An attempt has been made to enhance the thermodynamic capability of the general purpose modeling and simulation environment OpenModelica. The property database ChemSep and the thermodynamic algorithms of DWSIM are made available in OpenModelica. The following three approaches, listed in the order of increasing computational efficiency, are attempted in this work: Python-C API, socket programming, and a native port. The most efficient method of native port is adopted to make available NRTL, Peng-Robinson, UNIFAC, and UNIQUAC algorithms in OpenModelica. Through several examples, OpenModelica results are compared with Aspen Plus, indicating a good match in all cases. This work is released as an open source to enhance the collaboration among chemical engineers.

The need for integrating system modeling with advanced tool capabilities is becoming increasingly pronounced. For example, a set of simulation experiments may give rise to new data that are used to systematically construct a series of new models, e.g. for further simulation and design optimization. Such combined symbolic-numeric capabilities have been pioneered by dynamically typed interpreted languages such as Lisp and Mathematica. Such capabilities are also relevant for advanced modeling and simulation applications but lacking in the standard Modelica language. Therefore, this is a topic of long-running design discussions in the Modelica Design group. One contribution in this direction is MetaModelica, that has been developed to extend Modelica with symbolic operations and advanced data structures, while preserving safe engineering practices through static type checking and a compilation-based efficient implementation. Another recent effort is Modia, implemented using the Julia macro mechanism, making it dynamically typed but also adding new capabilities. The Julia language has appeared rather recently and has expanded into a large and fast-growing ecosystem. It is dynamically typed, provides both symbolic and numeric operations, advanced data structures, and has a just-intime compilation-based efficient implementation. Despite independent developments there are surprisingly many similarities between Julia and MetaModelica. This paper presents MetaModelica and its environment as a large case study, together with a short comparison to Julia. Since Julia may be important for the future Modelica, some integration options between Modelica tools and Julia are also discussed, including a possible approach for implementing MetaModelica (and OpenModelica) in Julia.

In many mathematical models of physical phenomenons and engineering fields, such as electrical circuits or mechanical multibody systems, which generate the differential algebraic equations (DAEs) systems naturally. In general, the feature of DAEs is a sparse large scale system of fully nonlinear and high index. To make use of its sparsity, this paper provides a simple and efficient algorithm for index reduction of large scale DAEs system. We exploit the shortest augmenting path algorithm for finding maximum value transversal (MVT) as well as block triangular forms (BTFs). We also present the extended signature matrix method with the block fixed point iteration and its complexity results. Furthermore, a range of nontrivial problems are demonstrated by our algorithm. (C) 2015 Elsevier Inc. All rights reserved.

Domine el modelamiento y simulación usando Modelica, el nuevo poderoso y altamente versátil lenguaje de modelamiento basado en objetos.

Modelica, el nuevo lenguaje de modelamiento de software/hardware orientado a objetos que está ganando una rápida popularidad en el mundo entero, ofrece un acercamiento casi universal al modelamiento y simulación computacional de alto nivel. Modelica maneja un amplio rango de dominios de aplicación, por ejemplo, sistemas mecánicos, eléctricos, de control, y termodinámicos, y facilita el uso de notación general así como el uso de poderosas abstracciones e implementaciones eficientes. Usando el versátil lenguaje de Modelica y su tecnología asociada, este texto presenta un acercamiento orientado a objetos basado en componentes que le hace posible a los lectores dominar rápidamente las bases del modelamiento matemático basado en ecuaciones orientado a objetos (EOO por sus siglas en inglés) y simulación soportado por computadora.

A través de este texto Modelica se usa para ilustrar los diferentes aspectos del modelamiento y la simulación. A la vez, se explican varios conceptos claves del lenguaje Modelica con el uso de ejemplos de modelamiento y simulación. Este libro:

• Examina los conceptos básicos tales como sistemas, modelos y simulaciones
• Guía al lector a través del lenguaje Modelica con la ayuda de varios ejemplos paso a paso
• Introduce el concepto de la clase Modelica y su uso en el modelamiento gráfico y basado en texto.
• Explora las metodologías de modelamiento para sistemas continuos, discretos e híbridos
• Presenta una revisión de la Librería Estándar de Modelica y las librerías clave de modelos de Modelica

Los lectores encontrarán una buena cantidad de ejemplos de modelos que simulan aplicaciones en distintos dominios así como ejemplos que combinan varios dominios. Todos los ejemplos y ejercicios en el texto están disponibles a través de DrModelica. Este programa de auto enseñanza electrónico, disponible gratuitamente en el sitio web  que acompaña al texto, guía a los lectores desde ejemplos introductorios y simples hasta ejercicios mas avanzados.

Escrito por el Director del consorcio Open Source Modelica Consortium, Introducción al Modelamiento y Simulación de Sistemas Físicos y Técnicos con Modelica es un libro recomendado para ingenieros y estudiantes interesados en el diseño, modelamiento, simulación y análisis asistido por computador de sistemas técnicos y naturales. Partiendo de conceptos básicos, el texto es ideal para estudiantes quienes desean aprender del modelamiento y la simulación orientado a objetos.

Este libro está enfocado en la enseñanza del modelamiento y simulación usando Modelica para principiantes, o en cursos donde hay limitado espacio de tiempo para una introducción a Modelica. Para un cubrimiento con mayor profundidad de este tópico se recomienda el libro Principles of Object-Oriented Modeling and Simulation with Modelica 3.3: A Cyber-Physical Approach, el cual también incluye el material introductorio de este libro.

