Choosing a Warehouse External Shape with the Help of New Simulation Programme
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Click to edit Master title style MINLP with Combined Interior Point and Active Set Methods Jose L. Mojica Adam D. Lewis John D. Hedengren Brigham Young University INFORM 2013, Minneapolis, MN Presentation Overview NLP Benchmarking Hock-Schittkowski Dynamic optimization Biological models Combining Interior Point and Active Set MINLP Benchmarking MacMINLP MINLP Model Predictive Control Chiller Thermal Energy Storage Unmanned Aerial Systems Future Developments Oct 9, 2013 APMonitor.com APOPT.com Brigham Young University Overview of Benchmark Testing NLP Benchmark Testing 1 1 2 3 3 min J (x, y,u) APOPT , BPOPT , IPOPT , SNOPT , MINOS x Problem characteristics: s.t. 0 f , x, y,u t Hock Schittkowski, Dynamic Opt, SBML 0 g(x, y,u) Nonlinear Programming (NLP) Differential Algebraic Equations (DAEs) 0 h(x, y,u) n m APMonitor Modeling Language x, y u MINLP Benchmark Testing min J (x, y,u, z) 1 1 2 APOPT , BPOPT , BONMIN x s.t. 0 f , x, y,u, z Problem characteristics: t MacMINLP, Industrial Test Set 0 g(x, y,u, z) Mixed Integer Nonlinear Programming (MINLP) 0 h(x, y,u, z) Mixed Integer Differential Algebraic Equations (MIDAEs) x, y n u m z m APMonitor & AMPL Modeling Language 1–APS, LLC 2–EPL, 3–SBS, Inc. Oct 9, 2013 APMonitor.com APOPT.com Brigham Young University NLP Benchmark – Summary (494) 100 90 80 APOPT+BPOPT APOPT 70 1.0 BPOPT 1.0 60 IPOPT 3.10 IPOPT 50 2.3 SNOPT Percentage (%) 6.1 40 Benchmark Results MINOS 494 Problems 5.5 30 20 10 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 Not worse than 2 times slower than -
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). -
Acoustic Simulation for Spacecraft Launch Modeling & Simulating Random Excitations
TM FFT: Solution Brief - Actran SOLUTION BRIEF Acoustic Simulation for Spacecraft Launch Modeling & Simulating Random Excitations Design Challenge Key Software Features At lift-off, payload components like satellites or antennas are exposed • Acoustic Finite Elements for cavity to intense acoustic excitations that can damage their structures. and exterior acoustics modeling • Acoustic Infinite Elements or Adaptive Excitations to the Model Perfectly Match Layers (APML) for modeling the far field anechoic condition Excitations to the model can include Diffuse Sound Field (DSF) • Structure elements library: solids, shells, or Turbulent Boundary Layer (TBL) directly on the structure, or composites, laminated structures, randomized plane waves applied to air around the spacecraft membranes, beams, springs, rigid or launch vehicle, which in turn applies the DSF to the structure. connec tions, etc. Internal air volumes and acoustic blankets can also be modeled for a • Poro-elastic element library based on the more accurate simulation. A hybrid frequency response solution is BIOT theory for modeling bulk reacting possible where modal frequency response will be solved on the structure materials and direct frequency response will be solved for the fluid and blanket, • Nastran to Actran translator (NAS2ACT) or anywhere that damping is frequency dependent. to convert Nastran structure models into Results can include acoustic quantities like Sound Power, Sound Actran models Pressure, or directivity, as well as quantities calculated on the • Import -
Progress in Dynamic Simulation of Thermal Power Plants
Progress in Energy and Combustion Science 59 (2016) 79À162 Contents lists available at ScienceDirect Progress in Energy and Combustion Science journal homepage: www.elsevier.com/locate/pecs Progress in dynamic simulation of thermal power plants FalahTagedPD15XX Alobaid*D16XX, NicolasD17XX Mertens,D18XXRalfD19XX Starkloff,D20XXThomasD21XX Lanz,D2XXChristianD23XX Heinze,D24XXBerndD25XX EppleD26XX TechnischeTagedP Universitat€ Darmstadt, Institute for Energy Systems and Technology, Otto-Berndt-Straße 2, 64287 Darmstadt, Germany ARTICLETAGEDP INFO ABSTRACTTAGEDP Article History: While the conventional design of thermal power plants is mainly focused on high process efficiency, market Received 29 March 2016 requirements increasingly target operating flexibility due to the continuing shift towards renewables. Dynamic Accepted 10 November 2016 simulation is a cost-ef0X3DX ficient tool for improving the flexibility of dispatchable power generation in transient Available online xxx operation1X3DXsuch as load changes and start-up procedures. Specific applications include the optimisation of con- trol structures, stress assessment for critical components and plant safety analysis in malfunction cases. This Keywords:TagedP work is2X3DXa comprehensive review of dynamic simulation, its development and application to various thermal Dynamic simulation power plants. The required mathematical models and various components for description the basic process, Thermal power generation Flexibility automation and electrical systems of thermal power plants are explained with the support of practical example fl Transient operation models. The underlying ow models and their fundamental assumptions are discussed, complemented by an Load changes overview of commonly used simulation codes. Relevant studies are summarised and placed in context for dif- Start-up procedures ferent thermal power plant technologies: combined-cycle power, coal-fired power, nuclear power, concen- Flow models trated solar power, geothermal power, municipal waste incineration and thermal desalination. -
