The document discusses FunctionalDMU, a framework for simulating the behavior of mechatronic systems directly within digital mockups (DMUs). It aims to integrate heterogeneous behavior models from different domains and tools through a co-simulation approach. Wrappers adapt the native behavior models for communication, while a master simulator coordinates the simulation. This allows behavior models to be simulated alongside 3D geometry without data conversion. The framework has been applied to examples like simulating the thermal effects of an electric motor's electronics. Benefits include earlier detection of multi-domain issues and integrated visualization of functional and geometric aspects of mechatronic designs.
CDTire: State-of-the-Art Tire Models For Full Vehicle SimulationAltair
Tire modeling is critical for full vehicle simulations because all road excitations pass through the tires to the vehicle. The accurate representation of roads and tires and their interactions is one of the central challenges in full vehicle simulation.
CDTire is a family of sophisticated tire models capable of accurately representing the dynamic behavior of tires and the road-tire interaction.
This presentation gives an overview of tire modeling methods developed at Fraunhofer and made available through CDTire. The presentation covers three topics:
(a) Tire and road models that are commercially available today and where they can be used
(b) New developments in tire and road modeling technology
(c) Fraunhofer’s special relationship with Altair to make this technology available to you and how CDTire works with MotionSolve
CADFEM Information Day: Drives and automation engineeringCADFEM Austria GmbH
CADFEM Information Day
Begin: 10:00 am
End: 5:00 pm
Fee: Free of charge
• Reception and introduction of CADFEM
• Simulation-Driven-Product-Development
• Drive engineering
- Simulation based design of electric drives
·
Example: permanently excited synchronous machine
·
Cogging torques, inductivities, start-up behavior
- Loss assessment of electric drives
·
Eddy current, iron and copper losses
·
Time and frequency coupling
- Electric machine acoustics
· Example: transient, DFT
• Electromechanical systems
- Abstraction of system description (analytics, COSIM, ROM)
- Modeling of sensor systems (incremental encoders)
- Control (speed control of a drive, PWM)
• Automation technology
- Sensors and control systems
- Interference on wires
· Determination of concentrated parameters
· Mains pollution
· Parasitic effects
Register ar http://www.cadfem.at/produkte/infoveranstaltungen/infotage/s/11885.html
CDTire: State-of-the-Art Tire Models For Full Vehicle SimulationAltair
Tire modeling is critical for full vehicle simulations because all road excitations pass through the tires to the vehicle. The accurate representation of roads and tires and their interactions is one of the central challenges in full vehicle simulation.
CDTire is a family of sophisticated tire models capable of accurately representing the dynamic behavior of tires and the road-tire interaction.
This presentation gives an overview of tire modeling methods developed at Fraunhofer and made available through CDTire. The presentation covers three topics:
(a) Tire and road models that are commercially available today and where they can be used
(b) New developments in tire and road modeling technology
(c) Fraunhofer’s special relationship with Altair to make this technology available to you and how CDTire works with MotionSolve
CADFEM Information Day: Drives and automation engineeringCADFEM Austria GmbH
CADFEM Information Day
Begin: 10:00 am
End: 5:00 pm
Fee: Free of charge
• Reception and introduction of CADFEM
• Simulation-Driven-Product-Development
• Drive engineering
- Simulation based design of electric drives
·
Example: permanently excited synchronous machine
·
Cogging torques, inductivities, start-up behavior
- Loss assessment of electric drives
·
Eddy current, iron and copper losses
·
Time and frequency coupling
- Electric machine acoustics
· Example: transient, DFT
• Electromechanical systems
- Abstraction of system description (analytics, COSIM, ROM)
- Modeling of sensor systems (incremental encoders)
- Control (speed control of a drive, PWM)
• Automation technology
- Sensors and control systems
- Interference on wires
· Determination of concentrated parameters
· Mains pollution
· Parasitic effects
Register ar http://www.cadfem.at/produkte/infoveranstaltungen/infotage/s/11885.html
SiriusCon 2015 - Breathe Life into Your Designer!melbats
You have your shiny new DSL up and running thanks to the Eclipse Modeling Technologies and you built a powerful tooling with graphical modelers, textual syntaxes or dedicated editors to support it. But how can you see what is going on when a model is executed ? Don't you need to simulate your design in some way ? Wouldn't you want to see your editors being animated directly within your modeling environment based on execution traces or simulator results?