Master modeling and simulation using Modelica, the new powerful, highly versatile object-based modeling language

Modelica, the new object-based software/hardware modeling language that is quickly gaining popularity around the world, offers an almost universal approach to high-level computational modeling and simulation. It handles a broad range of application domains, for example mechanics, electrical systems, control, and thermodynamics, and facilitates general notation as well as powerful abstractions and efficient implementations. Using the versatile Modelica language and its associated technology, this text presents an object-oriented, component-based approach that makes it possible for readers to quickly master the basics of computer-supported equation-based object-oriented (EOO) mathematical modeling and simulation.

Throughout the text, Modelica is used to illustrate the various aspects of modeling and simulation. At the same time, a number of key concepts underlying the Modelica language are explained with the use of modeling and simulation examples. This book:

• Examines basic concepts such as systems, models, and simulations

• Guides readers through the Modelica language with the aid of several step-by-step examples

• Introduces the Modelica class concept and its use in graphical and textual modeling

• Explores modeling methodology for continuous, discrete, and hybrid systems

• Presents an overview of the Modelica Standard Library and key Modelica model libraries

Readers will find plenty of examples of models that simulate distinct application domains as well as examples that combine several domains. All the examples and exercises in the text are available via DrModelica. This electronic self-teaching program, freely available on the text's companion website, guides readers from simple, introductory examples and exercises to more advanced ones.

Written by the Director of the Open Source Modelica Consortium, Introduction to Modeling and Simulation of Technical and Physical Systems with Modelica is recommended for engineers and students interested in computer-aided design, modeling, simulation, and analysis of technical and natural systems. By building on basic concepts, the text is ideal for students who want to learn modeling, simulation, and object orientation.

This book is aimed at teaching Modelica modeling and simulation to beginners, or in courses where there is only limited time for an introduction to Modelica. For more in-dept coverage of this topic, the book Principles of Object-Oriented Modeling and Simulation with Modelica 3.3: A Cyber-Physical Approach is recommended. That book also includes the introductory material of the small book.

This book is aimed at teaching Modelica modeling and simulation to beginners, or in courses where there is only limited time for an introduction to Modelica.

For more in-dept coverage of this topic, the book Principles of Object-Oriented Modeling and Simulation with Modelica 3.3: A Cyber-Physical Approach is recommended. That book also includes the introductory material of the small book.

The second edition features improvements and updates of the Modelica language including synchronous clocked constructs, examines basic concepts of cyber-physical, equation-based, object-oriented system modeling and simulation. Prof. Fritzson introduces the Modelica class concept and its use in graphical and textual modeling with several hundred examples from many application areas and explores modeling methodology for continuous, discrete, and hybrid systems; and more.

This text is aimed at System Modeling and Simulation engineers, control engineers, mechanical engineers, those working with CAD (Computer Aided Design), virtual reality, biochemistry, embedded systems, and data communication.

Fritzson covers the Modelica language in impressive depth from the basic concepts such as cyber-physical, equation-base, object-oriented, system, model, and simulation, while also incorporating over a hundred exercises and their solutions for a tutorial, easy-to-read experience.

• The only book with complete Modelica 3.3 coverage
• Over one hundred exercises and solutions
• Examines basic concepts such as cyber-physical, equation-based, object-oriented, system, model, and simulation

The complexity of models for the simulation of physical systems is steadily increasing. This makes the effective validation of models for different design aspects crucial. One of the many important aspects is the structural correctness and the behavior due to design parameters which are of particular concern for the modeling of wind turbines. This article presents a design and implementation of a role-based validation framework. The framework allows for the creation of validation rules for different design aspects. This is done by role models that are used to define restrictions for an aspect by roles and rules. Multiple role models can be combined to cover all design features during model development. Restrictions on how models can interact with each other can be defined, which broadens language-specific restriction capabilities. The resulting rules can then be tested on arbitrary models based on the Eclipse Modeling Framework, for which mapping between elements of the role model and elements of the validated modeling language must be provided. In the domain of wind turbines, this approach is evaluated by application to two kinds of modeling languages (Modelica and UML2). Role models and rules have shown to be easily described with the frameworks role model language and role model definitions are successfully re-used by the definition of mappings for both kinds of modeling languages.

So far no qualifiable automatic code generators (ACGs) are available for Modelica. Hence, digital control applications can be modeled and simulated in Modelica, but require tedious additional efforts (e.g., manual reprogramming) to produce qualifiable target system production code. In order to more fully leverage the potential of a model-based development (MBD) process in Modelica, a qualifiable automatic code generator is needed. Typical Modelica code generation is a fairly complex process which imposes a huge development burden to any efforts of tool qualification. This work aims at mapping a Modelica subset for digital control function development to a well-understood synchronous data-flow kernel language. This kernel language allows to resort to established compilation techniques for data-flow languages which are understood enough to be accepted by certification authorities. The mapping is established by providing a translational semantics from the Modelica subset to the synchronous data-flow kernel language. However, this translation turned out to be more intricate than initially expected and has given rise to several interesting issues that require suitable design decisions regarding the mapping and the language subset.

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.

This paper presents an automatic parallelization approach for handling complex task systems with heavy dependencies, including methods of analyzing dependencies, representing them in a convenient way, and processing the resulting task graph representation. We present a library-based task system representation, clustering, profiling, and scheduling approach to simplify the otherwise tedious process of parallelizing complex task systems. We have implemented a flexible and robust task system handling library to manipulate and parallelize these complex task systems on shared memory multi-core and multi-processor systems. The implementation has been developed as part of the OpenModelica simulation environment. We demonstrate methods of extracting and utilizing parallelism in the context of mathematical modeling languages.