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 -
Click to Edit Master Title Style
Click to edit Master title style APMonitor Modeling Language John Hedengren Brigham Young University Advanced Process Solutions, LLC http://apmonitor.com Overview of APM Software as a service accessible through: MATLAB, Python, Web-browser interface Linux / Windows / Mac OS / Android platforms Solvers 1 1 2 3 3 APOPT , BPOPT , IPOPT , SNOPT , MINOS Problem characteristics: min J (x, y,u, z) Large-scale x s.t. 0 f , x, y,u, z Nonlinear Programming (NLP) t Mixed Integer NLP (MINLP) 0 g(x, y,u, z) Multi-objective 0 h(x, y,u, z) n m m Real-time systems x, y u z Differential Algebraic Equations (DAEs) 1 – APS, LLC 2 – EPL 3 – SBS, Inc. Oct 14, 2012 APMonitor.com Advanced Process Solutions, LLC Overview of APM Vector / matrix algebra with set notation Automatic Differentiation st nd Exact 1 and 2 Derivatives Large-scale, sparse systems of equations Object-oriented access Thermo-physical properties Database of preprogrammed models Parallel processing Optimization with uncertain parameters Custom solver or model connections Oct 14, 2012 APMonitor.com Advanced Process Solutions, LLC Unique Features of APM Initialization with nonlinear presolve minJ(x, y,u) x s.t. 0 f ,x, y,u min J (x, y,u) t 0 g(x, y,u) 0h(x, y,u) x minJ(x, y,u) x s.t. 0 f ,x, y,u s.t. 0 f , x, y,u t 0 g(x, y,u) t 0 h(x, y,u) minJ(x, y,u) x s.t. 0 f ,x, y,u t 0g(x, y,u) 0h(x, y,u) 0 g(x, y,u) minJ(x, y,u) x s.t. -
Actran VI Dedicated Pre & Post-Processor for the Actran CAE Software Family
Actran VI Dedicated pre & post-processor for the Actran CAE software family Key features • Support of all Actran features for the creation and editing of Actran analyses • Support of different mesh formats Product overview such as BDF (MSC Nastran), OP2 (MSC Nastran), UNV, RST (Ansys), Dedicated pre & post-processor for the Actran CAE software family CDB (Ansys), NFF & DAT (Actran) Actran VI is the graphical user interface (GUI) specifi cally designed for the pre- and and Patran Neutral Format post-processing of all the Actran vibro- and aero-acoustic analyses. Actran VI can import • Support of different results formats a large number of different mesh formats (Nastran BDF, ANSYS RST and CDB, Actran such as OP2, UNV, NFF, RST, HDF DAT and NFF, I-DEAS UNV, PATRAN Neutral Format) into its environment and features and Punch its own meshing tool specifi cally designed for generating, modifying and improving meshes for vibro- and aero-acoustic analyses. Its numerous meshing functionalities • Reading Nastran structure analysis, include surface and volume operations such as shrink-wrap and mesh-on-mesh surface translate and enrich into Actran generation or tetrahedral volume creations. It also features several editing tools allowing vibro-acoustic analysis fast and easy improvements of the acoustic mesh. Its various pre-processing functionalities ease the creation and editing of Actran models. • Visualization of Actran specific It is easy to visualize specifi c Actran model features, such as acoustic sources, duct features modes, beam’s shape, dynamic load, different boundary conditions, infi nite elements coordinate system, etc. ActranVI can also read Nastran structure analysis and translate • Visualization of the projection the Nastran properties into Actran properties. -
ACTRAN Acoustics the Most Efficient Solution for Predicting Acoustic Radiation
