DOCSLIB.ORG

Dynardo Gmbh

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

Design of a control system model in SimulationX using calibration and optimization Dynardo GmbH 1 © Dynardo GmbH Notes • Please let your microphone muted • Use the chat window to ask questions • During short breaks we will answer your questions Supported versions • From version 4.1 optiSLang supports SimulationX since version 3.5 Design of a control system model in SimulationX 2 using calibration and optimization © Dynardo GmbH 1. Introduction 2. Process integration 3. Sensitivity 4. Optimization analysis 5. Trainings & Contact Design of a control system model in SimulationX 3 using calibration and optimization © Dynardo GmbH 1. Introduction 2. Process integration 3. Sensitivity 4. Optimization analysis 5. Trainings & Contact Design of a control system model in SimulationX 4 using calibration and optimization © Dynardo GmbH Dynardo • Founded: 2001 (Will, Bucher, CADFEM International) • More than 60 employees, offices at Weimar and Vienna • Leading technology companies Daimler, Bosch, E.ON, Nokia, Siemens, BMW are supported Software Development CAE-Consulting • Mechanical engineering • Civil engineering & Dynardo is engineering specialist for Geomechanics CAE-based sensitivity analysis, • Automotive industry optimization, robustness evaluation • Consumer goods industry and robust design optimization • Power generation Design of a control system model in SimulationX 5 using calibration and optimization © Dynardo GmbH Application of Multi-disciplinary Optimization • Virtual prototyping is an interdisciplinary process • Multidisciplinary approach requires to run different solvers in parallel and to handle different types of constraints and objectives • Arbitrary engineering software with complex non-linear analysis have to be connected • The resulting optimization problem may become very noisy, very sensitive to design changes or ill conditioned for mathematical function analysis (e.g. non-differentiable, non-convex, non-smooth) Design of a control system model in SimulationX 6 using calibration and optimization © Dynardo GmbH Excellence of optiSLang • algorithmic toolbox for • sensitivity analysis, • optimization, • robustness evaluation, • reliability analysis • robust design optimization (RDO) • complete functionality of stochastic analysis to run real world industrial applications • optiSLang advantages: • easy and reliable application, • predefined workflows, • algorithmic wizards and • robust default settings Design of a control system model in SimulationX 7 using calibration and optimization © Dynardo GmbH Example: design of a control system dynamic system • control loop consisting of a dynamic system and a controller • system transfer function should fit with a measured one from a real system • consequence is a difference between input and output signal • controller has to minimize the difference between both signals Design of a control system model in SimulationX 8 using calibration and optimization © Dynardo GmbH Step 1: calibration of the dynamic system Design parameters Responses Task • System gain • Output signal • Minimize the difference • Delay time between output signal • 2 time constants and measured reference signal SimulationX model measured reference signal Design of a control system model in SimulationX 9 using calibration and optimization © Dynardo GmbH 1. Introduction 2. Process integration 3. Sensitivity 4. Optimization analysis 5. Trainings & Contact Design of a control system model in SimulationX 10 using calibration and optimization © Dynardo GmbH Process Integration Parametric model as base for • User defined optimization (design) space • Naturally given robustness (random) space Design variables Entities that define the design space Response variables The CAE process Outputs from the Generates the system Scattering variables results according Entities that define the to the inputs robustness space Design of a control system model in SimulationX 11 using calibration and optimization © Dynardo GmbH Input and Response Variables • Scalar design variables with continuous, discrete and binary resolution and real, integer or string type • Scattering variables with continuous resolution • Scalar responses with continuous resolution • Vector responses with continuous resolution having variable length • Signal responses having variable length and several channels Design of a control system model in SimulationX 12 using calibration and optimization © Dynardo GmbH optiSLang Integrations Connection of arbitrary ASCII file based solvers Direct integrations Ansys Workbench Matlab Python Excel SimulationX Supported connections Ansys Abaqus Adams Design of a control system model in SimulationX 13 using calibration and optimization © Dynardo GmbH Step 1: calibration of the dynamic