Modelica and Fmi As Enablers for Virtual Product Innovation
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Efficient Software Tools in the Renewable Energy Domain: Maple and Maplesim
EnviroInfo 2013: Environmental Informatics and Renewable Energies Copyright 2013 Shaker Verlag, Aachen, ISBN: 978-3-8440-1676-5 Efficient software tools in the renewable energy domain: Maple and MapleSim Ji ří H řebí ček 1, Jaroslav Urbánek 1,2 Abstract There are presented efficient software tools Maple and MapleSim for solving technical problems in the renewable energy domain using mathematics-based modelling. MapleSim represents the simulation environment, which has a graphical interface for interconnecting system components. The system models are then processed by the Maple (symbolic computation system) mathematics engine, and finally the differential-algebraic equations describing the solved systems are simulated numerically to produce and 2-D, 3-D visualise output results. Maple allows users to quickly focus and reliably solve problems with easy access to over 5000 algorithms and functions developed over 30 years of cutting-edge research and development. In the paper there are presented two solved problems in the renewa- ble energy domain, which are freely downloadable from the web of the Canadian company Maplesoft developing above software tools. 1. Introduction The Canadian company Maplesoft 3 has developed simple and friendly used software tools Maple® 4 and MapleSim® 5 which reduce the cost and effort of developing high-fidelity models of power generation and energy storage systems, including gas and steam turbine generator sets; wind, wave, and solar energy sys- tems and batteries. Maple information technology has been trusted as a cutting edge mathematical and technical tool for over 30 years. In that time, millions of users from around the world have used and relied on the power of Maple for their research, testing, analysis, design, teaching, and schoolwork (Gander/Hřebí ček 2004), (Lynch 2009), (Borwein/Skerritt 2011), (Fox 2011), (Hřebí ček et al 2011), (Hřebí ček 2012). -
Hosted by Indian Institute of Science, Bangalore
Hosted by Indian Institute of Science, Bangalore 29 November 2018 30 November 2018 Modelica Tutorials – Beginners and 1st Modelica Users’ Meet – India Intermediate level with Hands on (MUMI 2018) Venue Class Room – 222, ICER, IISc, Bangalore Register here https://tinyurl.com/isadigitaltwins Silver Sponsor Bronze Sponsor About Modelica A non-proprietary, object-oriented, equation based language to conveniently model complex multi- domain systems used by many Industries for Modeling and Simulation Control Edge Designer MIKE from OpenModelica from from Bosch Rexroth DHI OSMC SimulationX from ESI ITI Technologies GmbH, Dresden, Germany. Simcenter Amesim from Siemens PLM Software SystemModeler from Wolfram Research, Sweden CATIA Systems Engineering Dymola from Dassault from Dassault Systèmes Systèmes Altair Activate from Altair OPTIMICA Compiler solidThinking Toolkit from Modelon AB ABB OPTIMAX PowerFit Twin Builder MapleSim from JModelica from from ABB Group from ANSYS Waterloo Maple Modelon with academia Application Tool Modelica Tutorial Modelica Users’ Meet India, 2018 Keynote: Dr Peter Fritzson Presenters from Professor and Research Director of the Programming Altair India Private Limited Environment Laboratory at Linköping University BMSCE Bangalore Director of the Open Source Modelica Consortium Dymola Director of the MODPROD center for model-based IISc Bangalore product development IIT Bombay Vice chairman of the Modelica Association ModeliCon InfoTech LLP Modelon Engineering Private Limited Tutorial Agenda SASTRA Deemed University -
Automatic Calibrations Generation for Powertrain Controllers Using Maplesim
2018-01-1458 Automatic Calibrations Generation for Powertrain Controllers Using MapleSim Abstract development costs. Furthermore, MapleSim’s symbolic capabilities, including symbolic simplification and symbolic optimization of gen- Modern powertrains are highly complex systems whose development erated code, enable complex models to be simulated at speeds that al- requires careful tuning of hundreds of parameters, called calibrations. low real-time simulation for Hardware-In-the-Loop testing. The tool These calibrations determine essential vehicle attributes such as per- has been used in different industries, including safety critical indus- formance, dynamics, fuel consumption, emissions, noise, vibrations, tries [3]. Furthermore, the tool has been applied in powertrain model- harshness, etc. This paper presents a methodology for automatic gen- ing and analysis [4, 5, 6]. For example, [4] uses MapleSim/Maple for eration of calibrations for a powertrain-abstraction software module modeling and rapid prototyping of a powertrain. within the powertrain software of hybrid electric vehicles. This mod- ule hides the underlying powertrain architecture from the remaining In model-based development, the calibration process makes up a sig- powertrain software. The module encodes the powertrain’s torque- nificant portion of overall development efforts [7]. The calibration speed equations as calibrations. The methodology commences with process deals with tuning system parameters—calibrations—to meet modeling the powertrain in MapleSim, a multi-domain modeling and multiple requirements. Within the powertrain controls, calibrations simulation tool. Then, the underlying mathematical representation of typically reflect parameters (e.g., filters’ parameters, delays, thresh- the modeled powertrain is generated from the MapleSim model using olds, etc.) that are used for fine-tuning performance, fuel-efficiency, Maple, MapleSim’s symbolic engine. -