PRODUCTS ACTRAN Acoustics The most efficient solution for predicting acoustic radiation. KEY FEATURES > Standard and convected acous- tics > Extraction of acoustic modes > Handling of heterogeneities such as complex flows or temperature gradients Product overview > Account for dissipation me- chanisms such as viscothermal Rich and powerful acoustic features for your simu- losses, acoustic absorption... lation needs > Direct response and modal su- perposition approaches ACTRAN Acoustics is the foundation where it brings unprecedented module of the ACTRAN family and efficiency, speed and productivity > Unique library of stable infinite is both a standalone tool and a pre- to your analysis process. ACTRAN elements for modeling anechoic requisite for advanced modules like Acoustics features seamless boundary conditions ACTRAN VibroAcoustics, ACTRAN interfaces with most FEA structural AeroAcoustics or ACTRAN TM. analysis codes like NASTRAN, > Pressure, velocity and admit- ACTRAN Acoustics contains a wide ABAQUS™ or ANSYS™. tance boundary condition set of acoustic modeling features ACTRAN Acoustics also offers making it the CAE tool of choice powerful features for analyzing > Plane, spherical and cylindrical for the simulation of a large variety sound propagation in ducts and may wave sources and excitation of of problems, from the simplest be used for designing e.g. intake ducts by incident plane waves component to the most elaborate and exhaust lines or air distribution system. The ACTRAN Acoustics systems in buildings, aircrafts and > Vibration results recovery from product relies on Free Field cars. most FEA structural analysis solvers for radiation analysis Technologies’ exclusive powerful, Among the many advanced features robust, fast and reliable acoustic available in ACTRAN Acoustics are > Direct and iterative solvers for finite and infinite element library. -
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. -
ANSYS 18: Composite Cure Simulation Fans and Heat Exchanger Since 1909 48 FOUNDRY 4.0
Newsletter Simulation Based Engineering & Sciences Year 14 n°2 Summer 2017 Improving the development of farming equipment using CAE technologies Investigate different configurations Reliability-based RDO Visualization of Oil Lubrication and increase the overall Using Polynomial Chaos Expansion in the Transfer Case and switcher performances for Aeronautics Applications the Transmission Increase the efficiency of of fans Reliable hydraulic direct drives Optimum Design of Diesel and compressors at Boldrocchi for improved performance Ship Engine Silencer BOOST The 4th Industrial revolution is upon us, otherwise known as YOUR ANSYS Industry 4.0. From the mechanization of the 1st industrial revolution in the 18th century, we have experienced the benefits of mass production (2nd revolution) and computer PRODUCTIVITY! automation (3rd revolution). However, we are now moving LASH into the 4th, Cyber-Physical systems, where physical systems have a virtual copy and encompasses areas such as the F internet of things and cloud computing. Many companies have already taken the necessary steps to support their Industry 4.0 strategy by adopting advanced data management or innovative simulation software into their design process. The advantages of Industry 4.0 to transform the foundry process is evident on page 48. It reveals how a digital transformation can provide real time data to make adjustments, allowing for improvements in production efficiently. A complete adoption of an Industry 4.0 strategy could be a real game-changer, allowing costs to be minimized and reducing time-to-market to gain competitive advantage. Where oil plays an important role and conditions of its behaviour is often inspected on a physical product, significant changes to the product design at this stage is near impossible due to the costs involved. -
Treball (1.484Mb)
Treball Final de Màster MÀSTER EN ENGINYERIA INFORMÀTICA Escola Politècnica Superior Universitat de Lleida Mòdul d’Optimització per a Recursos del Transport Adrià Vall-llaura Salas Tutors: Antonio Llubes, Josep Lluís Lérida Data: Juny 2017 Pròleg Aquest projecte s’ha desenvolupat per donar solució a un problema de l’ordre del dia d’una empresa de transports. Es basa en el disseny i implementació d’un model matemàtic que ha de permetre optimitzar i automatitzar el sistema de planificació de viatges de l’empresa. Per tal de poder implementar l’algoritme s’han hagut de crear diversos mòduls que extreuen les dades del sistema ERP, les tracten, les envien a un servei web (REST) i aquest retorna un emparellament òptim entre els vehicles de l’empresa i les ordres dels clients. La primera fase del projecte, la teòrica, ha estat llarga en comparació amb les altres. En aquesta fase s’ha estudiat l’estat de l’art en la matèria i s’han repassat molts dels models més importants relacionats amb el transport per comprendre’n les seves particularitats. Amb els conceptes ben estudiats, s’ha procedit a desenvolupar un nou model matemàtic adaptat a les necessitats de la lògica de negoci de l’empresa de transports objecte d’aquest treball. Posteriorment s’ha passat a la fase d’implementació dels mòduls. En aquesta fase m’he trobat amb diferents limitacions tecnològiques degudes a l’antiguitat de l’ERP i a l’ús del sistema operatiu Windows. També han sorgit diferents problemes de rendiment que m’han fet redissenyar l’extracció de dades de l’ERP, el càlcul de distàncies i el mòdul d’optimització. -
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