Efficient Software Tools in the Renewable Energy Domain: Maple and Maplesim
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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 -
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. -
Modelica and Fmi As Enablers for Virtual Product Innovation
OPEN STANDARDS IN SIMULATION MODELICA AND FMI AS ENABLERS FOR VIRTUAL PRODUCT INNOVATION Hubertus Tummescheit Board member, Modelica Association & co-founder of Modelon 1 OVERVIEW • Background and Motivation • Introduction – why open standards matter • Innovation in Model Based Design • Modelica – the equation based modeling language • The Functional-Mockup-Interface (FMI) • Examples • What’s next: upcoming innovations • Conclusions 2 The Modelica Association • An independent non-profit organization registered in Sweden. https://www.modelica.org • Members: ▪ Tool vendors ▪ Government research organizations ▪ Service providers ▪ Power users • Two standards and four core projects: ▪ The Modelica Language ▪ The Modelica Standard Library ▪ The FMI Standard for model exchange and co-simulation ▪ The SSP project for an upcoming standard on system structure and parameterization • FMI web site: http://www.fmi-standard.org • Next Modelica Conference: May 15-17 in Prague MOTIVATION: THE COMPLEXITY ISSUE ▪ System complexity increases ▪ Required time to market decreases (most industries) ▪ Without disruptive changes, an impossible equation to solve. Source: DARPA Large part of AVM project complexity is in software! 4 WHY OPEN STANDARDS ARE NEEDED • Computer Aided Engineering is a very fragmented industry • Tools evolved domain by domain • Interoperability has been an afterthought, at best • Today’s complex systems require interoperability! • An everyday challenge for engineering design • Open standards drastically reduce the cost of creating interoperability between tools • There is an interplay with open source: open source can also increase the speed of software innovation 5 TIE YOURSELF TO STANDARDS, NOT TOOLS! 1. It will be cheaper 2. It will keep software vendors on their toes to compete on tool capability, not quality of lock-in 3. -
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. -
Building System Modeling in Modelica: a Next-Generation Open-Source Framework for Modeling, Simulation, Optimization and Operation
Building system modeling in Modelica: A next-generation open-source framework for modeling, simulation, optimization and operation Michael Wetter Simulation Research Group Building Technologies Department Energy and Environmental Technologies Division Lawrence Berkeley National Laboratory August, 2010 1 Overview What is Modelica Toolkit needs & how they are addressed by Modelica Example applications 2 What is Modelica? Object-oriented equation-based modeling language Open standard Developed since 1996 because conventional approach for modeling was inadequate for integrated engineered systems Well positioned to become de-facto open standard for modeling multi-engineering systems − ITEA2: 370 person years investment over current three years. Graphical modeling - input/output free - block-diagram - state machines - bond-graphs C code a:=2; Algorithmic code b:=2*a; executable C*der(T) = Q_flow; Acausal equations solver 0 = T - TBoundary; 3 Toolkit Needs Low barrier to entry for new users & developers − modularization − transparent (open) models − free tools & commercial support Exchange of technologies − standardized interface (models, solvers) − encapsulation Critical mass − ecosystem of diverse developers and users Support for modeling, simulation, optimization, operation 4 Modularization & Transparency connector HeatPort_a "Thermal port for 1-dim. heat transfer" Modelica.SIunits.Temperature T "Port temperature"; flow Modelica.SIunits.HeatFlowRate Q_flow "Heat flow rate (positive if flowing from outside into the component)"; end HeatPort_a; model HeatCapacitor "Lumped thermal element storing heat" parameter Modelica.SIunits.HeatCapacity C "Heat capacity"; Modelica.SIunits.Temperature T "Temperature of element"; Interfaces.HeatPort_a port; equation T = port.T; C*der(T) = port.Q_flow; end HeatCapacitor; a b connect(a.port, b.port); a.port.T = b.port.T 0 = a.port.Q_flow + b.port.Q_flow 5 Tools Commercial Free Dymola JModelica SimulationX OpenModelica LMS Imagine.Lab SCICOS AMESim MapleSim MathModelica (links to Mathematica) 6 Modelica Standard Library. -
Comparative Study of Tools for Computer Modeling and Simulation
Comparative study of tools for computer Modeling and Simulation (complex dynamical systems) Yuri Senichenkov Yuri Shornikov Vladimir Ryzhov Adi Maimun Abdul Malik Classification for educational purposes • free commercial • Windows Linux • cloud technologies campus network • student computer class-room Ideally • FREE • {WINDOS,LINUX} • licensed copy • for {cloud technology, campus network} • of PRACTICALLY IMPORTANT software • for using • in Campus Really PRACTICALLY IMPORTANT Acceptable «approximation» PRACTICALLY Acceptable «approximation» IMPORTANT Matlab Scilab Modelica OpenModelica Rand Model Designer Trial Rand Model Designer Simulink ( oriented , «causal» blocks) Trial Rand Model Designer Modelica (non-oriented, «physical» blocks) Trial Rand Model Designer Basis of Mathematical Modeling for engineers (complex dynamical systems) Variants for Single-component models a) A Tool for visual modeling b) A Mathematical package c) Mathematical package + Tool for visual modeling Technologies of Computer modeling for engineers (complex dynamical systems) Variants for multi-component models a) OpenModelica b) Trial Rand Model Designer c) Simulink + StateFlow + SimPowerSystems(?) d) SystemModeler Examples: Mathematical packages Mathematica SystemModeler Maple MapleSim MapleSim modeling and simulation software • Software – Control design: MapleSim is a physical modeling and simulation tool from Maplesoft built on a foundation of symbolic computation technology. This is a Control Engineering 2012 Engineers’ Choice honorable mention. • It efficiently handles all of the complex mathematics involved in the development of engineering models, including multidomain systems, plant modeling, and control design. MapleSim reduces model development time from months to days while producing high- fidelity, high-performance models. • www.maplesoft.com Wolfram SystemModeler • Build high-fidelity models using predefined components in an easy drag-and-drop environment. Perform numerical experiments on your models to explore and tune system behavior. -