Lecture #4: Simulation of Hybrid Systems
Embedded Control Systems Lecture 4 – Spring 2018 Knut Åkesson Modelling of Physcial Systems Model knowledge is stored in books and human minds which computers cannot access “The change of motion is proportional to the motive force impressed “ – Newton Newtons second law of motion: F=m*a Slide from: Open Source Modelica Consortium, Copyright © Equation Based Modelling • Equations were used in the third millennium B.C. • Equality sign was introduced by Robert Recorde in 1557 Newton still wrote text (Principia, vol. 1, 1686) “The change of motion is proportional to the motive force impressed ” Programming languages usually do not allow equations! Slide from: Open Source Modelica Consortium, Copyright © Languages for Equation-based Modelling of Physcial Systems Two widely used tools/languages based on the same ideas Modelica + Open standard + Supported by many different vendors, including open source implementations + Many existing libraries + A plant model in Modelica can be imported into Simulink - Matlab is often used for the control design History: The Modelica design effort was initiated in September 1996 by Hilding Elmqvist from Lund, Sweden. Simscape + Easy integration in the Mathworks tool chain (Simulink/Stateflow/Simscape) - Closed implementation What is Modelica A language for modeling of complex cyber-physical systems • Robotics • Automotive • Aircrafts • Satellites • Power plants • Systems biology Slide from: Open Source Modelica Consortium, Copyright © What is Modelica A language for modeling of complex cyber-physical systems -
Exploitable Results by Third Parties 11004 MODRIO
Exploitable Results by Third Parties 11004 MODRIO Project details Project leader: Daniel Bouskela Email: [email protected] Website: https://www.modelica.org/external-projects/modrio 4 Exploitable Results by Third Parties 11004 MODRIO Name: O3PRM editor Input(s): Main feature(s) Output(s): . PRM (Probabilistic . Syntactic editor for O3PRM . Probability Relational Model) language distributions of written in the . Bayesian inference engine the requested O3PRM modeling variables language . Observations and requests on some variables of the PRM Unique Selling . Supports object oriented PRM Proposition(s): . Will soon be connected to Modelica models . Performance of inference algorithms . Free, open source . Web site including documentation, ready to use executable, source code: http://o3prm.lip6.fr Integration . Uses the Agrum open source library for inference constraint(s): Intended user(s): . In a first step: researchers interested in creating diagnosis applications. Then the users of such applications in the industry. Provider: . Lip6 (Laboratoire d’informatique de Paris 6) and EDF Contact point: . Marc Bouissou (EDF R&D) Condition(s) for . This software is currently under a GPL license reuse: Latest update: 19/04/2016 5 Exploitable Results by Third Parties 11004 MODRIO Name: SKELBO Figaro library Input(s): Main feature(s) Output(s): . Thermohydraulic . This library can be exploited . Fault tree(s) system architecture by the Figaro processor in order to generate a fault tree describing the causes of a thermohydraulic system failure . Describes failure modes (on demand and in function) of the most common thermohydraulic components, with the way they can propagate in a system . Includes 29 classes of objects that can be used to describe a system . -
A Modelica Power System Library for Phasor Time-Domain Simulation
2013 4th IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), October 6-9, Copenhagen 1 A Modelica Power System Library for Phasor Time-Domain Simulation T. Bogodorova, Student Member, IEEE, M. Sabate, G. Leon,´ L. Vanfretti, Member, IEEE, M. Halat, J. B. Heyberger, and P. Panciatici, Member, IEEE Abstract— Power system phasor time-domain simulation is the FMI Toolbox; while for Mathematica, SystemModeler often carried out through domain specific tools such as Eurostag, links Modelica models directly with the computation kernel. PSS/E, and others. While these tools are efficient, their individual The aims of this article is to demonstrate the value of sub-component models and solvers cannot be accessed by the users for modification. One of the main goals of the FP7 iTesla Modelica as a convenient language for modeling of complex project [1] is to perform model validation, for which, a modelling physical systems containing electric power subcomponents, and simulation environment that provides model transparency to show results of software-to-software validations in order and extensibility is necessary. 1 To this end, a power system to demonstrate the capability of Modelica for supporting library has been built using the Modelica language. This article phasor time-domain power system models, and to illustrate describes the Power Systems library, and the software-to-software validation carried out for the implemented component as well as how power system Modelica models can be shared across the validation of small-scale power system models constructed different simulation platforms. In addition, several aspects of using different library components. Simulations from the Mo- using Modelica for simulation of complex power systems are delica models are compared with their Eurostag equivalents. -
