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Exploring thousands of configurations: Find the best design out of infinite variants with LMS System Synthesis

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This presentation describes how LMS Imagine.Lab System Synthesis, part of the Simcenter portfolio, can be used for multi-attribute balancing and variant analysis. It highlights the architecture driven simulation workflow thanks to its tool neutral approach, from a topology description to an heterogeneous simulation.
LMS System Synthesis is then applied on two use cases: an electric vehicle case for automotive and an aileron actuation system case for aerospace. The evaluation of the multiple configurations allows to extract the best designs (architectures, parameters) depending on the criteria of interest at a synthesis level.

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siemens.com/plm/simcenter

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Exploring thousands of configurations: Find the best design out of infinite variants with LMS System Synthesis

  1. 1. Config_1_1 Config_1_2 Config_1_3 Config_2_1 Config_2_2 Config_2_3 Exploring thousands of configurations Find the best design out of infinite variants with LMS System Synthesis Realize innovation.Unrestricted © Siemens AG 2017
  2. 2. Unrestricted © Siemens AG 2017 Page 2 Siemens PLM Software Agenda • Overview of LMS System Synthesis • Multi-attribute balancing and variant analysis • Systems-driven product development (SDPD) • Applications • Going further • Definitions Best range = 206 km Best efficiency = 0,1125kW/ km Best power & Performance ratings Config_1_1 Config_1_2 Config_1_3
  3. 3. Unrestricted © Siemens AG 2017 Page 3 Siemens PLM Software Engineering decisions intrinsically involve systems Design – Configuration – Physical behaviors – Electronics, controls and SW Complicated Segregated systems / divisible Complex Integrated systems / non-divisible • Model-based systems engineering • Integrated processes • Integrated verification • Document-based systems engineering • Isolated departments • Distributed verification System of systems Interdependent – extra-vehicle • Enterprise systems engineering • Systematic integration • Continuous verification • Multi-attribute performance Multi-attribute balancing Vehicle integration Driving Dynamics NVH and Acoustics Structure & Durability Thermal and Energy Safety Chassis and Suspension Body Powertrain E&E Full Vehicle
  4. 4. Unrestricted © Siemens AG 2017 Page 4 Siemens PLM Software Model-based systems engineering challenges Dealing with many variants 2160 configurations to be evaluated These models could be represented through 1 architecture and 48 component models Vehicle (2 platforms) Battery (3 types) … … Tires AC motor …. (2x2 each) Transmission (2 types AT/MT) Engine (5 types) Model accuracy • Concept models • Measured data • Detailed models Load cases • EU • Asia • US
  5. 5. Unrestricted © Siemens AG 2017 Page 5 Siemens PLM Software ConfigurationSimulation Architecture Deployment of system engineering Teamcenter – LMS Imagine.Lab Product suite and positioning in systems engineering Product Life Management - Teamcenter Stand Alone or PLM Plugin Functional architecture LMS Amesim Other CAE disciplines Engine Specialist Chassis Specialist Controls Specialist Transmission Specialist LMS System Synthesis Requirements Functions Logical Physical PLM platform
  6. 6. Unrestricted © Siemens AG 2017 Page 6 Siemens PLM Software LMS System Synthesis Concept of simulation factory for the sake of the simulation engineer • Few experts are creating models • Waste of time and ressource in « repetitive task » • Common background for company • Common infrastructure for a program • Design rules are enforced from expert to engineers 1-10 ~50 ~100 >100 System engineer Simulation architect Model expert Calibrator Model user
  7. 7. Unrestricted © Siemens AG 2017 Page 7 Siemens PLM Software Simcenter™ Portfolio for Predictive Engineering Analytics LMS Imagine.Lab
  8. 8. Unrestricted © Siemens AG 2017 Page 8 Siemens PLM Software Agenda • Overview of LMS System Synthesis • Multi-attribute balancing and variant analysis • Systems-driven product development (SDPD) • Applications • Going further • Definitions Best range = 206 km Best efficiency = 0,1125kW/ km Best power & Performance ratings Config_1_1 Config_1_2 Config_1_3
