Dymola User's Manual

Total Page:16

File Type:pdf, Size:1020Kb

Dymola User's Manual >241& >3&2.(= 4)*1.3,=&'47&947> 8*78=&3:&1 Dymola Dynamic Modeling Laboratory User’s Manual Version 5.3a © Copyright 1992-2004 by Dynasim AB. All rights reserved. Dymola is a trademark of Dynasim AB. Dymola is a registered trademark of Dynasim AB in Sweden. Modelica is a registered trademark of the Modelica Association. Dynasim AB Research Park Ideon SE-223 70 Lund Sweden E-mail: [email protected] URL: http://www.Dynasim.com Phone: +46 46 2862500 Fax: +46 46 2862501 Contents What is Dymola? . 13 Features of Dymola . 13 Architecture of Dymola . 14 Basic Operations . 14 Simulating an existing model . 15 Building a model . 16 Features of Modelica . 19 Background . 19 Equations and reuse . 19 Modelica history . 20 Getting started with Dymola . 23 Introduction . 23 Simulating a model — industrial robot . 24 Simulation . 32 Other demo examples . 33 Solving a non-linear differential equation . 33 Simulation . 36 Improving the model . 40 5 Using the Modelica Standard Library . 42 The Modelica Standard Library . 43 Creating a library for components . 48 Creating a model for an electric DC motor . 49 Testing the model . 56 Creating a model for the motor drive . 59 Parameter expressions . 61 Building a mechanical model . 65 Introduction to Modelica . 73 Modelica basics . 73 Variables . 74 Connectors and connections . 75 Partial models and inheritance . 75 Acausal modeling . 76 Background . 76 Differential-algebraic equations . 78 Advanced modeling features . 79 Vectors, matrices and arrays . 79 Class parameters . 79 Algorithms and functions . 81 Hybrid modeling in Modelica . 81 Synchronous equations . 82 Relation triggered events . 85 Variable structure systems . 86 Initialization of models . 90 Basics . 90 Continuous time problems . 91 Parameter values . 95 Discrete and hybrid problems . 96 Example: Initialization of discrete controllers . 97 Standard libraries . 100 Summary . 101 References . 101 Developing a model . 107 General concepts . 107 Window types . 107 Class layers . 111 6 Class documentation . 112 Coordinate system . 113 Model editing . 114 Basic operations . 114 Components and connectors . 115 Connections . 121 Creating graphical objects . 123 Changing graphical attributes . 124 Modelica text . 125 Documentation . 126 HTML documentation ..
Recommended publications
  • Applying Reinforcement Learning to Modelica Models
    ModelicaGym: Applying Reinforcement Learning to Modelica Models Oleh Lukianykhin∗ Tetiana Bogodorova [email protected] [email protected] The Machine Learning Lab, Ukrainian Catholic University Lviv, Ukraine Figure 1: A high-level overview of a considered pipeline and place of the presented toolbox in it. ABSTRACT CCS CONCEPTS This paper presents ModelicaGym toolbox that was developed • Theory of computation → Reinforcement learning; • Soft- to employ Reinforcement Learning (RL) for solving optimization ware and its engineering → Integration frameworks; System and control tasks in Modelica models. The developed tool allows modeling languages; • Computing methodologies → Model de- connecting models using Functional Mock-up Interface (FMI) to velopment and analysis. OpenAI Gym toolkit in order to exploit Modelica equation-based modeling and co-simulation together with RL algorithms as a func- KEYWORDS tionality of the tools correspondingly. Thus, ModelicaGym facilit- Cart Pole, FMI, JModelica.org, Modelica, model integration, Open ates fast and convenient development of RL algorithms and their AI Gym, OpenModelica, Python, reinforcement learning comparison when solving optimal control problem for Modelica dynamic models. Inheritance structure of ModelicaGym toolbox’s ACM Reference Format: Oleh Lukianykhin and Tetiana Bogodorova. 2019. ModelicaGym: Applying classes and the implemented methods are discussed in details. The Reinforcement Learning to Modelica Models. In EOOLT 2019: 9th Interna- toolbox functionality validation is performed on Cart-Pole balan- tional Workshop on Equation-Based Object-Oriented Modeling Languages and arXiv:1909.08604v1 [cs.SE] 18 Sep 2019 cing problem. This includes physical system model description and Tools, November 04–05, 2019, Berlin, DE. ACM, New York, NY, USA, 10 pages. its integration using the toolbox, experiments on selection and in- https://doi.org/10.1145/nnnnnnn.nnnnnnn fluence of the model parameters (i.e.
