Special Issue "Selected Papers from Modelica Conference 2021"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 October 2022) | Viewed by 5637

Special Issue Editors

Dr. Martin Sjölund
E-Mail Website
Guest Editor
Department of Computer and Information Science (IDA), Linköping University, SE-581 83 Linköping, Sweden
Interests: software and systems (SAS); programming environments; debugging, modeling and simulation, programming language design
Prof. Dr. Peter Fritzson
E-Mail Website
Guest Editor
Department of Computer and Information Science, Linköpings University, SE-581 83 Linköping, Sweden
Interests: software engineering, especially programming languages; high-level specification and modeling languages; programming and debugging tools and environments; parallel and multicore computing, compilers and compiler generators; tools and languages for object-oriented equation-based modeling and simulation; Modelica language
Special Issues, Collections and Topics in MDPI journals
Dr. Lena Buffoni
E-Mail Website
Guest Editor
Department of Computer and Information Science (IDA), Linköping University, SE-581 83 Linköping, Sweden
Interests: language construction; requirement modeling; synchronous languages
Dr. Adrian Pop
E-Mail Website
Guest Editor
Department of Computer and Information Science, Linköpings University, 581 83 Linköping, Sweden
Interests: programming languages (design, implementation, debugging, etc.) and integrated development tools
Dr. Lennart Ochel
E-Mail Website
Guest Editor
RISE, 501 15 Borås, Sweden
Interests: FMI and SSP based simulation environments (OpenModelica compiler and OMSimulator) and autonomous drones and drone swarm applications

Special Issue Information

Dear Colleagues,

The Modelica Conference is the main event for users, library developers, tool vendors, and language designers to share their knowledge and learn about the latest scientific and industrial progress related to Modelica, the Functional Mockup Interface (FMI), System Structure and Parametrization (SSP), Distributed Co-Simulation Protocol (DCP), and eFMI.

Since the start of the collaborative design work for Modelica in 1996, Modelica has matured from an idea among a small number of dedicated enthusiasts to a widely accepted standard language for the modeling and simulation of cyber-physical systems.
The Modelica language was standardized by the non-profit Modelica Association, which enabled Modelica models to be portable between a growing number of tools.
Modelica is the language of choice for model-based systems engineering and is now used in many industries, including automotive, energy and process, aerospace, and industrial equipment.

The Modelica Association has since grown to include several projects supporting modeling and simulation, creating a family of inter-related standards complementing each other.
FMI is an open standard that defines a container and an interface to exchange dynamic models using a single file (an FMU).
SSP is a tool-independent standard to define complete systems consisting of one or more FMUs, including its parameterization, which can be transferred between simulation tools.
DCP is a platform and standard for the integration of models or real-time systems into simulation environments.
The eFMI tool enables the automatic transformation of higher-level acausal model representations (such as Modelica) to causal solutions suitable for integration in embedded systems.

Dr. Martin Sjölund
Prof. Dr. Peter Fritzson
Dr. Lena Buffoni
Dr. Adrian Pop
Dr. Lennart Ochel
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cyber-physical systems
  • modeling and simulation
  • Modelica
  • model-based systems

Published Papers (11 papers)