  9. 9. Unrestricted © Siemens AG 2017 Page 9 Siemens PLM Software Architecture-driven simulation LMS System Synthesis VCU Driver Vehicle Gearbox Electric motor Battery • Tool neutral architecture consisting of templates defining the interface contract between subsystem behavioral models • Easy integration of subsystem models from different departments • Execution of heterogenous simulation architectures LMS Amesim LMS Amesim LMS Amesim LMS Amesim
  10. 10. Unrestricted © Siemens AG 2017 Page 10 Siemens PLM Software Architecture-driven simulation LMS System Synthesis VCU Driver Vehicle Gearbox Electric motor Battery • Tool neutral architecture consisting of templates defining the interface contract between subsystem behavioral models • Easy integration of subsystem models from different departments • Execution of heterogenous simulation architectures • Enable scenario and variant management
  11. 11. Unrestricted © Siemens AG 2017 Page 11 Siemens PLM Software Architecture-driven simulation LMS System Synthesis • Tool neutral architecture consisting of templates defining the interface contract between subsystem behavioral models • Easy integration of subsystem models from different departments • Execution of heterogenous simulation architectures • Enable scenario and variant management • Perform multi-attribute balancing within one project Performance simulation architecture Comfort simulation architecture Base architecture ComfortPerformance Attributes
  12. 12. Unrestricted © Siemens AG 2017 Page 12 Siemens PLM Software Agenda • Overview of LMS System Synthesis • Multi-attribute balancing and variant analysis • Systems-driven product development (SDPD) • Applications • Going further • Definitions Best range = 206 km Best efficiency = 0,1125kW/ km Best power & Performance ratings Config_1_1 Config_1_2 Config_1_3
  13. 13. Unrestricted © Siemens AG 2017 Page 13 Siemens PLM Software Systems-driven product development (SDPD) Key challenges driving our strategy Optimize competing targets Manage product data complexity Enable systematic reuse Balance performance and quality attributes Consistency Coordination  Existing tools  Existing processes  People Integrate and coordinate engineering disciplines
  14. 14. Unrestricted © Siemens AG 2017 Page 14 Siemens PLM Software Model-based systems engineering (MBSE) workflow Unstructured process 2 System simulation I need a model Advanced engineering departments Local hard discs Local servers Stored for department need only unclassified, no meta-data, no corporate access control 1 This model should be ok. 2 This test data should be ok. Unclassified, no meta-data, no corporate access control Risk analysis - Model generation • Confusion prone • No overview • No control 1 Risk analysis - Model exchange • No standard workflow • No security • No traceability 2 Risk analysis - System simulation • Manual work • Error prone definition • Error prone traceability 3 Simulation results generation 3
  15. 15. Unrestricted © Siemens AG 2017 Page 15 Siemens PLM Software MLM Model lifecycle management Model-based systems engineering (MBSE) workflow Many challenges remain for system simulation engineers Advanced engineering departments Pool of classified models stored on one repository Model management & meta-data client 1 Search based on meta data 32 Import or create Dedicated models Execute System simulation with available models Manage system configurations for traceability System simulation Which parameter? Which models to use? Where to store optimized parameters How to track results? How to do trade- off study? What are the targets? LMS System Synthesis
  16. 16. Unrestricted © Siemens AG 2017 Page 16 Siemens PLM Software MLM Model Lifecycle Management Closed-loop performance engineering and system design Model-based systems engineering (MBSE) workflow Closed loop performance engineering and system design Advanced engineering departments Pool of classified models stored on one repository Model management & Meta-data client Search based on meta data System simulation System design Take Design decisions based on performance assessment Target Input parameter ModelResults Optimized parameter Target Input parameter Model Results Optimized parameter LMS System Synthesis
  17. 17. Unrestricted © Siemens AG 2017 Page 17 Siemens PLM Software Product Lifecycle Management Requirements Logical PhysicalFunctional LMS System Synthesis Architecture Mapping Deploy simulationAnalysis request LMS Amesim 1D CAE Data management: LMS Sysdm, Teamcenter LMS System Synthesis 15 overview LMS platform in the Product Lifecycle Management (PLM) environment Teamcenter