    [Show full text]
  • Optimation Optimizing Process Control with Dymola
    DS PLM SUCCESS STORY Optimation Optimizing process control with Dymola Overview Challenge Optimizing manufacturing processes define the strategies to run the mill or the Optimation needed to provide its Sweden’s Optimation helps companies to power plant at an optimum level and use customers with solutions that define optimize their manufacturing processes via simulation to test those strategies before they the optimal process control strategy for their production processes its expertise in control technology, dynamic are implemented in the real world.” simulation, and production processes. Solution Optimized process control can contribute to With Dymola, Optimation produces simulation Optimation uses Dymola to energy savings, better product quality, and results that mimic reality enabling its dynamically simulate the way a increased output. On the contrary, incorrectly customers to implement the most optimum controller should function for maximum structured and insufficiently configured configuration from the beginning. “I suppose operating capacity control systems lead to production downtime, we can say that we are control architects - we idleness or inefficiencies. Customers that turn define the optimum strategy and we create a Benefits to Optimation for process control optimization roadmap so that programmers have precise Thanks to Dymola, Optimation’s customers have a precise idea of the come from a variety of disciplines that include instructions on how to program a control way a process controller should be pulp mills, power plants, mining and steel. system,” said Eriksson. programmed before proceeding with physical modifications or installations Optimum configuration with Dymola A plant is an ensemble of hydraulic, Optimation uses Dymola, Dassault Systèmes mechanical, electrical systems. This is why multi-engineering modeling and simulation Optimation adopts a broad approach when solutions based on the open Modelica asked to optimize an existing plant.
    [Show full text]
  • Dassault Systèmes Products Lines Releases Support Life Cycle Dates for Licensed Program
    Dassault Systèmes Products Lines Releases Support Life Cycle Dates For licensed program © Dassault Systèmes | Confidential Information | 5/23/14 | ref.: 3DS_Document_2014 ref.: Information | | 5/23/14 © Dassault | Confidential Systèmes 3DS.COM Applicable as of - 9/13/2019 Dassault Systèmes - Customer Support Table of contents 1. 3DEXPERIENCE ........................................................................................................... 4 2. 3DEXCITE ..................................................................................................................... 5 3. BIOVIA ........................................................................................................................... 6 4. CATIA Composer ........................................................................................................... 8 5. CATIA V4 ....................................................................................................................... 9 6. CATIA AITAC ............................................................................................................... 10 7. DELMIA APRISO ......................................................................................................... 11 8. DELMIA ORTEMS ....................................................................................................... 12 9. DYMOLA...................................................................................................................... 13 10. ELECTRE & ELECTRE Connectors for V5 .................................................................
    [Show full text]
  • Reusing Static Analysis Across Di Erent Domain-Specific Languages
    Reusing Static Analysis across Dierent Domain-Specific Languages using Reference Attribute Grammars Johannes Meya, Thomas Kühnb, René Schönea, and Uwe Aßmanna a Technische Universität Dresden, Germany b Karlsruhe Institute of Technology, Germany Abstract Context: Domain-specific languages (DSLs) enable domain experts to specify tasks and problems themselves, while enabling static analysis to elucidate issues in the modelled domain early. Although language work- benches have simplified the design of DSLs and extensions to general purpose languages, static analyses must still be implemented manually. Inquiry: Moreover, static analyses, e.g., complexity metrics, dependency analysis, and declaration-use analy- sis, are usually domain-dependent and cannot be easily reused. Therefore, transferring existing static analyses to another DSL incurs a huge implementation overhead. However, this overhead is not always intrinsically necessary: in many cases, while the concepts of the DSL on which a static analysis is performed are domain- specific, the underlying algorithm employed in the analysis is actually domain-independent and thus can be reused in principle, depending on how it is specified. While current approaches either implement static anal- yses internally or with an external Visitor, the implementation is tied to the language’s grammar and cannot be reused easily. Thus far, a commonly used approach that achieves reusable static analysis relies on the trans- formation into an intermediate representation upon which the analysis is performed. This, however, entails a considerable additional implementation effort. Approach: To remedy this, it has been proposed to map the necessary domain-specific concepts to the algo- rithm’s domain-independent data structures, yet without a practical implementation and the demonstration of reuse.