system Definition of the Input Parameters • The input parameters and its properties can be defined directly in the SimulationX integration node Design of a control system model in SimulationX 14 using calibration and optimization © Dynardo GmbH Step 1: calibration of the dynamic system Definition of the Reference Signal • The reference signal is given in an ASCII text file Design of a control system model in SimulationX 15 using calibration and optimization © Dynardo GmbH Step 1: calibration of the dynamic system Definition of the Error Measure and Responses • The SimulationX and the reference signal are compared in an ETK node • The resulting error measure is used as scalar response within the objective function Design of a control system model in SimulationX 16 using calibration and optimization © Dynardo GmbH Step 1: calibration of the dynamic system Definition of the Objective and Constraint • The objective function is defined as a minimization criterion • Constraints are not necessary Design of a control system model in SimulationX 17 using calibration and optimization © Dynardo GmbH The Integration Flow Parametric System • SimulationX node with loaded model system.isx • Text ETK node to read the reference signal from text file and to compute the signal difference Design of a control system model in SimulationX 18 using calibration and optimization © Dynardo GmbH 1. Introduction 2. Process integration 3. Sensitivity 4. Optimization analysis 5. Trainings & Contact Design of a control system model in SimulationX 19 using calibration and optimization © Dynardo GmbH The Sensitivity Flow Design of a control system model in SimulationX 20 using calibration and optimization © Dynardo GmbH Scanning the Design Space Inputs Design of Experiments Model evaluation Outputs • Uniform distribution of inputs is represented by Latin Hypercube Sampling • Minimum number of samples (variants) should represent statistical properties, cover the input space optimally and avoid clustering • For each design all responses are calculated Design of a control system model in SimulationX 21 using calibration and optimization © Dynardo GmbH Metamodel of Optimal Prognosis (MOP) • Approximation of model output by fast surrogate model • Reduction of input space to get best compromise between available information (variants) and model representation (number of inputs) • Determination of optimal approximation model • Assessment of approximation quality • Evaluation of variable sensitivities Design of a control system model in SimulationX 22 using calibration and optimization © Dynardo GmbH Step 1: calibration of the dynamic system Sensitivity with Respect to the Objective • The signal difference is mainly influenced by two parameters • Moving Least Squares approximation is a sufficient meta-model • Small values of the system gain results in strong signal deviations Design of a control system model in SimulationX 23 using calibration and optimization © Dynardo GmbH 1. Introduction 2. Process integration 3. Sensitivity 4. Optimization analysis 5. Trainings & Contact Design of a control system model in SimulationX 24 using calibration and optimization © Dynardo GmbH The Optimization Flow • Flow contains the existing sensitivity and an additional optimization • Due to the small number of design parameters, the simplex algorithm is a good choice • As start design automatically the best design of the sensitivity analysis is considered Design of a control system model in SimulationX 25 using calibration and optimization © Dynardo GmbH optiSLang Optimization Algorithms Gradient-based Gradient-free Nature inspired Methods Methods Optimization • Most efficient method if • Attractive methods for • GA/EA/PSO imitate gradients are accurate a small set of mechanisms of nature to enough continuous variables improve individuals • Consider its restrictions • Method of choice if • Method of choice if like local optima, only gradient-based fails gradient-based or continuous variables gradient-free fails and noise • Very robust against numerical noise, non- linearity, number of Start variables,… Design of a control system model in SimulationX 26 using calibration and optimization © Dynardo GmbH Decision Tree for Optimizer Selection • optiSLang automatically suggests an optimizer depending on the parameter properties, the defined criteria and user specified settings Design of a control system model in SimulationX 27 using calibration and optimization © Dynardo GmbH Step 1: calibration of the dynamic system Optimization Downhill Simplex Method • Convergence criteria fulfilled after 160 variants • Small improvement after 81 variants Design of a control system model in SimulationX 28 using calibration and optimization © Dynardo GmbH Step 1: calibration of the dynamic system Final
Recommended publications
  • ESI's Simulationx

