Generation of Sparse Jacobians for the Function Mock-Up Interface 2.0

Åkesson, Johan; Braun, Willi; Lindholm, Petter; Bachmann, Bernhard

doi:10.3384/ecp12076185

Cited by 9 publications

(7 citation statements)

References 8 publications

(13 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, most Modelica tools, especially OpenModelica, are capable of converting this formulation (by means of the so-called BLT transformation [2]) into a semi-explicit ODE form as formulated also in (2.3)…”

Section: Modelica Model Descriptionmentioning

confidence: 99%

“…In general, there is no closed expression for the functions f and g, but rather, iterative techniques, e.g., Newton's method, are employed to solve the so-called occurrent linear or nonlinear algebraic loops [2]. At this point it is possible to choose between two strategies for the discretization of (2.3).…”

Section: Modelica Model Descriptionmentioning

confidence: 99%

“…This can be done by hand, which results in an error-prone process, or automatically as provided, e.g., by Maple. Natural a Modelica compiler like OpenModelica has also capabilities to differentiate symbolically a Modelica model (see [5], [2]). This purely symbolic method usually yields a very efficient way to compute first-order derivatives for closed-form expressions.…”

Section: Derivativesmentioning

confidence: 99%

See 2 more Smart Citations

Efficient Implementation of Collocation Methods for Optimization using OpenModelica and ADOL-C

Ruge

Braun

Bachmann

et al. 2014

Linköping Electronic Conference Proceedings

Self Cite

View full text Add to dashboard Cite

Efficient calculation of the solutions of nonlinear optimal control problems (NOCPs) is becoming more and more important for today's control engineers. The systems to be controlled are typically described using differential-algebraic equations (DAEs), which can be conveniently formulated in Modelica. In addition, the corresponding optimization problem can be expressed using Optimica. Solution algorithms based on collocation methods are highly suitable for discretizing the underlying dynamic model formulation. Thereafter, the corresponding discretized optimization problem can be solved, e.g. by the interior-point optimizer Ipopt. The performance of the optimizer heavily depends on the availability of derivative information for the underlying optimization problem. Typically, the gradient of the objective function, the Jacobian of the DAEs as well as the Hessian matrix of the corresponding Lagrangian formulation need to be determined. If only some or none of these derivatives are provided, usually numerical approximations are used by the optimizer internally. OpenModelica supports the Optimica language and is capable of automatically generating the discretized optimization problem using collocation methods as well as the whole symbolic machinery available. In addition, all necessary derivative information is determined using the automatic differentiation capabilities of ADOL-C, which has now been integrated into the OpenModelica environment.

show abstract

Section: Modelica Model Descriptionmentioning

confidence: 99%

Section: Modelica Model Descriptionmentioning

confidence: 99%

Section: Derivativesmentioning

confidence: 99%

See 1 more Smart Citation

Efficient Implementation of Collocation Methods for Optimization using OpenModelica and ADOL-C

Ruge

Braun

Bachmann

et al. 2014

Linköping Electronic Conference Proceedings

Self Cite

View full text Add to dashboard Cite

show abstract

“…The derivatives are useful for simulating a model as well as for the sensitivity analysis or the optimization of models. Further, jacobians are necessary to support the next FMI 1 version 2.0 [1]. In the work before it was not possible to show improvements for the simulation.…”

Section: Introductionmentioning

confidence: 99%

“…The symbolical jacobians are generated within the OpenModelica compiler (for more details see [3], [1]). …”

Section: Introductionmentioning

confidence: 99%

Fast Simulation of Fluid Models with Colored Jacobians

Braun

Yances

Link

et al. 2012

Linköping Electronic Conference Proceedings

Self Cite

View full text Add to dashboard Cite

The industrial usage of the open-source Modelica tool OpenModelica was limited so far for power plant applications, due to the performance of large fluid systems. This paper presents some efforts to improve the simulation time on benchmark fluid models proposed by Siemens Energy. The main aspects presented here to achieve a faster simulation are an efficient evaluation of the jacobian matrix by a coloring technique, that exploits the sparsity pattern of a modelica model. Therefore the techniques are scratched and applied to benchmark models provided by Siemens Energy.

show abstract

References

2014

Principles of Object Oriented Modeling and Simulation With Modelica 3.3

View full text Add to dashboard Cite

Generation of Sparse Jacobians for the Function Mock-Up Interface 2.0

Cited by 9 publications

References 8 publications

Efficient Implementation of Collocation Methods for Optimization using OpenModelica and ADOL-C

Efficient Implementation of Collocation Methods for Optimization using OpenModelica and ADOL-C

Fast Simulation of Fluid Models with Colored Jacobians

References

Contact Info

Product

Resources

About