    [Show full text]
  • Introduction to Dymola
    DYMOLA AND MODELICA Course overview DAY 1 Dymola and Modelica I • Introduction Dymola, Modelica, Modelon • Lecture 1 Overview of Dymola and Physical modeling . Workshop 1 Workflow of modeling physical systems in Dymola • Lecture 2 Simulation and post-processing with Dymola . Workshop 2 Simulating and analyzing a physical system • Lecture 3 Configure system models . Workshop 3 Creating a reconfigurable system DAY 2 Dymola and Modelica I • Lecture 4 Modelica I – Writing Modelica models . Workshop 4a Cauer low pass filter using Electric Library . Workshop 4b A moving coil using Magnetic, Electric and Translational mechanics libraries . Workshop 4c Temperature control using Heat transfer Library • Lecture 5 Understanding equation-based modeling . Workshop 5 Defining boundary conditions • Lecture 6 Trouble shooting and common pitfalls . Workshop 6 Common pitfalls DAY 3 Dymola and Modelica II • Lecture 7 Modelica II – Advanced features . Workshop 7 Implementing a solar collector • Lecture 8 Working with the Modelica Standard Library . Workshop 8a Lamp logic using StateGraph II . Workshop 8b Suspension linkage using MultiBody mechanics • Lecture 9 Hybrid modeling . Workshop 9a Hybrid examples . Workshop 9b Hammer impact model . Workshop 9c Designing a thermostat valve DAY 4 Dymola and Modelica II • Lecture 10 Efficient and reconfigurable modeling . Workshop 10 Creating a system architecture based on templates and interfaces • Lecture 11 Model variants and data management . Workshop 11 Creating a data architecture and adaptive parameter interfaces • Lecture 12 FMI technology . Workshop 12a Import and Export FMUs in Dymola . Workshop 12b FMI with Excel . Workshop 12c FMI with Simulink DAY 5 Dymola and Modelica II • Lecture 13 Workflow automation and scripting . Workshop 13 Automated sensitivity analysis • Lecture 14 Dymola code with other tools .
    [Show full text]
  • Simulia Community News
    SIMULIA COMMUNITY NEWS #08 October 2014 THE POWER OF THE PORTFOLIO COVER STORY SIMULATION HELPS UPGRADE LONDON TUNNELS in this Issue October 2014 3 Welcome Letter Scott Berkey, Chief Executive Officer, SIMULIA 4 Portfolio Update Latest SIMULIA Portfolio Releases Deliver Powerful, Advanced Simulation Functionality to Users 8 Strategy Overview DR. SAUER How to Stay at the Top of Your Game in the Fast-Evolving 10 World of Simulation 10 Cover Story Dr. Sauer and Partners Helps Upgrade London Underground Station 13 News SIMPACK Joins the Dassault Systèmes Family 14 Case Study Stadler Rail Simulates Train Safety with FEA 17 Alliances Topology and Shape Optimization Using the Tosca-Ansa Environment 18 Case Study Fine-tuning the Anatomy of Car-Seat Comfort STADLER 20 Academic Update 14 Oil & Gas Subsurface Innovation with Multiphysics Simulations 22 Tips & Tricks Optimization Module Within Abaqus/CAE Contributors: Dr. Alois Starlinger (Stadler Rail), Dr. Alexander Siefert (Wölfel Group), Jeremy Brown and Randi Jean Walters (Stanford University), Parker Group On the Cover: Ali Nasekhian, Dr. techn., M.Sc. Senior Tunnel Engineer, Dr. Sauer and Partners Cover photo by Roger Brown Photography WÖLFEL 18 SIMULIA Community News is published by Dassault Systèmes Simulia Corp., Rising Sun Mills, 166 Valley Street, Providence, RI 02909-2499, Tel. +1 401 276 4400, Fax. +1 401 276 4408, [email protected], www.3ds.com/simulia Editor: Tad Clarke Associate Editor: Kristina Hines Graphic Designer: Todd Sabelli ©2014 Dassault Systèmes. All rights reserved. 3DEXPERIENCE, the Compass icon and the 3DS logo, CATIA, SOLIDWORKS, ENOVIA, DELMIA, SIMULIA, GEOVIA, EXALEAD, 3D VIA, BIOVIA, NETVIBES, and 3DXCITE are commercial trademarks or registered trademarks of Dassault Systèmes or its subsidiaries in the U.S.
