The OpenModelica integrated environment for modeling, simulation, and model-based development

OpenModelica is a unique large-scale integrated open-source Modelica- and FMI-based modeling, simulation, optimization, model-based analysis and development environment. Moreover, the OpenModelica environment provides a number of facilities such as debugging; optimization; visualization and 3D animation; web-based model editing and simulation; scripting from Modelica, Python, Julia, and Matlab; efficient simulation and co-simulation of FMI-based models; compilation for embedded systems; Modelica- UML integration; requirement verification; and generation of parallel code for multi-core architectures. The environment is based on the equation-based object-oriented Modelica language and currently uses the MetaModelica extended version of Modelica for its model compiler implementation. This overview paper gives an up-to-date description of the capabilities of the system, short overviews of used open source symbolic and numeric algorithms with pointers to published literature, tool integration aspects, some lessons learned, and the main vision behind its development.Fil: Fritzson, Peter. Linköping University; SueciaFil: Pop, Adrian. Linköping University; SueciaFil: Abdelhak, Karim. Fachhochschule Bielefeld; AlemaniaFil: Asghar, Adeel. Linköping University; SueciaFil: Bachmann, Bernhard. Fachhochschule Bielefeld; AlemaniaFil: Braun, Willi. Fachhochschule Bielefeld; AlemaniaFil: Bouskela, Daniel. Electricité de France; FranciaFil: Braun, Robert. Linköping University; SueciaFil: Buffoni, Lena. Linköping University; SueciaFil: Casella, Francesco. Politecnico di Milano; ItaliaFil: Castro, Rodrigo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Franke, Rüdiger. Abb Group; AlemaniaFil: Fritzson, Dag. Linköping University; SueciaFil: Gebremedhin, Mahder. Linköping University; SueciaFil: Heuermann, Andreas. Linköping University; SueciaFil: Lie, Bernt. University of South-Eastern Norway; NoruegaFil: Mengist, Alachew. Linköping University; SueciaFil: Mikelsons, Lars. Linköping University; SueciaFil: Moudgalya, Kannan. Indian Institute Of Technology Bombay; IndiaFil: Ochel, Lennart. Linköping University; SueciaFil: Palanisamy, Arunkumar. Linköping University; SueciaFil: Ruge, Vitalij. Fachhochschule Bielefeld; AlemaniaFil: Schamai, Wladimir. Danfoss Power Solutions GmbH & Co; AlemaniaFil: Sjolund, Martin. Linköping University; SueciaFil: Thiele, Bernhard. Linköping University; SueciaFil: Tinnerholm, John. Linköping University; SueciaFil: Ostlund, Per. Linköping University; Sueci

EOOLT 2007 – Proceedings of the 1st International Workshop on Equation-Based Object-Oriented Languages and Tools

Computer aided modeling and simulation of complex systems, using components from multiple application domains, such as electrical, mechanical, hydraulic, control, etc., have in recent years witness0065d a significant growth of interest. In the last decade, novel equation-based object-oriented (EOO) modeling languages, (e.g. Mode- lica, gPROMS, and VHDL-AMS) based on acausal modeling using equations have appeared. Using such languages, it has become possible to model complex systems covering multiple application domains at a high level of abstraction through reusable model components. The interest in EOO languages and tools is rapidly growing in the industry because of their increasing importance in modeling, simulation, and specification of complex systems. There exist several different EOO language communities today that grew out of different application areas (multi-body system dynamics, electronic circuit simula- tion, chemical process engineering). The members of these disparate communities rarely talk to each other in spite of the similarities of their modeling and simulation needs. The EOOLT workshop series aims at bringing these different communities together to discuss their common needs and goals as well as the algorithms and tools that best support them. Despite the short deadlines and the fact that this is a new not very established workshop series, there was a good response to the call-for-papers. Thirteen papers and one presentation were accepted to the workshop program. All papers were subject to reviews by the program committee, and are present in these electronic proceedings. The workshop program started with a welcome and introduction to the area of equa- tion-based object-oriented languages, followed by paper presentations and discussion sessions after presentations of each set of related papers. On behalf of the program committee, the Program Chairmen would like to thank all those who submitted papers to EOOLT'2007. Special thanks go to David Broman who created the web page and helped with organization of the workshop. Many thanks to the program committee for reviewing the papers. EOOLT'2007 was hosted by the Technical University of Berlin, in conjunction with the ECOOP'2007 conference

