov, Nedialko S.; Tan, Guangning: DAESA -- a Matlab tool for structural analysis of differential-algebraic equations: theory (2015)
  10. Acuña, Oscar; Martin-Villalba, Carla; Urquia, Alfonso: Virtual lab in Modelica of a cement clinker cooler for operator training (2014)
  11. Mehlhase, Alexandra: A Python framework to create and simulate models with variable structure in common simulation environments (2014)
  12. Kirches, Christian; Leyffer, Sven: TACO: a toolkit for AMPL control optimization (2013)
  13. De P. Soares, R.; Secchi, Argimiro R.: Structural analysis for static and dynamic models (2012) ioport
  14. Ha, Phi; Mehrmann, Volker: Analysis and reformulation of linear delay differential-algebraic equations (2012)
  15. Ramírez-Arias, A.; Rodríguez, F.; Guzmán, J. L.; Berenguel, M.: Multiobjective hierarchical control architecture for greenhouse crop growth (2012)
  16. Dangl, F.; Neudorfer, Harald: Einfluss der umrichterbedingten zusatzverluste auf das thermische verhalten von asynchronmaschinen für traktionsantriebe (2011) ioport
  17. Liscouët, Jonathan; Budinger, Marc; Maré, Jean-Charles; Orieux, Stéphane: Modelling approach for the simulation-based preliminary design of power transmissions (2011)
  18. Sielemann, M.; Schmitz, G.: A quantitative metric for robustness of nonlinear algebraic equation solvers (2011)
  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. Chen, Qiong-Zhong; Meng, Guang; Zeng, Shui-Sheng: On the algorithms of adaptive neural network-based speed control of switched reluctance machines (2010)

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