    [Show full text]
  • 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
    [Show full text]
  • Running an Abaqus Job on the Cloud
    SIMULIA COMMUNITY NEWS #18 December 2017 EMPOWERED BY THE CLOUD COVER STORY DIGITAL ORTHOPAEDICS 6 | DigitalAdmedes Orthopaedics In this Issue December 2017 3 Welcome Letter Bruce Engelmann, SIMULIA R&D VP & CTO 4 Future Outlook Accessing the Latest Simulation Technologies from SIMULIA on the Cloud 6 Cover Story Digital Orthopaedics: Feet in the Cloud 10 | The Living Heart 9 Solution Highlight A Simulation Tool that Connects the Dots on the 3DEXPERIENCE Platform 10 The Living Heart on the Cloud Growing Awareness of the Value of Modeling and Simulation for Life Sciences 12 Solution Update: Virtual Human Modeling on The Cloud Advance Biomedical Engineering Through Realistic Simulation 14 Tech Tip Running an Abaqus Job on the Cloud 12 | Virtual Human 15 Alliances Using Your SIMULIA Portfolio License on the Cloud Modeling 17 Vertical Applications Democratize Analysis using Simulation Vertical Applications 18 3DEXPERIENCE for Academia + SIMULIA Bringing Innovation and Industry into the Classroom Contributors: Parker Group & Digital Orthopaedics On the Cover: Mr. Eric Halioua, Dr. Thibaut Leemrijse, Dr. Bruno Ferré, Digital Orthopaedics Photo by: Couloir 3, Paris, France 18 | Academic SIMULIA Community News is published by Dassault Systèmes Simulia Corp., 1301 Atwood Avenue, Suite 101W, Johnston, RI 02919, Tel. +1 401 531 5000, Fax. +1 401 531 5005, [email protected], www.3ds.com/simulia Editor: Tad Clarke Associate Editor: Kristina Hines Graphic Designer: Todd Sabelli ©2017 Dassault Systèmes. All rights reserved. 3DEXPERIENCE®, the Compass icon and the 3DS logo, CATIA, SOLIDWORKS, ENOVIA, DELMIA, SIMULIA, GEOVIA, EXALEAD, 3D VIA, 3DSWYM, BIOVIA, NETVIBES and 3DEXCITE are commercial trademarks or registered trademarks of Dassault Systèmes or its subsidiaries in the U.S.
