Efficient connection processing in equation–based object–oriented models

Marzorati, Denise; Pastor, Joaquı́n; Kofman, Ernesto

doi:10.1016/j.amc.2021.126842

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…These results are currently being extended to translate general Modelica models into the μ -Modelica descriptions used by the QSS solver as a way to make these algorithms available in Modelica simulation environments. The initial stage of this conversion (flattening) has already been proposed in the work by Marzorati et al 75 With a similar goal of allowing Modelica tools to use QSS algorithms, another approach that can be adopted is that of using Functional Mockup Interfaces so that the models are co-simulated by both tools. 76…”

Section: Discussionmentioning

confidence: 99%

“…of this conversion (flattening) has already been proposed in the work by Marzorati et al 75 With a similar goal of allowing Modelica tools to use QSS algorithms, another approach that can be adopted is that of using Functional Mockup Interfaces so that the models are co-simulated by both tools. 76 Another open research line is that of automatic partitioning of models for load balancing in parallel simulation.…”

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confidence: 99%

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Discrete-event simulation of continuous-time systems: evolution and state of the art of quantized state system methods

Castro,

Bergonzi,

Marcosig

et al. 2024

SIMULATION

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In this work, we attempt to bring together the origins, main results, and recent advances on discrete-event simulation of continuous-time systems. Starting from the early approaches that aimed to represent continuous-time dynamics within the discrete-event system specification (DEVS) formalism framework, the work shows how these ideas gave place to the formalization of the quantized state system (QSS) family of numerical integration algorithms. Then, we describe the QSS algorithms, their properties, their extensions, and the main practical software tools implementing them. We also present a selection of simulation examples illustrating the main features and advantages through comparisons with state-of-the-art continuous-time simulation solvers.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Discrete-event simulation of continuous-time systems: evolution and state of the art of quantized state system methods

Castro,

Bergonzi,

Marcosig

et al. 2024

SIMULATION

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“…One suggestion would be for MTK to introduce descalarization or avoid scalarization during symbolic processing. Recent techniques for unscalarized processing are described in [48]. Another alternative is MTK support for DAE-Mode, as presented in [43,49].…”

Section: Future Workmentioning

confidence: 99%

A Modular, Extensible, and Modelica-Standard-Compliant OpenModelica Compiler Framework in Julia Supporting Structural Variability

2022

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Nowadays, industrial products are getting increasingly complex, and time-to-market is significantly shorter. Modeling and simulation tools for cyber-physical systems need to keep up with the increased complexity. This paper presents OpenModelica.jl, a modular and extensible Modelica compiler framework in Julia targeting ModelingToolkit.jl and supporting Variable Structured Systems. We extended the Modelica language with three new operators to support continuous-time mode-switching and reconfiguration via recompilation at runtime. Therefore, our compiler supports the Modelica language and variable structure systems via the aforementioned extensions. To our knowledge, there are no other Modelica tools available that support both standard Modelica and variable structure systems. We evaluated our framework using a standardized benchmark suite, in terms of simulation, compilation and recompilation performance. The results concerning compilation and simulation time performance were compared with the results of running the existing OpenModelica compiler with the same set of models. A custom benchmark was devised to estimate the cost in terms of recompilation when simulating variable structure systems. The performance experiments showed that OpenModelica.jl is currently about four times slower in terms of compilation time when compiling a transmission line model with tens of thousands of equations and variables. The difference in simulation performance between the two compilers was negligable. Furthermore, the impact of recompilation during the simulation was usually small compared with the simulation time for long simulations. The results are promising for a prototype, and we outline approaches to further improve both compilation and simulation performance as future research.

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Efficient Matching in Large DAE Models

Marzorati,

Fernández,

Kofman

2024

ACM Trans. Math. Softw.

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This article presents a matching algorithm for bipartite graphs containing repetitive structures and represented by intension as Set-Based Graphs . Under certain conditions on the structure of the graphs, the computational cost of this novel algorithm is not affected by the cardinality of the sets of vertices and edges. The main application of the algorithm is that of matching large Equation-Based Models where, provided that most equations are defined using for loop statements that iterates over vectors of unknown variables, the computational cost becomes independent of the growth of the vectors involved. Besides introducing the algorithm, the article describes its implementation in a Modelica compiler and studies its performance over different test models.

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Efficient connection processing in equation–based object–oriented models

Cited by 3 publications

References 2 publications

Discrete-event simulation of continuous-time systems: evolution and state of the art of quantized state system methods

Discrete-event simulation of continuous-time systems: evolution and state of the art of quantized state system methods

A Modular, Extensible, and Modelica-Standard-Compliant OpenModelica Compiler Framework in Julia Supporting Structural Variability

Efficient Matching in Large DAE Models

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