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Research

Article
The DLR ThermoFluid Stream Library
Electronics 2022, 11(22), 3790; https://doi.org/10.3390/electronics11223790 - 18 Nov 2022
Viewed by 249
Abstract
This paper introduces the DLR Thermofluid Stream Library: a free open-source library for the robust modeling of complex thermofluid architectures. Designed to be easy to use, easy to adapt, and enriched by a number of examples, this library contains the fundamental components for [...] Read more.
This paper introduces the DLR Thermofluid Stream Library: a free open-source library for the robust modeling of complex thermofluid architectures. Designed to be easy to use, easy to adapt, and enriched by a number of examples, this library contains the fundamental components for many different applications, such as thermal management of electric cars, power plants, or building physics. Different from many previous software implementations, the library exploits a new computational concept on the basis of the fluid inertance that derives the mass flow balance in such a manner that large non-linear equations systems in implicit form can be avoided. Hence, there is a reliable and efficient computational scheme for simulation and initialization. Although this scheme is explained in detail in previous publications, this paper focuses on the implementational aspects of the library, explaining its structure, the underlying equations of key components and providing basic examples. In addition to the practical value of the library, we also aim to display the underlying thought driving the design of the software. Full article
(This article belongs to the Special Issue Selected Papers from Modelica Conference 2021)
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Article
The FMI 3.0 Standard Interface for Clocked and Scheduled Simulations
Electronics 2022, 11(21), 3635; https://doi.org/10.3390/electronics11213635 - 07 Nov 2022
Viewed by 340
Abstract
This paper presents an overview and formalization of the Functional Mock-up Interface (FMI) 3.0. The formalization captures the new FMI 3.0 standard and is intended to be used as an introduction for conceptualizing the use of clocks in the FMI standard to support [...] Read more.
This paper presents an overview and formalization of the Functional Mock-up Interface (FMI) 3.0. The formalization captures the new FMI 3.0 standard and is intended to be used as an introduction for conceptualizing the use of clocks in the FMI standard to support the simulation of event-based cyber-physical systems. The FMI 3.0 standard supports two kinds of clock-based simulations: Synchronous Clocked Simulation to ensure predictable systems scheduling with multiple simultaneous events and scheduled execution to facilitate real-time simulations comprising multiple black-box models by allowing fine-grained control over the computation time of submodels. The formalization is a basis for developing tools for orchestrating, verifying and validating the composition of multiple FMUs. The formalization is provided as an accessible VDM-SL specification. Full article
(This article belongs to the Special Issue Selected Papers from Modelica Conference 2021)
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Article
A Graph-Based Metadata Model for DevOps in Simulation-Driven Development and Generation of DCP Configurations
Electronics 2022, 11(20), 3325; https://doi.org/10.3390/electronics11203325 - 15 Oct 2022
Viewed by 263
Abstract
With the goal of improving the quality of model-based development and to reduce testing effort, DevOps practices have gained more and more importance. However, most system engineers are not DevOps specialists, and there are a lot of manual steps involved when writing build [...] Read more.
With the goal of improving the quality of model-based development and to reduce testing effort, DevOps practices have gained more and more importance. However, most system engineers are not DevOps specialists, and there are a lot of manual steps involved when writing build pipelines and configurations of simulations. For this purpose, an abstract graph-based metadata model is proposed. This allows the autogeneration of scenario descriptions for simulations and code for the build server where the simulation environment is set up and executed. This is demonstrated by applying this process to the DCP standard. In this paper, we will discuss three simple use cases which are motivated by practical problems that arise in complex development environments and how the proposed solutions can be used to tackle them. Detailed descriptions and implementations of the use cases show how the proposed methods can be applied in practice and help solve the described problems. Furthermore, a Python implementation of a DCP master and a simple FMI-to-DCP wrapper are presented in this work. Full article
(This article belongs to the Special Issue Selected Papers from Modelica Conference 2021)
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Article
NeuralFMU: Presenting a Workflow for Integrating Hybrid NeuralODEs into Real-World Applications
Electronics 2022, 11(19), 3202; https://doi.org/10.3390/electronics11193202 - 06 Oct 2022
Viewed by 333
Abstract
The term NeuralODE describes the structural combination of an Artificial Neural Network (ANN) and a numerical solver for Ordinary Differential Equations (ODE), the former acts as the right-hand side of the ODE to be solved. This concept was further extended by a black-box [...] Read more.
The term NeuralODE describes the structural combination of an Artificial Neural Network (ANN) and a numerical solver for Ordinary Differential Equations (ODE), the former acts as the right-hand side of the ODE to be solved. This concept was further extended by a black-box model in the form of a Functional Mock-up Unit (FMU) to obtain a subclass of NeuralODEs, named NeuralFMUs. The resulting structure features the advantages of the first-principle and data-driven modeling approaches in one single simulation model: a higher prediction accuracy compared to conventional First-Principle Models (FPMs) and also a lower training effort compared to purely data-driven models. We present an intuitive workflow to set up and use NeuralFMUs, enabling the encapsulation and reuse of existing conventional models exported from common modeling tools. Moreover, we exemplify this concept by deploying a NeuralFMU for a consumption simulation based on a Vehicle Longitudinal Dynamics Model (VLDM), which is a typical use case in the automotive industry. Related challenges that are often neglected in scientific use cases, such as real measurements (e.g., noise), an unknown system state or high-frequency discontinuities, are handled in this contribution. To build a hybrid model with a higher prediction quality than the original FPM, we briefly highlight two open-source libraries: FMI.jl, which allows for the import of FMUs into the Julia programming language, as well as the library FMIFlux.jl, which enables the integration of FMUs into neural network topologies to obtain a NeuralFMU. Full article