    [Show full text]
  • DS Reports 2008 First Quarter Software Revenue Growth Above 14% in Constant Currencies
    DS Reports 2008 First Quarter Software Revenue Growth Above 14% in Constant Currencies Paris, France, April 29, 2008 ─ Dassault Systèmes (DS) (Nasdaq: DASTY; Euronext Paris: #13065, DSY.PA) reported U.S. GAAP unaudited financial results for the first quarter ended March 31, 2008. Summary Financial Highlights Q1 GAAP total revenue up 12% on GAAP software revenue growth of 16%, both in constant currencies Q1 non-GAAP total revenue up 10% on non-GAAP software revenue growth of 14%, both in constant currencies Q1 EPS €0.34 on GAAP basis and €0.41 on non-GAAP basis DS reconfirms 2008 Business Outlook: reconfirms constant currencies non-GAAP software and non-GAAP total revenue growth objectives for 2008; reconfirms non-GAAP operating margin expansion objective for 2008; adjusts non-GAAP EPS growth objective for 2008 to between 6% and 10% growth solely due to US dollar weakness First Quarter 2008 Financial Summary U.S. GAAP Non-GAAP In millions of Euros, except per share data Growth Growth in cc* Growth Growth in cc* Q1 Total Revenue 307.4 6% 12% 307.9 4% 10% Q1 Software Revenue 269.1 9% 16% 269.6 8% 14% Q1 EPS 0.34 21% 0.41 5% Q1 Operating Margin 17.3% 22.8% * In constant currencies. Bernard Charlès, Dassault Systèmes President and Chief Executive Officer, commented, “Dassault Systèmes had a solid start to 2008, meeting all of our financial objectives for revenue, operating margin and earnings per share. We are seeing good dynamics in our core industries and new verticals. In particular, we had a very strong quarter for CATIA benefiting from broad-based demand among automotive and aerospace companies and good execution in our Business Transformation Channel for large accounts.
    [Show full text]
  • 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.
    [Show full text]
  • OMG Sysphs: Integrating Sysml, Simulink, Modelica And
    OMG SysPhs: Integrating SysML, Simulink, Modelica and FMI | ref.: 3DS_Document_2015 | ref.: 2/13/20 | Confidential Information | Information | Confidential Nerijus Jankevicius Systèmes CATIA | No Magic Dassault © INCOSE IW, Torrance, Jan 27, 2020 3DS.COM System Model as an Integration Framework © 2012-2014 by Sanford Friedenthal SysML as co-simulation environment 35 Reduce and standardize mappings 4 Unified Physics Domain Flowing Substance Flow rate Potential to flow Electrical Charge Current Voltage Hydraulic Volume Volumetric flow rate Pressure Rotational Angular momentum Torque Angular velocity Translational Linear momentum Force Velocity Thermal Entropy Entropy flow Temperature flow rate = amount of substance/time flow rate * potential = energy / time = power The Standard : SysPhs •SysPhS - https://www.omg.org/spec/SysPhS/1.0 • SysML Mapping to SiMulink and Modelica • SysPhS profile • SysPhS library Simulation profile Modelica vs Simulink • Modelica • SiMulink • Language is better suited for physical • Language is well-suited for control modeling (plant) algorithMs • Object oriented approach for • TransforMational seMantics of signals modeling physical and signal processing coMponents (Mechanical, electrical, etc.) • Causal seMantics (inputs -> outputs) • Causal and A-Causal seMantics • Well integrated into the “MATLAB (equations) universe” • Open standard (of the textual • Widely used in industry (standard de- language) facto) • Multi tool support (although DyMola • Many existing tool integrations is doMinant) • Code generation to
    [Show full text]
  • Modelica: Equation-Based, Object-Oriented Modelling of Physical Systems
    Chapter 3 Modelica: Equation-Based, Object-Oriented Modelling of Physical Systems Peter Fritzson Abstract The field of equation-based object-oriented modelling languages and tools continues its success and expanding usage all over the world primarily in engineering and natural sciences but also in some cases social science and economics. The main properties of such languages, of which Modelica is a prime example, are: acausal modelling with equations, multi-domain modelling capability covering several application domains, object-orientation supporting reuse of components and evolution of models, and architectural features facilitating modelling of system architectures including creation and connection of components. This enables ease of use, visual design of models with combination of lego-like predefined model building blocks, ability to define model libraries with reusable components enables. This chapter gives an introduction and overview of Modelica as the prime example of an equation-based object-oriented language. Learning Objectives After reading this chapter, we expect you to be able to: • Create Modelica models that represent the dynamic behaviour of physical components at a lumped parameter abstraction level • Employ Object-Oriented constructs (such as class specialisation, nesting and packaging) known from software languages for the reuse and management of complexity • Assemble complex physical models employing the component and connector abstractions • Understand how models are translated into differential algebraic equations for execution 3.1 Introduction Modelica is primarily a modelling language that allows specification of mathematical models of complex natural or man-made systems, e.g., for the purpose of computer simulation of dynamic systems where behaviour evolves as a function of time.
    [Show full text]