The GEMOC Research Project designed a methodology to bring animation and execution analysis to DSLs. The companion technologies required to put this in action are small dedicated components (all open-source) at a "proof of concept" maturity level extending proven components : Sirius, Eclipse Debug, Xtend making such features within the reach of Eclipse based tooling. The general intent regarding those OSS technologies is to leverage them within different contexts and contribute them to Eclipse once proven strong enough. The method covers a large spectrum of use cases from DSLs with a straightforward execution semantic to a combination of different DSLs with concurrent execution semantic. Any tool provider can leverage both the technologies and the method to provide an executable DSL and animated graphical modelers to its users enabling simulation and debugging at an early phase of the design.
This talk presents the approach, the technologies and demonstrate it through an example: providing Eclipse Debug integration and diagram animation capabilities for Arduino Designer (EPL) : setting breakpoints, stepping forward or backward in the execution, inspecting the variables states... We will walk you through the steps required to develop such features, the choices to make and the trade-offs involved. Expects live demos with simulated blinking leds and a virtual cat robot ! This talks presents also xCapella an industrial use case onwhich the Gemoc methodology was applied.
This talks was presented at SiriusCon 2015 in collaboration with Jérôme Le Noir from Thales.
The Arduino Designer documentation is available on : https://github.com/mbats/arduino/wiki/Documentation
Provides products to commercial organizations and government projects seeking to improve their application delivery and development timelines and resource load
MLeap: Deploy Spark ML Pipelines to Production API ServersDataWorks Summit
MLeap is an open-source technology that allows Data Scientists and Engineers to deploy Spark-trained ML Pipelines and Models to a scoring engine instantly. During our presentation, we will show you how to deploy any Spark ML Pipeline, as well as custom transformers, that are trained using Spark streaming to both a cloud-based API server as well as an IoT device.
Why MLeap? Data Scientists use a myriad tools to analyze datasets, clean them and build offline models and validate their performance. The resulting scripts are thrown across the wall to Data Engineers and Architects whose job is to bring these pipelines to production. The Engineers are left with the unenviable job of not only reproducing the Data Scientists’ conclusions, but to scale the resulting pipeline both of which require a deep understanding of Data Science itself. As a result, most if not all Data Science deployments in the wild end up either too simplistic or take too long to productionize.
MLeap solves this problem for Spark users by providing serialization of ML Pipelines’ transformers to an MLeap Bundle, which is a graph-based serialization framework built on top of Protobuf 3 and JSON. In addition, MLeap also provides a highly optimized execution engine that doesn’t rely on the Spark-context, making inference blazing fast and is capable of executing one model or thousands of models in parallel.
Nads & presagis teaming to innovate in distributed simulation xxSimware
NADS and Presagis have agreed to team up to promote modeling & simulation (M&S) solutions based on both companies' commercial-off-the-shelf (COTS) software. On November 8th, representatives of the two parties signed a Marketing Agreement at NADS Headquarters to conclude the deal.
The agreement includes collaboration for marketing of COTS-based solutions for Live-Virtual-Constructive (LVC) interoperability. These solutions will be based on Presagis product lines and SIMWARE, the flagship product from NADS.
Know about the agreement between NADS and PRESAGIS and how this companies complement in tools and technologies.
You can see also the webinar made on april 17th about this presentation
http://youtu.be/WPAJ1_UfP9E
These slides were presented at a meetup in Kansas City by Bahador Khaleghi of H2O.ai.
More details can be viewed here: https://www.meetup.com/Kansas-City-Artificial-Intelligence-Deep-Learning/events/265662978/
When talking about modeling, I think there will be a bundle of terms that will come to our mind, UML, domain driven development, DSL, forward/reverse enginerring, MDD, MDA, BPMN. These technology or methodology have been there for years; And obviously, modeling has proven itself to provide value by improving communication, business-alignment, quality, and productivity. Its applicability includes a number of disciplines such as analysis, design, or development. But why aren’t we all doing Model Driven Development yet?