    ESI's Simulationx

    by ESI‘s SimulationX Courses and Contents SimulationX is a registered trademark of ESI ITI GmbH Dresden. © ESI ITI GmbH, Dresden, Germany, 2019. All rights reserved. Doc. Vers. 08/2019 Preface Dear Sir or Madam, with the continuous development of SimulationX we provide you with the necessary resources for a targeted and sustain- able work in the field of system simulation. You will learn the efficient use of the software and its innovations in group and individual training courses at ESI or directly at your site. The highly practical nature of our courses and the professional Your personal contact: expertise of our tutors guarantee a quick and successful learn- ing process. We look forward to passing on our knowledge and would be happy to discuss new solutions with you. Antje Richter T + 49 (0) 351 260 50 - 120 With best regards on behalf of the ESI team, F + 49 (0) 351 260 50 - 155 [email protected] Antje Richter, Content 1 ESI ITI Academy 1.1 Introduction 07 1.2 Course Overview 08 1.3 Conditions of Participation 09 1.4 Registration 10 2 Introduction to SimulationX 2.1 Fundamentals 11 2.2 Advanced Modeling 12 2.3 Methods for Calculation and Analyses 13 3 Physical Domains 3.1 Mechanics (1D) 14 3.2 Planar Mechanics 15 3.3 Multi-Body Systems 16 3.4 Hydraulics 17 3.5 Thermal 18 3.6 Pneumatics 19 3.7 Acoustics (1D) 20 3.8 Electronics, Electromagnetics, Electromechanics 21 3.9 Electrical Power & Communications Analysis (1D) 22 3.10 Control Engineering 23 Content 4 Applications 4.1 Drive Systems 24 4.1 Drive Systems 25 4.2 Hybrid
  • Simulationx를 이용한 기계, 유체, 전기 통합 시스템 모델링 및 해석 Simulationx, Multi-Domain Simulation and Modeling Tool for the Design, Analysis, and Optimization of Complex Systems

    Simulationx를 이용한 기계, 유체, 전기 통합 시스템 모델링 및 해석 Simulationx, Multi-Domain Simulation and Modeling Tool for the Design, Analysis, and Optimization of Complex Systems

    기술해설 SimulationX를 이용한 기계, 유체, 전기 통합 시스템 모델링 및 해석 SimulationX, Multi-domain Simulation and Modeling tool for the Design, Analysis, and Optimization of Complex systems 윤영환, 장주섭 Y. H. Yoon and J. S. Jang 1. 서 론 SimulationX의 사용상 장점은 다음과 같다. 1) 각기 다른 분야의 엔지니어들이 각자의 방식대 컴퓨터의 성능 발달로 인하여 이것을 활용한 시 로 부품이나 시스템을 모델링 할 수 있고, 이를 뮬레이션의 활용도가 높아지고 있고, 그 결과 복잡 서로 공유함으로써 복합적인 시스템 전체의 해 한 시스템을 실제 시스템처럼 모사가 가능하다. 이 석을 가능하게 하여 Multi-Domain Simulation 러한 CAE(Computer Aided Engineering) 기술 활 S/W라고 한다. 용은 비용 절감 및 개발 기간의 단축이라는 점에서 2) 상호 작용 및 피드백을 포함하는 다양한 도메 매우 중요하며, 현재 많은 제품 개발에 해석 기술이 인 부품들의 상호 작용을 모델링 할 수 있다. 활용되고 있다.1-4) 3) 시스템 개발에 있어서 공통적인 모델링 및 해 SimulationX는 독일의 드레스덴(Dresden)에 본사 석 환경을 이용함으로써 모든 관계자들 간 상 를 두고 있는 ITI GmbH에서 개발한 Multi-Domain 호 이해의 기반을 다질 수 있다. 시뮬레이션 프로그램으로서, 어떤 시스템을 구성하 4) 시제품(prototype) 제작 및 테스트 기간을 단축 고 있는 부품들의 상호 작용을 해석하고 평가할 수 하고 개발 비용을 절감할 수 있다. 있는 대표적인 소프트웨어(Software, S/W)이다. 5) 실제로 측정이 어렵거나 불가능한 모델에 대한 SimulationX를 가장 많이 시용하는 업체로는 유 관찰이 가능하여 시스템 전반에 대한 이해도를 럽의 BMW, Benz와 같은 자동차산업의 선두그룹으 높여 준다. 로 독일 자동차 회사들이 왜 세계 최고의 제품을 SimulationX의 조작 화면(GUI)은 그림 1과 같이 만드는지 알 수 있을 정도로 잘 만들어진 S/W이다. 왼쪽의 각종 물리적인 모델을 포함하고 있는 라이 SimulationX는 이미 라이브러리 화 되어있는 기 브러리, 오른쪽 위에 이러한 모델들을 배치하고 서 계(1D mechanics, 3D multi-body systems), 유압 로 연결하여 시스템 전체의 회로도를 작성하는 (Hydraulics), 공압(Pneumatics), 제어(Controls), 전 Model view외 각 모델의 물리적 특성 파라미터의 기, 전자(Electric, Electronics), 자장(Magnetics), 파 표시 및 조정, 해석과 관련된 사항을 조정하는 워트레인(Power transmission 1D, 3D), 전기 기계 Model explorer로 구성되어 있다.
  • S C H U L U N G S K a T a L O G 2 0