  18. 18. Unrestricted © Siemens AG 2017 Page 18 Siemens PLM Software Program director, interested in: • Schedule • Open engineering changes and issues • Cost of program Vehicle system architect, interested in: • Vehicle level architecture • Functional system integration • Open engineering changes and issues Product configuration specialist, interested in: • Features • Variability • Marketing Requirements engineer, interested in: • Documentation of requirements • Validation of requirements Domain architect, interested in: • Architecture of a system, like HVAC • Requirement • System functions • System behaviors Domain engineer, interested in: • Control modeling • Source code management • Calibration and configuration The product line engineering factory
  19. 19. Unrestricted © Siemens AG 2017 Page 19 Siemens PLM Software Simulation architecture • Abstract description of the system • Tool neutral • Not simulatable / no behavior information • System structure description • Model template • Connection between model template A simulation architecture is defined • From a logical architecture • By refactoring the logical architecture structuring • By adding simulation specific information • According to a simulation purpose • Fuel efficiency • … Architecture
  20. 20. Unrestricted © Siemens AG 2017 Page 20 Siemens PLM Software User A Individual users create collections associating with other « Core » collections Core set of collections User B Research program 2025 Hybrid program 2017Engine Gearbox Vehicle Electric motor Battery Central repository Re-usability of core models / project
  21. 21. Unrestricted © Siemens AG 2017 Page 21 Siemens PLM Software Agenda • Overview of LMS System Synthesis • Multi-attribute balancing and variant analysis • Systems-driven product development (SDPD) • Applications: for automotive • Going further • Definitions
  22. 22. Unrestricted © Siemens AG 2017 Page 22 Siemens PLM Software Variants evaluation NEDC WOT Loaded slope Battery #1 Battery #2 Battery #3 EM #1 EM #2 Attributes Range Efficiency Performance Continuous PowerPeak Power Scenario’s Variants Architecture 0,12 kWh/km150 km 140 km/h 50 s 80 km/hVCU Driver Vehicle Gearbox Electric motor Battery Case of the electric vehicle (EV) Overview of the whole picture
  23. 23. Unrestricted © Siemens AG 2017 Page 23 Siemens PLM Software Architecture and template definition Architecture & template definition Model instrumentation Model assembly Study & run execution Variant evaluation Architecture • Define subsystems • Define the connections / relations between subsystems Templates • Defined for each subsystem • Interface contract (i.e. Input/output definition) • Parameters and variables Architecture Templates Battery i SOC V
  24. 24. Unrestricted © Siemens AG 2017 Page 24 Siemens PLM Software Case of the electric vehicle (EV) Model instrumentation Architecture & template definition Model instrumentation Model assembly Study & run execution Variant evaluation Behavioral model connections • Complex interactions involve multiple “wires” between subsystems • No interface contract defined • Limited re-use conditions Instrumented model connections • Interface contract definition • Subsystems are connected using a single connector • Increased re-use of subsystems
  25. 25. Unrestricted © Siemens AG 2017 Page 25 Siemens PLM Software Case of the electric vehicle (EV) Model assembly Architecture & template definition Model instrumentation Model assembly Study & run execution Variant evaluation Model assembly • Select for each template an instrumented model • “Plug & play” connection of models thanks to interface contract Scenario EM Gearbox Controls Vehicle Battery Model assembly
  26. 26. Unrestricted © Siemens AG 2017 Page 26 Siemens PLM Software Case of the electric vehicle (EV) Study & run execution Architecture & template definition Model instrumentation Model assembly Study & run execution Variant evaluation Run • Defines a single simulation scenario (example: NEDC cycle) Study • Groups several runs Scenario’s Attributes NEDC WOT slopeLoaded Range Efficiency 0,12 kWh/km150 km Performance Continuous PowerPeak Power 140 km/h 50 s 80 km/h
  27. 27. Unrestricted © Siemens AG 2017 Page 27 Siemens PLM Software Case of the electric vehicle (EV) Study: running multiple scenario’s to score on different attributes LMS System Synthesis NEDC WOT Loaded slope
  28. 28. Unrestricted © Siemens AG 2017 Page 28 Siemens PLM Software Case of the electric vehicle (EV) Variants Architecture & template definition Model instrumentation Model assembly Study & run execution Variant evaluation Run • Defines a single simulation scenario (NEDC Cycle ) Study • Defined for each variant • Groups all scenario’s for this variant Battery #1 Battery #2 Battery #3 EM #1 EM #2 Variants