Why is dev ops for machine learning so differentRyan Dawson
DevOps instincts tend to be shaped by what has worked well before. Instincts derived from mainstream software development projects get challenged when we turn to enabling machine learning projects. The key reasons are that the development/delivery workflow is different and the kind of software artefacts involved are different. We will explore the differences and look at emerging open source projects in order to appreciate why the DevOps for machine learning space is growing and the needs that it addresses.
A presentation about COCOMA, a framework for COntrolled COntentious and MAlicious patterns, presented at MERMAT, 2nd International Workshop on Measurement-based Experimental Research, Methodology and Tools, FIA 2013, Dublin, Ireland
SiriusCon 2015 - Breathe Life into Your Designer!melbats
You have your shiny new DSL up and running thanks to the Eclipse Modeling Technologies and you built a powerful tooling with graphical modelers, textual syntaxes or dedicated editors to support it. But how can you see what is going on when a model is executed ? Don't you need to simulate your design in some way ? Wouldn't you want to see your editors being animated directly within your modeling environment based on execution traces or simulator results?
The GEMOC Research Project designed a methodology to bring animation and execution analysis to DSLs. The companion technologies required to put this in action are small dedicated components (all open-source) at a "proof of concept" maturity level extending proven components : Sirius, Eclipse Debug, Xtend making such features within the reach of Eclipse based tooling. The general intent regarding those OSS technologies is to leverage them within different contexts and contribute them to Eclipse once proven strong enough. The method covers a large spectrum of use cases from DSLs with a straightforward execution semantic to a combination of different DSLs with concurrent execution semantic. Any tool provider can leverage both the technologies and the method to provide an executable DSL and animated graphical modelers to its users enabling simulation and debugging at an early phase of the design.
This talk presents the approach, the technologies and demonstrate it through an example: providing Eclipse Debug integration and diagram animation capabilities for Arduino Designer (EPL) : setting breakpoints, stepping forward or backward in the execution, inspecting the variables states... We will walk you through the steps required to develop such features, the choices to make and the trade-offs involved. Expects live demos with simulated blinking leds and a virtual cat robot ! This talks presents also xCapella an industrial use case onwhich the Gemoc methodology was applied.
This talks was presented at SiriusCon 2015 in collaboration with Jérôme Le Noir from Thales.
The Arduino Designer documentation is available on : https://github.com/mbats/arduino/wiki/Documentation
Provides products to commercial organizations and government projects seeking to improve their application delivery and development timelines and resource load
MLeap: Deploy Spark ML Pipelines to Production API ServersDataWorks Summit
MLeap is an open-source technology that allows Data Scientists and Engineers to deploy Spark-trained ML Pipelines and Models to a scoring engine instantly. During our presentation, we will show you how to deploy any Spark ML Pipeline, as well as custom transformers, that are trained using Spark streaming to both a cloud-based API server as well as an IoT device.
Why MLeap? Data Scientists use a myriad tools to analyze datasets, clean them and build offline models and validate their performance. The resulting scripts are thrown across the wall to Data Engineers and Architects whose job is to bring these pipelines to production. The Engineers are left with the unenviable job of not only reproducing the Data Scientists’ conclusions, but to scale the resulting pipeline both of which require a deep understanding of Data Science itself. As a result, most if not all Data Science deployments in the wild end up either too simplistic or take too long to productionize.
MLeap solves this problem for Spark users by providing serialization of ML Pipelines’ transformers to an MLeap Bundle, which is a graph-based serialization framework built on top of Protobuf 3 and JSON. In addition, MLeap also provides a highly optimized execution engine that doesn’t rely on the Spark-context, making inference blazing fast and is capable of executing one model or thousands of models in parallel.
Nads & presagis teaming to innovate in distributed simulation xxSimware
NADS and Presagis have agreed to team up to promote modeling & simulation (M&S) solutions based on both companies' commercial-off-the-shelf (COTS) software. On November 8th, representatives of the two parties signed a Marketing Agreement at NADS Headquarters to conclude the deal.