First-class models: on a noncausal language for higher-order and structurally dynamic modelling and simulation

The field of physical modelling and simulation plays a vital role in advancing numerous scientific and engineering disciplines. To cope with the increasing size and complexity of physical models, a number of modelling and simulation languages have been developed. These languages can be divided into two broad categories: causal and noncausal. Causal languages express a system model in terms of directed equations. In contrast, a noncausal model is formulated in terms of undirected equations. The fact that the causality can be left implicit makes noncausal languages more declarative and noncausal models more reusable. These are considered to be crucial advantages in many physical domains. Current, mainstream noncausal languages do not treat equational models as first-class values; that is, a model cannot be parametrised on other models or generated at simulation runtime. This results in very limited higher-order and structurally dynamic modelling capabilities, and limits the expressiveness and applicability of noncausal languages. This thesis is about a novel approach to the design and implementation of noncausal languages with first-class models supporting higher-order and structurally dynamic modelling. In particular, the thesis presents a language that enables: (1) higher-order modelling capabilities by embedding noncausal models as first-class entities into a functional programming language and (2) efficient simulation of noncausal models that are generated at simulation runtime by runtime symbolic processing and just-in-time compilation. These language design and implementation approaches can be applied to other noncausal languages. This thesis provides a self-contained reference for such an undertaking by defining the language semantics formally and providing an in-depth description of the implementation. The language provides noncausal modelling and simulation capabilities that go beyond the state of the art, as backed up by a range of examples presented in the thesis, and represents a significant progress in the field of physical modelling and simulation

First-class models: on a noncausal language for higher-order and structurally dynamic modelling and simulation

The field of physical modelling and simulation plays a vital role in advancing numerous scientific and engineering disciplines. To cope with the increasing size and complexity of physical models, a number of modelling and simulation languages have been developed. These languages can be divided into two broad categories: causal and noncausal. Causal languages express a system model in terms of directed equations. In contrast, a noncausal model is formulated in terms of undirected equations. The fact that the causality can be left implicit makes noncausal languages more declarative and noncausal models more reusable. These are considered to be crucial advantages in many physical domains. Current, mainstream noncausal languages do not treat equational models as first-class values; that is, a model cannot be parametrised on other models or generated at simulation runtime. This results in very limited higher-order and structurally dynamic modelling capabilities, and limits the expressiveness and applicability of noncausal languages. This thesis is about a novel approach to the design and implementation of noncausal languages with first-class models supporting higher-order and structurally dynamic modelling. In particular, the thesis presents a language that enables: (1) higher-order modelling capabilities by embedding noncausal models as first-class entities into a functional programming language and (2) efficient simulation of noncausal models that are generated at simulation runtime by runtime symbolic processing and just-in-time compilation. These language design and implementation approaches can be applied to other noncausal languages. This thesis provides a self-contained reference for such an undertaking by defining the language semantics formally and providing an in-depth description of the implementation. The language provides noncausal modelling and simulation capabilities that go beyond the state of the art, as backed up by a range of examples presented in the thesis, and represents a significant progress in the field of physical modelling and simulation

