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.

Building complex systems form models that were developed separately without modifying existing code is a challenging task faced on a regular basis in multiple contexts, for instance, in design verification. To address this issue, this paper presents a new approach for automating the dynamic system model composition. The presented approach aims to maximise information reuse, by defining the minimum set of information that is necessary to the composition process, to maximise decoupling by removing the need for explicit interfaces and to present a methodology with a modular and structured approach to composition. Moreover the presented approach is illustrated in the context of system design verification against requirements using a Modelica environment, and an approach for expressing the information necessary for automating the composition is formalized.

The significant increase in the complexity and autonomy of the hardware systems renders the verification of the functional safety of each individual component as well as of the entire system a complex task and underlines the need for integrated, model based tools that would assist this process. In this paper the authors present such a tool, coupled with an approach to functional safety analysis, based on the integration of functional tests into the model itself. The analysis of the resulting model is done through a stochastic Bayesian model. This approach strives to both bypass the necessity for costly hardware testing and integrate the functional safety analysis into an intuitive component development process.

As cyber-physical systems grow increasingly complex, the need for methodologies and tool support for an automated requirement verification process becomes evident. Expressing requirements in a computable form becomes a crucial step in defining such a process. The equation based declarative nature of the Modelica language makes it an ideal candidate for modeling a large subset of system requirements. Moreover, modeling both the requirements and the system itself in the same language presents numerous advantages. However, a certain semantic gap subsists between the notions used in requirement modeling and the concepts of cyber-physical modeling that Modelica relies on. To bridge this gap, in this paper, we illustrate through the use of dedicated types, pseudo function calls and function block libraries, how the Modelica language can be tailored to fit the needs of requirement modeling engineers.

Requirement verification is an important part of the development process, and the increasing system complexity has exacerbated the need for integrating this step into a formalized model driven development process, providing a dedicated methodology as well as tool support. In this paper the authors propose an extension for Modelica, an equation-based language for system modeling, that will allow to represent system requirements in the same formalism as the design model, thus reducing the need for transformations between different specialized formalisms, lowering maintenance and modification costs, and benefitting from the expression and simulation capabilities, as well as extensive tool support of Modelica. The object-oriented nature of the approach provides the advantages of modular design and hierarchical structuring of the requirement model. This paper also illustrates, with the help of an example, how requirement verification can be used alongside the simulation process to trace the components responsible for requirement violations. To this end, we introduce a formalism for expressing relationships between components and requirements, as well as a tracing algorithm.

With the growing complexity of the hardware models, the verification of the functional safety of each individual component as well as of the entire system becomes increasingly complex. In this paper the authors present a novel approach to functional safety analysis, based on the integration of functional tests into the model itself and the analysis of resulting model through a stochastic Bayesian model. This approach strives to both bypass the necessity for costly hardware testing and integrate the functional safety analysis into an intuitive component development process.