The agreement includes collaboration for marketing of COTS-based solutions for Live-Virtual-Constructive (LVC) interoperability. These solutions will be based on Presagis product lines and SIMWARE, the flagship product from NADS.
Know about the agreement between NADS and PRESAGIS and how this companies complement in tools and technologies.
You can see also the webinar made on april 17th about this presentation
http://youtu.be/WPAJ1_UfP9E
These slides were presented at a meetup in Kansas City by Bahador Khaleghi of H2O.ai.
More details can be viewed here: https://www.meetup.com/Kansas-City-Artificial-Intelligence-Deep-Learning/events/265662978/
When talking about modeling, I think there will be a bundle of terms that will come to our mind, UML, domain driven development, DSL, forward/reverse enginerring, MDD, MDA, BPMN. These technology or methodology have been there for years; And obviously, modeling has proven itself to provide value by improving communication, business-alignment, quality, and productivity. Its applicability includes a number of disciplines such as analysis, design, or development. But why aren’t we all doing Model Driven Development yet?
Why is dev ops for machine learning so differentRyan Dawson
DevOps instincts tend to be shaped by what has worked well before. Instincts derived from mainstream software development projects get challenged when we turn to enabling machine learning projects. The key reasons are that the development/delivery workflow is different and the kind of software artefacts involved are different. We will explore the differences and look at emerging open source projects in order to appreciate why the DevOps for machine learning space is growing and the needs that it addresses.
A presentation about COCOMA, a framework for COntrolled COntentious and MAlicious patterns, presented at MERMAT, 2nd International Workshop on Measurement-based Experimental Research, Methodology and Tools, FIA 2013, Dublin, Ireland
This presentation was held at the SBML/BioModels.net hackathon on May 1st 2010. It gives a brief overview of SED-ML and introduces a first implementation with http://libsedml.sf.net.
5. FDMU
In addition to geometry models
behavior models of the mechatronic domains:
software
electronics
mechanics
integration
behavior models in diffe-
DMU geometry models rent modelling languages
6. So the question arises:
What is the equivalent for geometric integration with respect to behavior
models?
Approaches
mapping of the heterogeneous behavior models into one standard?
no existing standard with full representative power
execution of such an integrated behavior model in a ‚super simulator‘?
7. Co-simulation matrix (Prof. Dr.-Ing. Marcus Geimer, Universität Karlsruhe)
number of
modeling tools
integration of
>1 co-simulation
models
integrating
„classic“
=1 different
simulation
simulators
=1 >1
number of
simulation algorithms
8. We decided to go for a
flexible
open
extensible
vendor-independent
co-simulation framework:
the FDMU framework Mastersimulator
Wrapper Wrapper Wrapper
Rhapsody Bild
Dymola Simpack
9. We want to use the native behavior models …
as unchanged as possible
but some small adaptations are needed
in / out values
parameters to be changed
native model connector communication-enabled model
11. Connectors to map native syntax to standardized syntax
Which standards?
SysML
Modelica
VHDL-AMS
SysML (Systems Modeling Language)
Modeling language for systems engineering
XML base
UML/XML tools exist to create and process SysML
Requirements and systems modeling
12. Mapping of internal interfaces to a standardized form
Encapsulation of behavior models (example: controller)
Controller (SysML)
Unified description
of interface
variables of the
behavior model
Controller (native)
native interface of
behavior model
13. ‚Glueing‘ functional building blocks together to create
a simulation model
U
up f
down s
I
and adding geometry models to embed them in an OO way
14. Next: the simulators
Spice
Rhapsody
Dymola
simulators
15. They are as heterogeneous as the behavior models with respect to:
programming languages and APIs
communication schemes
platforms
simulators Rhapsody Dymola
Dymola Simpack
Simpack
How to cope with their heterogeniety?