Foundations of Multi-Paradigm Modelling for Cyber-Physical Systems

This open access book coherently gathers well-founded information on the fundamentals of and formalisms for modelling cyber-physical systems (CPS). Highlighting the cross-disciplinary nature of CPS modelling, it also serves as a bridge for anyone entering CPS from related areas of computer science or engineering. Truly complex, engineered systems—known as cyber-physical systems—that integrate physical, software, and network aspects are now on the rise. However, there is no unifying theory nor systematic design methods, techniques or tools for these systems. Individual (mechanical, electrical, network or software) engineering disciplines only offer partial solutions. A technique known as Multi-Paradigm Modelling has recently emerged suggesting to model every part and aspect of a system explicitly, at the most appropriate level(s) of abstraction, using the most appropriate modelling formalism(s), and then weaving the results together to form a representation of the system. If properly applied, it enables, among other global aspects, performance analysis, exhaustive simulation, and verification. This book is the first systematic attempt to bring together these formalisms for anyone starting in the field of CPS who seeks solid modelling foundations and a comprehensive introduction to the distinct existing techniques that are multi-paradigmatic. Though chiefly intended for master and post-graduate level students in computer science and engineering, it can also be used as a reference text for practitioners

Semantic methods for functional hybrid modelling

Equation-based modelling languages have become a vital tool in many areas of science and engineering. Functional Hybrid Modelling (FHM) is an approach to equation-based modelling that allows the behaviour of a physical system to be expressed as a modular hierarchy of undirected equations. FHM supports a variety of advanced language features — such as higher-order models and variable system structure — that sets it apart from the majority of other modelling languages. However, the inception of these new features has not been accompanied by the semantic tools required to effectively use and understand them. Specifically, there is a lack of static safety assurances for dynamic models and the semantics of the aforementioned language features are poorly understood. Static safety guarantees are highly desirable as they allow problems that may cause an equation system to become unsolvable to be detected early, during compilation. As a result, the use of static analysis techniques to enforce structural invariants (e.g. that there are the same number of equations as unknowns) is now in use in main-stream equation-based languages like Modelica. Unfortunately, the techniques employed by these languages are somewhat limited, both in their capacity to deal with advanced language features and also by the spectrum of invariants they are able to enforce. Formalising the semantics of equation-based languages is also important. Semantics allow us to better understand what a program is doing during execution, and to prove that this behaviour meets with our expectation. They also allow different implementations of a language to agree with one another, and can be used to demonstrate the correctness of a compiler or interpreter. However, current attempts to formalise such semantics typically fall short of describing advanced features, are not compositional, and/or fail to show correctness. This thesis provides two major contributions to equation-based languages. Firstly, we develop a refined type system for FHM capable of capturing a larger number of structural anomalies than is currently possible with existing methods. Secondly, we construct a compositional semantics for the discrete aspects of FHM, and prove a number of key correctness properties

Semantic methods for functional hybrid modelling

Equation-based modelling languages have become a vital tool in many areas of science and engineering. Functional Hybrid Modelling (FHM) is an approach to equation-based modelling that allows the behaviour of a physical system to be expressed as a modular hierarchy of undirected equations. FHM supports a variety of advanced language features — such as higher-order models and variable system structure — that sets it apart from the majority of other modelling languages. However, the inception of these new features has not been accompanied by the semantic tools required to effectively use and understand them. Specifically, there is a lack of static safety assurances for dynamic models and the semantics of the aforementioned language features are poorly understood. Static safety guarantees are highly desirable as they allow problems that may cause an equation system to become unsolvable to be detected early, during compilation. As a result, the use of static analysis techniques to enforce structural invariants (e.g. that there are the same number of equations as unknowns) is now in use in main-stream equation-based languages like Modelica. Unfortunately, the techniques employed by these languages are somewhat limited, both in their capacity to deal with advanced language features and also by the spectrum of invariants they are able to enforce. Formalising the semantics of equation-based languages is also important. Semantics allow us to better understand what a program is doing during execution, and to prove that this behaviour meets with our expectation. They also allow different implementations of a language to agree with one another, and can be used to demonstrate the correctness of a compiler or interpreter. However, current attempts to formalise such semantics typically fall short of describing advanced features, are not compositional, and/or fail to show correctness. This thesis provides two major contributions to equation-based languages. Firstly, we develop a refined type system for FHM capable of capturing a larger number of structural anomalies than is currently possible with existing methods. Secondly, we construct a compositional semantics for the discrete aspects of FHM, and prove a number of key correctness properties