  29. 29. Unrestricted © Siemens AG 2017 Page 29 Siemens PLM Software Case of the electric vehicle (EV) Variants • Max Torque = 208 Nm • Efficient • Power Loss Map: EM #1 • Max Torque = 215 Nm • Less efficient • Power Loss Map: EM #2 • Energy = 16.5 kWh • Voltage = 386 V • Mass = 118 kg Battery #1 • Energy = 21.8 kWh • Voltage = 336 V • Mass = 156 kg Battery #2 • Energy = 24 kWh • Voltage = 360 V • Mass = 171 kg Battery #3 EM behavioral models impemented as FMU’s for co-simulation (FMI 2.0) Battery behavioral models impemented as seperate LMS Amesim supercomponents
  30. 30. Unrestricted © Siemens AG 2017 Page 30 Siemens PLM Software Case of the electric vehicle (EV) Configurations and evaluation Config_1_1 Config_1_2 Config_1_3 Config_2_1 Config_2_2 Config_2_3 EM #1 EM #2 Bat #1 Bat #2 Bat #3 Torque Efficiency Energy Best range = 206 km Best efficiency = 0,1125kW/ km Best power & Performance ratings Best balanced LMS System Synthesis
  31. 31. Unrestricted © Siemens AG 2017 Page 31 Siemens PLM Software Agenda • Overview of LMS System Synthesis • Multi-attribute balancing and variant analysis • Systems-driven product development (SDPD) • Applications: for aerospace • Going further • Definitions
  32. 32. Unrestricted © Siemens AG 2017 Page 32 Siemens PLM Software Sub-systems models and tools Electric aircraft Aircraft engine Environm. control system Fuel systems Engine equipment Flight control Landing gear LMS Imagine.Lab platform LMS Amesim System model – Synthesis and analysisScenarios Performance
  33. 33. Unrestricted © Siemens AG 2017 Page 33 Siemens PLM Software LMS Imagine.Lab platform LMS System Synthesis System model – Synthesis and analysisScenarios Performance Manage and execute multiple scenario’s. Ensure integration of behavioral models created in different tools by different departments. Manage and evaluate multiple variants. Enable multi- attribute balancing. Template Scenario 1 Scenario 2 Scenario 3 … Attribute 1 Attribute 2 Attribute 3 … Variant 1 Variant 2 Variant 3 …
  34. 34. Unrestricted © Siemens AG 2017 Page 34 Siemens PLM Software Application case: aileron actuation system • Actuation system architecture consisting of templates defining the Interface contract between subsystem behavioral models • Easy integration of subsystem models from different departments • Execution of heterogenous simulation architectures ActuationController Body Propulsion Controls Motor Aileron EHA LMS AmesimLMS Amesim
  35. 35. Unrestricted © Siemens AG 2017 Page 35 Siemens PLM Software Application case: aileron actuation system • Execute different scenario’s and ambient conditions • Analyze different variants: fluids properties, electric motor technologies, … • Analysis: pressure in the jack for different fluids Scenario 3 Motor Aileron EHA Scenario 2 Scenario 1 EHA EHA Aileron Aileron Motor Motor Different scenario’s Different fluids ActuationController Body Propulsion Jack Pressure [bar]
  36. 36. Unrestricted © Siemens AG 2017 Page 36 Siemens PLM Software Application case: aileron actuation system • Perform technology trade-off and multi-attribute balancing within one project • Compare different architectures EHA vs. EMA • Analysis: actuation force evaluation for EHA and EMA architecures Base architecture EMA architecture EHA architecture EHA EMA Technologies Actuation force [bar]
  37. 37. Unrestricted © Siemens AG 2017 Page 37 Siemens PLM Software Agenda • Overview of LMS System Synthesis • Multi-attribute balancing and variant analysis • Systems-driven product development (SDPD) • Applications • Going further • Definitions Best range = 206 km Best efficiency = 0,1125kW/ km Best power & Performance ratings Config_1_1 Config_1_2 Config_1_3
  38. 38. Unrestricted © Siemens AG 2017 Page 38 Siemens PLM Software LMS System Synthesis documentation in LMS Amehelp Concepts and examples of use LMS System Synthesis 15 online documentation comes with its user guide and tutorials to start with: • Architectures,  Configurations • Variants,  Analysis requests (Teamcenter) …
  39. 39. Unrestricted © Siemens AG 2017 Page 39 Siemens PLM Software LMS Imagine.Lab landscape Collaborative platform supporting model authoring, configuration and integration Authoring Behavioral Models LMS Amesim IT Admin E C U Vehicle Energy Management Virtual Integrated Aircraft Configuration, Integration LMS System Synthesis Siemens model & data management MLM (Model Lifecycle Management) « Service Data Bus »
