Modelica - A Language for Physical System Modeling, Visualization and Interaction

Modelica is an object-oriented language for modeling of large, complex and heterogeneous physical systems. It is suited for multi-domain modeling, for example for modeling of mechatronics including cars, aircrafts and industrial robots which typically consist of mechanical, electrical and hydraulic subsystems as well as control systems. General equations are used for modeling of the physical phenomena, No particular variable needs to be solved for manually. A Modelica tool will have enough information to do that automatically. The language has been designed to allow tools to generate efficient code automatically. The modeling effort is thus reduced considerably since model components can be reused and tedious and error-prone manual manipulations are not needed. The principles of object-oriented modeling and the details of the Modelica language as well as several examples are presented

Introducing Explicit Causality in Object-oriented Hybrid System Modeling

International audienceAlong with the rapid development of embedded devices and network technology, the area of CyberPhysical Systems (CPS), has arisen. In terms of modeling and simulation, CPS—like many technical systems—have ahybrid nature, i.e., discrete-event behavior and continuous-time dynamics have to be integrated with each other.Basically, this integration is supported by modern object-oriented modeling paradigms such as Modelica®. Theequation-based concept resolves the causality between interconnected components, which qualifies this modelingscheme for complex multi-domain systems. However, in hybrid systems, explicit causality is required to correctlymanage iterative events. This paper highlights these issues, including algorithmic loops and instantaneous multipleupdates, which essentially arise from incompatibilities between the object-oriented concept and specific discrete-eventphenomena. We discuss several possible solutions and introduce the concept of re-allocating the objects’ behavioralintelligence

Tools and Languages for Modeling and Optimization of Large-Scale Dynamical Systems

High-level modeling languages are receiving increased industrial and academic interest within several domains, such as chemical engineering, thermo-fluid systems and automotive systems. One such modeling language is Modelica. Modelica is an open language, specifically targeted at multi-domain modeling and model re-use. Key features of Modelica include object oriented modeling, declarative equation modeling, a software component model enabling acausal connections of submodels, as well as support for hybrid/discrete behaviour. These features have proven very applicable to large-scale modeling problems in various fields. This contribution gives an overview of a project targeted at i) defining an extension of Modelica, Optimica, which enables high level formulation of optimization problems, ii) developing prototype tools for translating a Modelica model and a complementary Optimica description into a representation suited for numerical algorithms, and iii) performing case studies demonstrating the potential of the concept

Introducing Explicit Causality in Object-oriented Hybrid System Modeling

International audienceAlong with the rapid development of embedded devices and network technology, the area of CyberPhysical Systems (CPS), has arisen. In terms of modeling and simulation, CPS—like many technical systems—have ahybrid nature, i.e., discrete-event behavior and continuous-time dynamics have to be integrated with each other.Basically, this integration is supported by modern object-oriented modeling paradigms such as Modelica®. Theequation-based concept resolves the causality between interconnected components, which qualifies this modelingscheme for complex multi-domain systems. However, in hybrid systems, explicit causality is required to correctlymanage iterative events. This paper highlights these issues, including algorithmic loops and instantaneous multipleupdates, which essentially arise from incompatibilities between the object-oriented concept and specific discrete-eventphenomena. We discuss several possible solutions and introduce the concept of re-allocating the objects’ behavioralintelligence

Search-based system architecture development using a holistic modeling approach

This dissertation presents an innovative approach to system architecting where search algorithms are used to explore design trade space for good architecture alternatives. Such an approach is achieved by integrating certain model construction, alternative generation, simulation, and assessment processes into a coherent and automated framework. This framework is facilitated by a holistic modeling approach that combines the capabilities of Object Process Methodology (OPM), Colored Petri Net (CPN), and feature model. The resultant holistic model can not only capture the structural, behavioral, and dynamic aspects of a system, allowing simulation and strong analysis methods to be applied, it can also specify the architectural design space. Both object-oriented analysis and design (OOA/D) and domain engineering were exploited to capture design variables and their domains and define architecture generation operations. A fully realized framework (with genetic algorithms as the search algorithm) was developed. Both the proposed framework and its suggested implementation, including the proposed holistic modeling approach and architecture alternative generation operations, are generic. They are targeted at systems that can be specified using object-oriented or process-oriented paradigm. The broad applicability of the proposed approach is demonstrated on two examples. One is the configuration of reconfigurable manufacturing systems (RMSs) under multi-objective optimization and the other is the architecture design of a manned lunar landing system for the Apollo program. The test results show that the proposed approach can cover a huge number of architecture alternatives and support the assessment of several performance measures. A set of quality results was obtained after running the optimization algorithm following the proposed framework --Abstract, page iii

Pattern Reification as the Basis for Description-Driven Systems

One of the main factors driving object-oriented software development for information systems is the requirement for systems to be tolerant to change. To address this issue in designing systems, this paper proposes a pattern-based, object-oriented, description-driven system (DDS) architecture as an extension to the standard UML four-layer meta-model. A DDS architecture is proposed in which aspects of both static and dynamic systems behavior can be captured via descriptive models and meta-models. The proposed architecture embodies four main elements - firstly, the adoption of a multi-layered meta-modeling architecture and reflective meta-level architecture, secondly the identification of four data modeling relationships that can be made explicit such that they can be modified dynamically, thirdly the identification of five design patterns which have emerged from practice and have proved essential in providing reusable building blocks for data management, and fourthly the encoding of the structural properties of the five design patterns by means of one fundamental pattern, the Graph pattern. A practical example of this philosophy, the CRISTAL project, is used to demonstrate the use of description-driven data objects to handle system evolution.Comment: 20 pages, 10 figure

Реализация концептуальной объектно-ориентированной многомерной метамодели информационной системы

Multidimensional approach to the creation of analytical information systems allows to organize information in a convenient form for analysis. Object-oriented approach provides essential advantages in domain modeling. In the article it is offered to use this principles while implementing a multi-dimensional metamodel.Использование многомерного подхода при создании информационно-аналитических систем позволяет организовывать данные в удобной для анализа форме. Объектно-ориентированный подход предоставляет преимущества при моделировании предметной области. В статье разбираются вопросы реализации многомерной метамодели на базе этих принципов