(This article belongs to the Special Issue Selected Papers from Modelica Conference 2021)
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Article
Realizing Interoperability between MBSE Domains in Aircraft System Development
Electronics 2022, 11(18), 2901; https://doi.org/10.3390/electronics11182901 - 13 Sep 2022
Viewed by 353
Abstract
Establishing interoperability is an essential aspect of the often-pursued shift towards Model-Based Systems Engineering (MBSE) in, for example, aircraft development. If models are to be the primary information carriers during development, the applied methods to enable interaction between engineering domains need to be [...] Read more.
Establishing interoperability is an essential aspect of the often-pursued shift towards Model-Based Systems Engineering (MBSE) in, for example, aircraft development. If models are to be the primary information carriers during development, the applied methods to enable interaction between engineering domains need to be modular, reusable, and scalable. Given the long life cycles and often large and heterogeneous development organizations in the aircraft industry, a piece to the overall solution could be to rely on open standards and tools. In this paper, the standards Functional Mock-up Interface (FMI) and System Structure and Parameterization (SSP) are exploited to exchange data between the disciplines of systems simulation and geometry modeling. A method to export data from the 3D Computer Aided Design (CAD) Software (SW) CATIA in the SSP format is developed and presented. Analogously, FMI support of the Modeling & Simulation (M&S) tools OMSimulator, OpenModelica, and Dymola is utilized along with the SSP support of OMSimulator. The developed technology is put into context by means of integration with the M&S methodology for aircraft vehicle system development deployed at Saab Aeronautics. Finally, the established interoperability is demonstrated on two different industrially relevant application examples addressing varying aspects of complexity. A primary goal of the research is to prototype and demonstrate functionality, enabled by the SSP and FMI standards, that could improve on MBSE methodology implemented in industry and academia. Full article
(This article belongs to the Special Issue Selected Papers from Modelica Conference 2021)
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Article
Signal Tables: An Extensible Exchange Format for Simulation Data
Electronics 2022, 11(18), 2811; https://doi.org/10.3390/electronics11182811 - 06 Sep 2022
Viewed by 330
Abstract
This article introduces Signal Tables as a format to exchange data associated with simulations based on dictionaries and multi-dimensional arrays. Typically, simulation results, as well as model parameters, reference signals, table-based input signals, measurement data, look-up tables, etc., can be represented by a [...] Read more.
This article introduces Signal Tables as a format to exchange data associated with simulations based on dictionaries and multi-dimensional arrays. Typically, simulation results, as well as model parameters, reference signals, table-based input signals, measurement data, look-up tables, etc., can be represented by a Signal Table. Applications can extend the format to add additional data and metadata/attributes, for example, as needed for a credible simulation process. The format follows a logical view based on a few data structures that can be directly mapped to data structures available in programming languages such as Julia, Python, and Matlab. These data structures can be conveniently used for pre- and post-processing in these languages. A Signal Table can be stored on file by mapping the logical view to available textual or binary persistent file formats, for example, JSON, HDF5, BSON, and MessagePack. A subset of a Signal Table can be imported in traditional tables, for example, in Excel, CSV, pandas, or DataFrames.jl, by flattening multi-dimensional arrays and not storing parameters. The format has been developed and evaluated with the Open Source Julia packages SignalTables.jl and Modia.jl. Full article
(This article belongs to the Special Issue Selected Papers from Modelica Conference 2021)
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Article
Algorithms for the Structural Analysis of Multimode Modelica Models
Electronics 2022, 11(17), 2755; https://doi.org/10.3390/electronics11172755 - 01 Sep 2022
Viewed by 302
Abstract
Since its 3.3 release, Modelica offers the possibility to specify models of dynamical systems with multiple modes having different DAE-based dynamics. However, the handling of such models by the current Modelica tools is not satisfactory, with mathematically sound models yielding exceptions at runtime. [...] Read more.
Since its 3.3 release, Modelica offers the possibility to specify models of dynamical systems with multiple modes having different DAE-based dynamics. However, the handling of such models by the current Modelica tools is not satisfactory, with mathematically sound models yielding exceptions at runtime. In this article, we propose several contributions to this multifaceted issue, namely: an efficient and scalable multimode extension of the structural analysis of Modelica models; a systematic way of rewriting a multimode Modelica model, based on this analysis, so that the rewritten model is guaranteed to be correctly compiled by state-of-the-art Modelica tools; a proposal for the handling of the consistent initialization of multimode models; multimode structural analysis algorithms that handle both multiple modes and mode change events in a unified framework, coupled with a compile-time algorithm for identifying and quantifying impulsive behaviors at mode changes. Our approach is illustrated on relevant example models, and the performance of our implementations is assessed on a variable dimension large-scale model. Full article
(This article belongs to the Special Issue Selected Papers from Modelica Conference 2021)
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Article
Towards Modelica Models with Credibility Information
Electronics 2022, 11(17), 2728; https://doi.org/10.3390/electronics11172728 - 30 Aug 2022
Cited by 2 | Viewed by 423
Abstract
Modeling and simulation is increasingly used in the design process for a wide span of applications. Rising demands and the complexity of modern products also increase the need for models and tools capable to cover areas such as virtual testing, design-space exploration or [...] Read more.