    S C H U L U N G S K a T a L O G 2 0

    SCHULUNGSKATALOG 2021 ENGINEERING SYSTEM INTERNATIONAL GMBH www.esi-group.com DIVE INTO THE WORLD OF ZERO TESTS, ZERO PROTOTYPES, ZERO DOWNTIME Seit der Gründung im Jahre 1973 hat ESI die Vision, reale und praxisnahe Konstruk- tionsprobleme verschiedener Industriezweige zu lösen. Diese Simulationslösungen, die auf der Materialphysik basieren, erzielten 1985 eine Weltpremiere: Zum ersten Mal wurde ein virtueller Crash-Test für Volkswagen durchgeführt, der den Weg für den umfangreichen Einsatz virtueller Lösungen in Entwicklungsprozessen eb- nete und neue Sicherheitsstandards etablierte. Ein neues Paradigma, die Outcome Economy Die zunehmende Komplexität stellt alle Branchen vor Herausforderungen. Die Herstel- ler stehen vor vielen neuen Aufgaben, um die Bedürfnisse der Kunden in Hinblick auf Qualität, Zuverlässigkeit, Sicherheit und pünktlicher Lieferung zu erfüllen. Die Outcome Economy erschwert die Erfüllung der wichtigen Leistungsindikatoren, da der Erfolg an der Leistung und nicht am Produkt selbst gemessen wird. Die Industrien müssen Wach- stum erzielen bei gleichzeitiger Aufrechterhaltung der Innovation. Daher entschei- den sie sich für eine digitale Transformation, die sie zu Initiativen ohne nachgewiesene Ergebnisse führen könnte. Courtesy of Volkswagen AG 2 Transformations-Reise Die von der ESI Group angebotenen Lösungen – das Ergebnis aus 45 Jahren Erfahrung – bringen die technologische Kompetenz mit, sich effizient und zuversichtlich zu entwickeln. Als Kernstück des ESI-Geschäftsmodells ermöglicht Virtual Proto- typing seinen globalen Kunden die Herstellung, Montage und das Verhalten ih- rer Produkte in verschiedenen Umgebungen zu validieren und so ihre Kos- ten und die Markteinführungszeit zu minimieren, ohne Abstriche bei Sicher- heit und Qualität zu machen. Um diese Ziele zu erreichen, begleitet ESI seine Kunden auf dem Weg hin zu Zero Tests, Zero Prototypes und Zero Downtime.
  • NI Tools for Automated Testing

