Simulation of Large-Scale Models in Modelica: State of the Art and Future Perspectives

The "Embedded Simulation via FMI" is a new modeling approach which allows for efficient and fast computation of systems with a clear separation of time axis or time scale. For its application the so-called "inner model" is wrapped into an FMU and embedded into an outer model, whose dynamics control the integration. The computation of the embedded model is only utilized on demand. In this way the "Embedded Simulation via FMI" uses the Functional Mock-up Interface for Co-Simulation in a different way than it was provided for. The functionality has been realized within SimulationX prototypically. It is applied to the simulation of the lifetime test of a linear stepper motor including wear in the screw drive, for which the axial play after several months' runtime shall be determined. A significant reduction of computing time while preserving considerable accuracy can be shown.

Section: Discussionmentioning

confidence: 99%

The Embedded Simulation via FMI and its Application to the Simulation of Lifetime Tests Including Wear

Gundermann

Thiele

Fraulob

et al. 2017

“…However, due to efficiency issues well described in (Casella, 2015), such modelling approach is currently not suitable for the representation of large-scale DHS comprising thousands of consumers and encompassing several hundreds of kilometers of distribution pipes. For such systems, we developed a dedicated C++ simulation code, not detailed in the present paper, and applied it to the Grenoble DHS (see Figure 8).…”

Section: Discussionmentioning

confidence: 99%

Optimal Control of District Heating Systems using Dynamic Simulation and Mixed Integer Linear Programming

Giraud

Merabet

Bavière

et al. 2017

This paper presents the development of a new advanced control method suitable for variable temperature District Heating Systems (DHS). The proposed controller determines optimal planning for the on/off status and power of the heat generators as well as for the supply temperature and differential pressure at the production plant level. Compared to existing methods, the original features of the developed solution are to fully exploit the thermal storage capacity of the network and to determine the best compromise between pumping costs and heat losses. A numerical case study based on a representative DHS is used to evaluate the method over a heating season (5 months). Results show that our method reduces production costs up to 8.3 % when compared to a more classical controller. Moreover, the observed computing time is compatible with the requirements of the receding time horizon principle, ensuring that the method is tractable on real DHS.

“…Studies have been developed testing how compilers work on large scale models recognizing their limitations on that area (Frenkel et al, 2011;Sezginer, 2014Sezginer, -2015Casella, 2015). In (Jens Frenkel, 2012) particularly, the authors study different causalization (matching) algorithms applied to large scale models and conclude that the PF+ algorithm (by Duff) is the best choice as it achieves linear performance on the tested cases.…”

Section: Related Workmentioning

confidence: 99%

Efficient Compilation of Large Scale Dynamical Systems

Bergero

Botta²,

Campostrini³

et al. 2015

In this work, we present a novel methodology to efficiently compile large scale dynamical systems described as Modelica models, and its implementation in a prototype Modelica Compiler called ModelicaCC. The methodology allows to perform the different stages of the compilation process without expanding the content of repetitive structures so the resources (CPU time and memory) used by the compiler result independent on the model size. Besides introducing the methodology with their algorithms and the implementation in the ModelicaCC compiler, we analyze their efficiency comparing its performance with that of OpenModelica in different large scale models.