Approaches for Modelling the Physical Behavior of Technical Systems on the Example of Wind Turbines
energies Article Approaches for Modelling the Physical Behavior of Technical Systems on the Example of Wind Turbines Ralf Stetter Department of Mechanical Engineering, Ravensburg-Weingarten University (RWU), 88250 Weingarten, Germany; [email protected] Received: 18 March 2020; Accepted: 16 April 2020; Published: 22 April 2020 Abstract: Models of technical systems are an essential means in design and product-development processes. A large share of technical systems, or at least subsystems, are directly or indirectly connected with the generation or transformation of energies. In design science, elaborated modelling approaches were developed for different levels of product concretization, for instance, requirement models and function models, which support innovation and new product-development processes, as well as for energy-generating or -transforming systems. However, on one product-concretization level, the abstract level that describes the physical behavior, research is less mature, and an overview of the approaches, their respective advantages, and the connection possibilities between them and other modelling forms is difficult to achieve. This paper proposes a novel discussion structure based on modelling perspectives and digital-engineering frameworks. In this structure, current approaches are described and illustrated on the basis of an example of a technical system, a wind turbine. The approaches were compared, and their specific advantages were elaborated. It is a central conclusion that all perspectives could contribute to holistic product modelling. Consequently, combination and integration possibilities were discussed as well. Another contribution is the derivation of future research directions in this field; these were derived both from the identification of “white spots” and the most promising modelling approaches. -
Symbolic Computation Techniques for Efficient Control Plant Modeling and HIL Simulation: the Case of Maplesim
IEEE NPSS (Toronto), UOIT, Oshawa, ON, 2-3 June, 2011 International Workshop on Real Time Measurement, Instrumentation & Control [RTMIC] Symbolic Computation Techniques for Efficient Control Plant Modeling and HIL Simulation: The Case of MapleSim Tom Lee, Maplesoft Orang Vahid, Mechanical and Mechatronic Engineering, University of Waterloo Abstract This paper describes recent developments in creating better models that still run efficiently for rapid virtual simulations, including realtime, or hardware-in-the-loop (HIL) simulations. The presented techniques have been motivated by the needs of the global automotive industries in particular. Progress in automotive model-based design (MBD), however, has faced very serious recent impediments due to increasing complexity of systems such as those required for hybrid electrical vehicle (HEV) or electric vehicle (EV) design. Such systems are naturally multi-domain and possess challenging dynamics. Conventional numerical simulation tools, many find, are unable to deliver either the necessary system fidelity, or sufficient speed in realtime. This paper describes the techniques embodied in recent simulation tools that deploy symbolic computation techniques. Symbolic approaches, as compared to conventional numeric approaches, manage inherent model equations in a natural algebraic way – i.e. the equations are both exposed and manipulable and not simply encased in compiled black boxes. With direct access to such techniques, various algebraic and computation optimization techniques can be readily applied. A case study applying these techniques for designing a radar-tracking gimbal controller, via inverse kinematic analysis is presented. Keywords: Dynamic modeling, Realtime simulation, Symbolic computation, MapleSim, Maple 1. Introduction Industry continues to seek more detailed and more efficient techniques for system simulation within control system design. -
Bachelor Thesis Integration of Modelica-Based Models with The
LehrstuhlAUT für Automatisierungstechnik Bachelor Thesis Integration of Modelica-based Models with the JModelica Platform Daniel Kraus Cabo ( 2 5 5 2 4 3 1 ) S UPERVISORS Prof. Dr.-Ing. Georg Frey Lehrstuhl für Automatisierungstechnik M.Sc. Fethi Belkhir Lehrstuhl für Automatisierungstechnik Saarbrücken 2014 B004-2014 Abstract In this thesis, an introduction to Nonlinear Model Predictive Control (NMPC) combin- ing both theory and application is presented. The basis of the NMPC is described to give a general idea how this process control strategy works. For this purpose, two test case- studies are implemented. The first one is a simple two tanks in series to get acquainted with open-source platform the JModelica.org and the Optimica extension.Finally, a more complex example, which consists of solving a start-up problem of a steam boiler using the JModelica framework, is provided. The results demonstrate the effort saved in solving an optimal control problem when using the JModelica framework in comparison to individual, case-specifically arranged solutions. I Contents 1 Introduction 1 1.1 Aim and structure of thesis . .1 1.2 Thesis Outline . .1 2 Nonlinear Model Predictive Control (NMPC) 2 2.1 Mathematical formulation of NMPC . .3 2.2 Properties of NMPC . .3 2.3 Nonlinear Constrained Optimization Algorithms . .4 2.4 Methods for dynamic optimization . .4 2.5 Direct methods . .4 2.5.1 Direct Single Shooting . .5 2.5.2 Direct collocation . .6 2.5.3 Direct Multiple Shooting . .7 2.5.4 Sequential quadratic programming (SQP) . .7 3 Open Source Tools 9 3.1 Modelica . .9 3.2 OpenModelica .