    NI Tools for Automated Testing

    Agenda ▪ Introduction to NI ▪ Real-Time Test ▪ NI Solutions for Real-Time Test ▪ SW ▪ HW ▪ Standardizing the signal path ▪ SLSC ▪ ASAM XIL Mission Statement NI equips engineers and scientists with systems that accelerate productivity, innovation, and discovery. ni.com ONE-PLATFORM APPROACH NI SERVICES AND SUPPORT THIRD-PARTY SOFTWARE THIRD-PARTY HARDWARE WEB SERVICES NI PRODUCTIVE ARDUINO PYTHON DEVELOPMENT SOFTWARE ETHERNET C USB The MathWorks, Inc. MATLAB® GPIB .NET SERIAL VHDL NI MODULAR HARDWARE LXI/VXI AND MORE AND MORE MATLAB® is a registered trademark of The MathWorks, Inc. ONE PLATFORM APPROACH NI SERVICES AND SUPPORT Community THIRD PARTY HARDWARE Support 300,000+ Online Members 700+ Field Engineers 450+ User Groups NI PRODUCTIVE 700+ Support Engineers 9,000+ Code Examples DEVELOPMENT SOFTWARE 50+ Worldwide Offices NI ECOSYSTEM Academic Add-Ons 8,000+ Universities Worldwide 400+ Software Add-Ons 5M+ Tools Network Downloads NI ECOSYSTEM Partners NI MODULAR HARDWARE Open Connectivity 1,000+ Alliance Partners THIRD PARTY SOFTWARE 10,000+ Instrument and Device Drivers Industry-Leading Technology Partners 1,000+ Sensor and Motor Drivers Flexible Software Protects Your Investments NI TestStand NI VeriStand NI DIAdem NI InsightCM Enterprise NI Multisim LabWindows/CVI Measurement Studio Third Party Software Our Customers’ Success Electronics and Industrial Machinery Aerospace and Defense Semiconductor Academic and Research Electronics and Industrial Machinery Aerospace and Defense Semiconductor Academic and Research Transportation and Automotive Wireless Heavy Equipment Energy Transportation and Automotive Wireless Heavy Equipment Energy AEROSPACE AND DEFENSE “Through the use of advanced software architecture and NI hardware, G Systems was able to provide Lockheed Martin Aeronautics with a highly-configurable, expandable system to meet current and future requirements of the F-35 VSIF.” — Michael Fortenberry, G Systems, Inc.
  • Automatic Calibrations Generation for Powertrain Controllers Using Maplesim

    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.
  • Optimus Rev. 2018.1 Introduces New Modeling Methods Boosted by Machine Learning

    Optimus Rev. 2018.1 Introduces New Modeling Methods Boosted by Machine Learning

    Optimus Rev. 2018.1 introduces new modeling methods boosted by Machine Learning. PRESS RELEASE Leuven (Belgium), November 5 - 2018 – Noesis Solutions, the developer of Optimus and id8, announces the release of Optimus Rev. 2018.1. This release introduces new modeling methods boosted by MaChine Learning, enabling engineering teams to Create aCCurate models for low and high-dimensional optimization problems faster. Many of the new updates that come with Optimus Rev. 2018.1 further enhance Design Space Exploration and Optimization Capabilities while increasing Compatibility with leading CAD/CAE solutions. This new release confirms Optimus as the industry’s preferred platform for Design Space Exploration and Engineering Optimization. Powerful modeling boosted by MaChine Learning. This new Optimus release introduces new modeling methods boosted by Machine Learning, enabling engineering teams to create accurate models for low and high-dimensional optimization problems even with a limited number of training data sets. The new Light Weight Neural Networks (LWNN), Random Forest Regression (RFR) and Relevance Vector Regression (RVR) algorithms assist the user in accurately modeling large data sets quickly. The new Best Model algorithm even assists non-expert users by automatically selecting the best-performing model for any given data set among the models available in Optimus. Enhanced Design Space Exploration and Optimization Capabilities In addition, Optimus Rev. 2018.1 brings several improvements to Optimus’ unique Adaptive DOE algorithm that learns from available data points and iteratively adds extra data samples in design space regions that matter most. This new Optimus release further extends the capabilities of NAVIRUN, the Noesis Advanced adviser, which now also supports discrete optimization problems.
  • Robustness & Reliability Analysis