16. Solution strategy: Wrapper
controlling the simulator
communicating and mapping data
standardizing interfaces
Wrapper Wrapper Wrapper
simulators Rhapsody Dymola Simpack
17. Which information does a wrapper receive?
interface
Wrapper Wrapper Wrapper
description
simulators Rhapsody Dymola Simpack
behavior models
18. Wrapper
different communication strategies
based on configuration of connectors
19. Wrapper
simulator control commands, e.g.
configure
initialize
run Wrapper
start data
control
commands exchange
suspend
resume
stop Simulator
terminate
20. What else do we need?
a master for communication and co-ordination
Which information does the Mastersimulator need?
system
behavior model Mastersimulator
interface
Wrapper Wrapper Wrapper
description
simulator Rhapsody Dymola Simpack
behavior models
22. Mastersimulator
TransferHandler
support different protocols
constant data flow
constant data flow with upsampling
constant data flow with downsampling
event based input with sampling
event based input /output
23. To complete the FDMU framework …
system behavior model
interactive visualization and geometry models
system
behavior model Mastersimulator
interface
Wrapper Wrapper Wrapper
description
simulator Rhapsody Dymola Simpack
behavior models
25. Integration of FE analysis: E-motor example
Demand to integrate and possible couple with
finite element analysis
Challenge: FE known to be slow
A coupling scenario for an E-motor
E-motor with electronic control
Electronics on a PCB mounted at heat sources
the backside of the E-motor
The electric behavior of the parts
on the PCB depends on the thermal
conditions (heat)
-> thermo-dynamic simulation
warming of the PCB
(transistors, controller)
26. Integration of FE analysis: E-motor example
General questions that may arise in the design process:
How warm will the transistors get?
What is the contribution of the engine to the temperature of the transistors?
Does the warming have effects on other elements, e.g. the controller?
What kind of cooling to attach to the transistors?
What happens if the distance between motor and PCB is changed?
Etc.
27. Integration of FE analysis: E-motor example
Physical model
controller load
software
time
motor (converter) time
signal electronics mechanics
signal
power loss
power loss
influence
on behavior thermo-dynamics
28. Integration of FE analysis: E-motor example
Physically-motivated splitting into partial models
TE1: temperature transistor 1
TE2: temperature transistor 2
TM: temperature motor winding
PE1: power loss transistor 1
PE2: power loss transistor 2
PM: power loss motor
29. Integration of FE analysis: E-motor example
We started to model the thermal behavior within ANSYS
approx. 50.000 nodes (volume mesh)
simulation time in the range of hours
way too slow for interactive simulation
30. Integration of FE analysis: E-motor example
Reduced model
Model order reduction
reduced systems of equations:
50.00 nodes > 100 nodes
Execution time
almost interactive
little impact on accuracy
(we have not measured
precision yet)
31. Integration of FE analysis: E-motor example
Mapping of the behavior models to simulators
Saber Dymosim Dymosim
Feed forward control
- Matlab
- direct user input input file
Reduced thermo-dyn.
Model (Dymosim)
34. Achievements
FunctionalDMU framework
open, extensible, flexible
FDMU
visualization
FDMU
distributed, service-oriented architecture service
s
Berlin
unique combination of features Saber
solvers Dresden
Darmstadt
methodology
visualization features
Mastersimulator Rhapsody
FDMU-Editor Simulink, Visu
Wrappers for
Dymola
Simulink SimPack Dymola, Mastersim.
Rhapsody, SimPack, Saber, Dymola (Modelica), Matlab/Simulink, …
Methodology for modelling, integrating and running FDMU simulations
Proof-of-concept scenarios
35. Benefits
end-user point of view
earlier multi-domain problem detection
visual insights and communication
integrated 2D/3D interactive visualization
shorter set-up of mechatronic simulations
re-use of behavior models (FBB) in different configurations
no transformation of models
running behavior models as services (without forwarding know-how)
simulation tool provider point of view
re-usable components for simulation coupling and
integrated visualisation
36. Outlook
Wish list / research issues
fast and flexible simulations, esp. FEM
coupling-in more different FE domains
taking environment conditions and tolerances into account
real-time requirements
‚informed‘ CAD models
data management -> MechatronicPLM
optimization
organizational aspects
IP issues
LTP of behavior models
37. Das Produkt muss vollständig als Gesamtsystem simulierbar sein.
(Bernd Ehrenberg, Daimler AG)