  40. 40. Unrestricted © Siemens AG 2017 Page 40 Siemens PLM Software System architecture & modeling Cross domain validation Engineering domains realization of system logical architecture Closed-loop with cross-domain asset management • Parameters • Targets • Attributes • Models • Test data • Simulation results Behavior modeling/simulation Integrated detailed design Domain architecture Sensor SWC Software component architecture [component architecture model] Control SWC Actuator SWC Control SWC Control SWC Actuator SWC ECU ECU Gateway Bus Bus Sensor Actuator-L Actuator-R Electronic/electrical architecture [network architecture model] deploy Behavior [behavior models] Mechanical modular architecture [3D DMU model] Multi-domain simulation architecture [behavior simulation architecture model] DynamicsEnergy conversion Plant Control Mechanism realize Control 1D- Plant 3D- Plant CAEAnalysis Calibration Diagnostic FMEAElectronics … configure specify specify specify behaviorbehaviorbehavior use use System architectureRFL System model realize
  41. 41. Unrestricted © Siemens AG 2017 Page 41 Siemens PLM Software Agenda • Overview of LMS System Synthesis • Multi-attribute balancing and variant analysis • System-driven product development (SDPD) • Applications • Going further • Definitions Best range = 206 km Best efficiency = 0,1125kW/ km Best power & Performance ratings Config_1_1 Config_1_2 Config_1_3
  42. 42. Unrestricted © Siemens AG 2017 Page 42 Siemens PLM Software Definitions • Architecture: An architecture describes the generic structure for a simulation model. It is a blueprint for the « to be assembled » simulation model. • Attribute: A property or characteristic of one or more entities. • Base architecture: High level representation of the future model to set. It defines the topology of the system model. The base architecture is a reusable structure that allows to bridge the simulation world to the system engineering one. It describes the overall simulation structure issued from the system model. • Behavioral model: A component or subsystem object can contain multiple behavioral models, describing the component or subsystem at different levels of detail. It is possible to capture this set of behavioral models in a single reconfigurable model. This requires, however, that all behavioral models have the same interface, a condition. Base architecture Simulation architecture
  43. 43. Unrestricted © Siemens AG 2017 Page 43 Siemens PLM Software Definitions • Configuration: A configuration associates instrumented models to the system model architecture. • Exposure: The exposures are non directional numerical properties, they act as a first level of contract definition for the template. • Instrumentation: An instrumented model is a reusable artefact that links a simulation template to a behavioral model by identifying the relations between parameters, variables and ports. • Interface contract: All the ports combined are the interface of the model. The interface contract defines how the component can interact with the other components in the system, but does not contain any information about the internal behavior of the component. • Model assembly: A model assembly is the configuration of a simulation architecture with the set of appropriate instrumented model. The model assembly describes the numerical process to be realized in order to provide the results to the simulation engineer. Automotive template library Base template Base port Exposures
  44. 44. Unrestricted © Siemens AG 2017 Page 44 Siemens PLM Software Definitions • Simulation architecture: Defines the interface of the system model. The simulation architecture describes how results are to be created, what are the variables to be exchanged between the elementary pieces of the assembly. • Simulation model: A simulation model is the final artefact that allows a computer to produce the expected set of numerical values that are considered as a « result » by the simulation engineer. • Template: The templates are the elementary pieces used to describe the architectures. Stored in libraries they are reusable across different architectures and across projects in a few properties. • Variant: A variant is a specialization of a model and inherits all of its properties, unless they are defined locally. This allows a very compact modeling of models that vary only. Simulation template Behavioral model Instrumented model
  45. 45. Realize innovation. Nico VANSINA Loig ALLAIN Stéphane NEYRAT LMS System Synthesis LMS Amesim Siemens Industry Software S.A.S. Digital Factory Division Product Lifecycle Management Simulation & Test Solutions DF PL STS CAE 1D

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