    Robustness & Reliability Analysis

    DYNARDO • Robust Design Optimization Workshop • © Dynardo GmbH 2011 Robustness & Reliability Analysis 1 DYNARDO • Robust Design Optimization Workshop • © Dynardo GmbH 2011 Start Robust Design Optimization Robust Design Optimization Variance based Robustness Evaluation Sensitivity Study Probability based Single & Multi objective Robustness Evaluation, (Pareto) optimization (Reliability analysis) CAE process (FEM, CFD, MBD, Excel, Matlab, etc.) Robust Design Optimization Methodology 2 DYNARDO • Robust Design Optimization Workshop • © Dynardo GmbH 2011 What is necessary for successful implementation? 1. Introduction of realistic scatter definitions Distribution function Correlations Random fields 2. Using of reliable stochastic methodology Variance-based robustness evaluation using optimized LHS 3. Development of reliable robustness measurements Standardized post processing Significance filter Reliable variation and correlation measurements Acceptance of method/result documentation/communication! 3 DYNARDO • Robust Design Optimization Workshop • © Dynardo GmbH 2011 Definition of Uncertainties 4 DYNARDO • Robust Design Optimization Workshop • © Dynardo GmbH 2011 Uncertainties and Tolerances • Design variables Property SD/Mean % • Material, geometry, loads, constrains,… Metallic materiales, yield 15 • Manufacturing Carbon fiber rupture 17 • Operating processes (misuse) Metallic shells, buckling 14 strength • Resulting from Deterioration Bond insert, axial load 12 • … Honeycomb, tension 16 Honeycomb, shear, compression 10 Honeycomb, face
  • Lecture #4: Simulation of Hybrid Systems

    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
  • ECU-TEST Makes Automation Easy Key Features at a Glance Interfaces

    ECU-TEST Makes Automation Easy Key Features at a Glance Interfaces

    PRODUCT DATA SHEET ECU-TEST makes automation easy With ECU-TEST you can intuitively create test cases for automotive software in every development phase and run them automatically – even without any prior knowledge of test automation and programming. We have designed the tool in such a way that the test quality is kept exceptionally high at all levels, although the effort it takes to use it is extremely low. Key features at a glance Bus description: • Supports a broad range of test tools and test environments (MiL/ • ARXML (Classic Platform) 4.1.1 to R20-11 SiL/PiL/HiL/vehicle) • ARXML (Adaptive Platform) to R20-11 • Uniform and effective automation of the entire test environment • DBC • Smooth collaboration through Diff and SCM integration (GIT, • FIBEX to 4.1.1 SVN) • FIBEX for Ethernet 4.1.2 • Automation of distributed test environments • FIBEX for Diagnostic Log and Trace (DLT): • Intuitive graphical user interface Analyse non-verbose Mode • Generic test-case description • LIN Description File (LDF) Integrated trace analysis module (see TRACE-CHECK ECU description: data sheet): • ASAP2 Database (A2L) • Easy analyis specification via • Executable and Linkable Format (ELF) with DWARF 2-5 • Triggered analyses • Intel HEX • Motorola S19 • Timing diagrams • Python interface • Support for all common recording formats Supported trace formats • High reusability of analyses Signal-based trace formate: • Clear presentation of results • AS3TRACE (TraceTronic) • Transition to the interactive SignalViewer • CSV • Plots enriched with result data
  • Optislang Connects

    Optislang Connects

    Title Story // Process Integration Process Integration ABAQUS | ADAMS | COMSOL | EXCEL | FLOEFD | GT POWER | MATLAB | LS DYNA MOTOR CAD | NASTRAN | PYTHON | ROCKY | SIEMENS NX | ZEMAX ... Product Data Product Statistical Connect PLM/SDM Connect PLM/SDM Analysis TITLE STORY // PROCESS INTEGRATION Parametric Variation Analysis Calibration Sensitivity Analysis Optimization OPTISLANG CONNECTS Robustness Evaluation Metamodeling Data-based ROMs optiSLang enables the building of transparent CAE tool chains and combines them with parametric algorithms for a broader usage of CAE techniques. Fig. 1: Simulation Workfl ow Management with optiSLang One of the major challenges of today’s CAE engineers is the gineers. Also, customers are aware of how many mistakes includes interfaces to all major software tools used in the maintain complex tool chains. The modular process inte- increasing complexity of processes while results have to might occur during this process. When different variants virtual product development. For example, there are inte- gration approach of building programmable nodes provides be delivered in shorter times. At the same time, within the have to be compared for fi nding an optimal design, it can grations of CAD parametric modeling with CATIA, Siemens a very economical way to standardize and automatize CAE engineering process, multiple disciplines like NVH, thermal- lead to an unsorted crowd of data. If simulations are done NX and ProE. CAE parametric modeling is supported by link- workfl ows. There are many examples of successful projects mechanical-electrical analyses, safety evaluation, tolerance by different engineers, it can be very hard to compare the ing ANSYS, ABAQUS, ADAMS, COMSOL, FLOEFD, GT POWER, with Dynardo’s customers like Bosch, Daimler, et al.
  • Adam D. Foltz SMT North American User Conference, October 10Th, 2019