Component-Oriented Acausal Modeling of the Dynamical Systems in Python Language on the Example of the Model of the Sucker Rod String
Component-oriented acausal modeling of the dynamical systems in Python language on the example of the model of the sucker rod string Volodymyr B. Kopei, Oleh R. Onysko and Vitalii G. Panchuk Department of Computerized Mechanical Engineering, Ivano-Frankivsk National Technical University of Oil and Gas, Ivano-Frankivsk, Ukraine ABSTRACT Typically, component-oriented acausal hybrid modeling of complex dynamic systems is implemented by specialized modeling languages. A well-known example is the Modelica language. The specialized nature, complexity of implementation and learning of such languages somewhat limits their development and wide use by developers who know only general-purpose languages. The paper suggests the principle of developing simple to understand and modify Modelica-like system based on the general-purpose programming language Python. The principle consists in: (1) Python classes are used to describe components and their systems, (2) declarative symbolic tools SymPy are used to describe components behavior by difference or differential equations, (3) the solution procedure uses a function initially created using the SymPy lambdify function and computes unknown values in the current step using known values from the previous step, (4) Python imperative constructs are used for simple events handling, (5) external solvers of differential-algebraic equations can optionally be applied via the Assimulo interface, (6) SymPy package allows to arbitrarily manipulate model equations, generate code and solve some equations symbolically. The basic set of mechanical components (1D translational “mass”, “spring-damper” and “force”) is developed. The models of a sucker rods string are developed and simulated using these components. The comparison of results of the sucker rod string simulations with practical dynamometer cards and Submitted 22 March 2019 Accepted 24 September 2019 Modelica results verify the adequacy of the models. -
5Th International Workshop on Equation-Based Object-Oriented Modeling Languages and Tools
Proceedings of the 5th International Workshop on Equation-Based Object-Oriented Modeling Languages and Tools University of Nottingham, UK, 19 April, 2013 Editor Henrik Nilsson EOOLT Supported by The Functional Programming Laboratory, School of Computer Science, University of Nottingham 2013 ISSN: 1650-3686 5th International Workshop on Equation-Based Object-Oriented Modeling Languages and Tools 19 April 2013, Nottingham, UK Proceedings Edited by Henrik Nilsson Copyright The publishers will keep this document online on the Internet — or its possible replacement — starting from the date of publication barring exceptional circumstances. The online availability of the document implies permanent permission for anyone to read, to down- load, or to print out single copies for his/her own use and to use it unchanged for noncommercial re- search and educational purposes. Subsequent transfers of copyright cannot revoke this permission. All other uses of the document are conditional upon the consent of the copyright owner. The publisher has taken technical and administrative measures to assure authenticity, security and accessibility. According to intellectual property law, the author has the right to be mentioned when his/her work is accessed as described above and to be protected against infringement. For additional information about Linkping University Electronic Press and its procedures for publication and for assurance of document integrity, please refer to its www home page: http://www.ep.liu.se/. Series: Link¨oping Electronic Conference Proceedings, -
Wolfram Mathematica
Wolfram bietet mit seinen Produkten die besten Technologien für Schulen an - in verschiedenen Themenbereichen und für jedes Bildungsniveau. Sie können dabei dieselbe Technologie einsetzen, wie sie an Universitäten, in der Industrie und an Forschungsinstituten von Wirtschaftswissenschaftlern, Ingenieuren und Lehrkräften weltweit verwendet wird. Wolfram-Produkte bieten Ihnen vollkommen neue Möglichkeiten für Ihren Unterricht. Berechnungen ohne Limit Unterrichtskonzepte zum Leben erwecken Gehen Sie Probleme jeglicher Größe und Komplexität an - ob Von Naturwissenschaften bis zu Sozialkunde oder Musik in der Schule oder außerhalb - und geben Sie Ihren Schülern - Wolframs interaktive Werkzeuge sind fesselnder als ein ein Werkzeug an die Hand, mit dem anspruchsvolle Aufgaben unbewegtes Lehrbuch und machen es einfacher, schwer verlässlich gelöst werden können. verständliche Konzepte zu erforschen. Analyse von experimentellen Ergebnissen Analysieren Sie Daten mithilfe von Funktionen für Wahrschein- lichkeit und Statistik und erwecken Sie Ihre Daten zum Leben. Die beste Wahl für die Schul- und Studienzeit Wolfram-Technologien können von Schülern und Studenten aller Fächer verwendet werden, von der Grundschulklasse bis zur Promotion. Steigen Sie ein: www.additive-mathematica.de Ihre Arbeit in Lehre und Forschung neu definieren Mathematica bietet eine vollständige Umgebung für Ihre Unterrichtsvorbereitung und Forschungsarbeit, indem es ein mächtiges Rechenwerkzeug mit einem dynamischen Visualisierungstool verbindet. Eine intuitive Benutzeroberfläche -