Efficient implementations of expressive modelling languages

This thesis is concerned with modelling languages aimed at assisting with modelling and simulation of systems described in terms of differential equations. These languages can be split into two classes: causal languages, where models are expressed using directed equations; and non-causal languages, where models are expressed using undirected equations. This thesis focuses on two related paradigms: FRP and FHM. FRP is an approach to programming causal time-aware applications that has successfully been used in causal modelling applications; while FHM is an approach to programming non-causal modelling applications. However, both are built on similar principles, namely, the treatment of models as first-class entities, allowing for models to be parametrised by other models or computed at runtime; and support for structurally dynamic models, whose behaviour can change during the simulation. This makes FRP and FHM particularly flexible and expressive approaches to modelling, especially compared to other mainstream languages. Because of their highly expressive and flexible nature, providing efficient implementations of these languages is a challenge. This thesis explores novel implementation techniques aimed at improving the performance of existing implementations of FRP and FHM, and other expressive modelling languages built on similar ideas. In the setting of FRP, this thesis proposes a novel embedded FRP library that uses the implementation approach of synchronous dataflow languages. This allows for significant performance improvement by better handling of the reactive network's topology, which represents a large portion of the runtime in current implementations, especially for applications that make heavy use of continuously varying values, such as modelling applications. In the setting of FHM, this thesis presents the modular compilation of a language based on FHM. Due to inherent difficulties with the simulation of systems of undirected equations, previous implementations of FHM and similarly expressive languages were either interpreted or generated code on the fly using just-in-time compilation, two techniques which have runtime overhead over ahead-of-time compilation. This thesis presents a new method for generating code for equation systems which allows for the separate compilation of FHM models. Compared with current approaches to FRP and FHM implementation, there is greater commonality between the implementation approaches described here, suggesting a possible way forward towards a future non-causal modelling language supporting FRP-like features, resulting in an even more expressive modelling language

Model-Driven Development of Control Applications: On Modeling Tools, Simulations and Safety

Control systems are required in various industrial applications varying from individual machines to manufacturing plants and enterprises. Software applications have an important role as an implementation technology in such systems, which can be based on Distributed Control System (DCS) or Programmable Control System (PLC) platforms, for example. Control applications are computer programs that, with control system hardware, perform control tasks. Control applications are efficient and flexible by nature; however, their development is a complex task that requires the collaboration of experts and information from various domains of expertise.This thesis studies the use of Model-Driven Development (MDD) techniques in control application development. MDD is a software development methodology in which models are used as primary engineering artefacts and processed with both manual work and automated model transformations. The objective of the thesis is to explore whether or not control application development can benefit from MDD and selected technologies enabled by it. The research methodology followed in the thesis is the constructive approach of design science.To answer the research questions, tools are developed for modeling and developing control applications using UML Automation Profile (UML AP) in a model-driven development process. The modeling approach is developed based on open source tools on Eclipse platform. In the approach, modeling concepts are kept extendable. Models can be processed with model transformation techniques that plug in to the tool. The approach takes into account domain requirements related to, for example, re-use of design. According to assessment of industrial applicability of the approach and tools as part of it, they could be used for developing industrial DCS based control applications.Simulation approaches that can be used in conjunction to model-driven development of control applications are presented and compared. Development of a model-in-the-loop simulation support is rationalized to enable the use of simulations early while taking into account the special characteristics of the domain. A simulator integration is developed that transforms UML AP control application models to Modelica Modeling Language (ModelicaML) models, thus enabling closed-loop simulations with ModelicaML models of plants to be controlled. The simulation approach is applied successfully in simulations of machinery applications and process industry processes.Model-driven development of safety applications, which are parts of safety systems, would require taking into account safety standard requirements related to modeling techniques and documentation, for example. Related to this aspect, the thesis focuses on extending the information content of models with aspects that are required for safety applications. The modeling of hazards and their associated risks is supported with fault tree notation. The risk and hazard information is integrated into the development process in order to improve traceability. Automated functions enable generating documentation and performing consistency checks related to the use of standard solutions, for example. When applicable, techniques and notations, such as logic diagrams, have been chosen so that they are intuitive to developers but also comply with recommendations of safety standards