    Adam D. Foltz SMT North American User Conference, October 10Th, 2019

    Adam D. Foltz SMT North American User Conference, October 10th, 2019 Updated: 10/21/19 Interests: Solid Mechanics, Torsional Vibration, Gear Dynamics, Gear Design, Engine Dynamics, Numerical Methods, Multibody-Dynamics, FEA, Optimization ▪ Title: Improvements on the Slice Method for External Spur and Helical Gear Loaded Tooth Contact Analysis ▪ Objective: Improve the slice method, used in the LTCA of external spur and helical gears, to bridge the gap between analytical and FE methods, and more accurately capture 3D effects. ▪ Focus: External spur and helical gears (shafts, bearings, and housings are neglected, or assumed infinitely stiff). Models that practical for use in industry. ▪ Collaboration: This work uses software developed by Divergent Solutions LLC, and will use SMT MASTA for verification purposes, along with other software. Validation with experimental data will not be performed, since manufacturing errors, misalignment, bearing stiffness, and housing stiffness are neglected in the models. ▪ What is LTCA? ▪ Geometry Creation ▪ Geometry Discretization ▪ Tooth Discretization ▪ LTCA Setup ▪ Commerical Gear Software on the Market ▪ General LTCA Methods ▪ LTCA Analytical Tooth Stiffness Models ▪ Downside of the Decoupled Slice Method ▪ 3 Tier Gear Design ▪ What Separates Different Software? ▪ Improvements Implemented in Dissertation ▪ Looking Forward ▪ Loaded tooth contact analysis (LTCA) models the meshing of a loaded gear pair over the range of the mesh cycle, through discretization of the geometry of the mating gears for both stiffness and contact, to determine outputs such as contact pressure, contact pattern, transmission error, and ,directly or indirectly, bending stress. ▪ On the other hand, tooth contact analysis (TCA), models the meshing of a gear pair over the range of the mesh cycle through discretization of the geometry of the mating gears only for contact, and not for stiffness, and models an interference between the mating gears, to determine contact pattern.
  • Dynardo Gmbh

    Dynardo Gmbh

    dynamic software & engineering Software & Consulting Sensitivity Analysis Multidisciplinary Optimization Multiobjective Optimization Robustness Evaluation Reliability Analysis Parameter Identifi cation Model Calibration Dynardo GmbH optiSLang SoS ETK LEONARDO DA VINCI dynamic software & engineering Without reason, no eff ect is produced in nature; understand the reason and you will not need experience. from Leonardo da Vinci’s „Codice Atlantico“ DYNARDO GMBH The Dynardo GmbH develops software for CAE-based sensitivity analyses, robustness evaluations and Robust Design Optimizations. Additionally Dynardo offers, in cooperation with worldwide distributors, an extensive support and training program. The range of services also includes consulting and simula- Tasks, tion services in virtual product development. Ideas, Solutions History & Expertise The Dynardo GmbH was founded in 2001 as a company for Dynardo’s engineering expertise and software products software development and engineering computing services enable fast and effi cient solutions of your tasks. Through with three partners and two employees in Weimar. With close cooperation with renowned research institutions and unique features such as complex non-linear fi nite element international partners, Dynardo has an extensive network computations in geomechanics and civil engineering as well of experts to respond fl exibly to worldwide customer re- as RDO in virtual product development, Dynardo has suc- quirements. cessfully established itself in the CAE-market. The fi rst op- tiSLang version was released in 2002 and has since evolved into a leading CAE software platform. Today, Dynardo runs 3 branches in Weimar, Vienna and San Francisco to help you meet the challenges of virtual product development. Philosophy Leonardo da Vinci was not only a great artist, but also a tion enables an appropriate analysis of system properties for visionary engineer and scientist.