Real-Time Simulation and HIL Testing
Real-time Simulation and HIL Testing Advanced projects require advanced tools for meeting and exceeding system-level requirements. By incorporating the most recent progress in engineering design technology, MapleSimTM offers a modern approach to physical modeling and simulation. It dramatically reduces model development and analysis time while producing fast, high-fidelity simulations. MapleSim is a “white-box” Modelica® platform, giving you complete flexibility and openness for complex multidomain models. With MapleSim, you create, analyze, and run system-level models in a fraction of the time it takes with other tools. As a result of its symbolic equation-formulation, simplification, and optimization technologies, MapleSim replaces repeated calls to computationally MapleSim dramatically reduces the computational effort of simulation. With MapleSim, you do not expensive function calls and algebraic terms with have to choose between model fidelity and real-time performance. precomputed single values, prior to the iteration. Real-time Simulation and HIL Testing MapleSim produces high-performance, royalty-free code suitable even from inside iteration loops, MapleSim can decrease the number of for complex real-time simulations, including hardware-in-the-loop (HIL) calculations for a single common subexpression from thousands to one applications. With MapleSim, you do not have to choose between model in a typical application. fidelity and real-time performance. • Available code generation targets, using MapleSim or MapleSim with a connectivity -
Numerical Analysis, Modelling and Simulation
Numerical Analysis, Modelling and Simulation Griffin Cook Numerical Analysis, Modelling and Simulation Numerical Analysis, Modelling and Simulation Edited by Griffin Cook Numerical Analysis, Modelling and Simulation Edited by Griffin Cook ISBN: 978-1-9789-1530-5 © 2018 Library Press Published by Library Press, 5 Penn Plaza, 19th Floor, New York, NY 10001, USA Cataloging-in-Publication Data Numerical analysis, modelling and simulation / edited by Griffin Cook. p. cm. Includes bibliographical references and index. ISBN 978-1-9789-1530-5 1. Numerical analysis. 2. Mathematical models. 3. Simulation methods. I. Cook, Griffin. QA297 .N86 2018 518--dc23 This book contains information obtained from authentic and highly regarded sources. All chapters are published with permission under the Creative Commons Attribution Share Alike License or equivalent. A wide variety of references are listed. Permissions and sources are indicated; for detailed attributions, please refer to the permissions page. Reasonable efforts have been made to publish reliable data and information, but the authors, editors and publisher cannot assume any responsibility for the validity of all materials or the consequences of their use. Copyright of this ebook is with Library Press, rights acquired from the original print publisher, Larsen and Keller Education. Trademark Notice: All trademarks used herein are the property of their respective owners. The use of any trademark in this text does not vest in the author or publisher any trademark ownership rights in such trademarks, nor does the use of such trademarks imply any affiliation with or endorsement of this book by such owners. The publisher’s policy is to use permanent paper from mills that operate a sustainable forestry policy. -
Unambiguous Power System Dynamic Modeling and Simulation Using Modelica Tools L
Unambiguous Power System Dynamic Modeling and Simulation using Modelica Tools L. Vanfretti, Member, IEEE, W. Li, Student Member, IEEE, T. Bogodorova, Student Member, IEEE, and P. Panciatici, Member, IEEE Abstract—Dynamic modeling and time-domain simulation for modeling of each component and to validate a power system power systems is inconsistent across different simulation plat- model as a whole. Explicitly exchanging the equations of forms, which makes it difficult for engineers to consistently the model may aid in achieving consistency across different exchange models and assess model quality. Therefore, there is a clear need for unambiguous dynamic model exchange. simulation platforms. In this article, a possible solution is proposed by using open A possible solution can be found by using an open model- modeling equation-based Modelica tools. The nature of the ing equation-based approach. Modelica is an object oriented Modelica modeling language supports model exchange at the language developed for equation-based modeling of physical “equation-level”, this allows for unambiguous model exchange systems and its components [3], [4]. There are several ad- between different Modelica-based simulation tools without loss of information about the model. An example of power system vantages for using equation-based modeling and simulation dynamic model exchange between two Modelica-based software approaches. First of all, the models of each component in Scilab/Xcos and Dymola is presented. In addition, common issues such software type are open for modification. They allow for related to simulation, including the extended modeling of complex straightforward implementation of new elements and libraries controls, the capabilities of the DAE solvers and initialization in order to simulate the behavior of each component and problems are discussed.