Modeling and simulation is increasingly used in the design process for a wide span of applications. Rising demands and the complexity of modern products also increase the need for models and tools capable to cover areas such as virtual testing, design-space exploration or digital twins, and to provide measures of the quality of the models and the achieved results. The latter is also called credible simulation process. In an article at the International Modelica Conference 2021, we summarized the state of the art and best practice from the viewpoint of a Modelica language user, based on the experience gained in projects in which Modelica models were utilized in the design process. Furthermore, missing features and gaps in the used processes were identified. In this article, new proposals are presented to improve the quality of Modelica models, in particular by adding traceability, uncertainty, and calibration information of the parameters in a standardized way to Modelica models. Furthermore, the new open-source Modelica library Credibility is discussed together with examples to support the implementation of credible Modelica models. Full article
(This article belongs to the Special Issue Selected Papers from Modelica Conference 2021)
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Article
Modeling of Satellite Constellation in Modelica and a PHM System Framework Driven by Model Data Hybrid
Electronics 2022, 11(14), 2155; https://doi.org/10.3390/electronics11142155 - 09 Jul 2022
Viewed by 477
Abstract
The new generation of low-earth-orbit (LEO) satellite constellation systems has the characteristics of low delay, strong signal and global coverage, and it is an important direction for the development of next-generation communication technology. A major disadvantage is that the constellation system is huge, [...] Read more.
The new generation of low-earth-orbit (LEO) satellite constellation systems has the characteristics of low delay, strong signal and global coverage, and it is an important direction for the development of next-generation communication technology. A major disadvantage is that the constellation system is huge, often composed of hundreds or thousands of satellites, which puts forward high requirements for the design and health management of the constellation system, and the existing telemetry data monitoring system cannot meet the actual needs. CPS is a multidimensional complex system that integrates computation, communication and control (3C). Through the deep integration and cooperation of 3C, the real-time monitoring and dynamic control of large-scale engineering systems are realized, which is completely suitable for the operation and maintenance requirements of the satellite constellation system. This paper firstly establishes the entire satellite constellation system model, which is integrated from the satellite multidomain system, the constellation orbit environment system and the communication link system. Then, according to the technical concept of cyber-physical systems (CPS), an implementation framework of a prognostics and health (PHM) system driven by a model–data hybrid for satellite constellation systems is proposed. The framework is based on model simulation data and telemetry data and combines virtual and real data fusion, fault diagnosis, simulation prediction and other technologies to generate enhanced data to drive the effective operation of the PHM system. Finally, a verification case is designed to prove that the satellite constellation health management system implemented under this framework has a positive effect on the reliable operation and maintenance of the satellite constellation system. Full article
(This article belongs to the Special Issue Selected Papers from Modelica Conference 2021)
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Article
A Modular, Extensible, and Modelica-Standard-Compliant OpenModelica Compiler Framework in Julia Supporting Structural Variability
Electronics 2022, 11(11), 1772; https://doi.org/10.3390/electronics11111772 - 02 Jun 2022
Cited by 1 | Viewed by 662
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Selected Papers from Modelica Conference 2021)
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Article
Dynamics Modeling of Bearing with Defect in Modelica and Application in Direct Transfer Learning from Simulation to Test Bench for Bearing Fault Diagnosis
Electronics 2022, 11(4), 622; https://doi.org/10.3390/electronics11040622 - 17 Feb 2022
Cited by 1 | Viewed by 706
Abstract
In data-driven bearing fault diagnosis, sufficient fault data are fundamental for algorithm training and validation. However, only very few fault measurements can be provided in most industrial applications, bringing the dynamics model to produce bearing response under defects. This paper built a Modelica [...] Read more.
In data-driven bearing fault diagnosis, sufficient fault data are fundamental for algorithm training and validation. However, only very few fault measurements can be provided in most industrial applications, bringing the dynamics model to produce bearing response under defects. This paper built a Modelica model for the whole bearing test rig, including the test bearing, driving motor and hydraulic loading system. First, a five degree-of-freedom (5-DoF) model was proposed for the test bearing to identify the normal bearing dynamics. Next, a fault model was applied to characterize the defect position, defect size, defect shape and multiple defects. The virtual bearing test bench was first developed with OpenModelica and then called in Python with OMPython. For validation of the positive effect of the dynamics model in the direct transfer learning for bearing fault diagnosis, the simulation data from the Modelica model and experimental data from the Case Western Reserve University were fed separately or jointly to train a Convolution Neural Network (CNN). Then the well-trained CNN was transferred directly to achieve the fault diagnosis under the test set consisting of experiment data. Additionally, 157 features were extracted from both time-domain and frequency-domain and fed into CNN as input, and then four different validation cases were designed. The results confirmed the positive effect of simulation data in the CNN transfer learning, especially when the simulation data were added as auxiliary to experimental data, and improved CNN classification accuracy. Furthermore, it indicated that the simulation data from the bearing dynamics model could play a part in the actual experimental measurement when the collected data were insufficient. Full article
(This article belongs to the Special Issue Selected Papers from Modelica Conference 2021)
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