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Gefördert durch das
Co-Simulation of Modelicaand Complex Models using High Performance Solvers
Dr. Andreas Nicolai
Dresden University of Technology Germany
Modeling needs in the present and future
• Interdisciplinary
• Mix of models of different complexity
• Different sources of models
• Different groups of experts collaborate on a single large model
• Stochastic Approaches (= many simulations)
• Increasing amount of input/output data
Dresden, June 11 2013 Nicolai – Co-Simulation of Modelica andComplex Models
Slide 2
Co-Simulation – Overview & Motivation
Why don‘t we just pick one flexible simulation tool and buildeverything in it?
Dresden, June 11 2013 Nicolai – Co-Simulation of Modelica andComplex Models
Slide 3
Co-Simulation – Overview & Motivation
Why don‘t we just pick one flexible simulation tool and buildeverything in it?
We start with existing model components …
Dresden, June 11 2013 Nicolai – Co-Simulation of Modelica andComplex Models
Slide 4
Co-Simulation – Overview & Motivation
Modelica
Why don‘t we just pick one flexible simulation tool and buildeverything in it?
We start with existing model components,
we create new models to add functionality …
Dresden, June 11 2013 Nicolai – Co-Simulation of Modelica andComplex Models
Slide 5
Co-Simulation – Overview & Motivation
Modelica
Why don‘t we just pick one flexible simulation tool and buildeverything in it?
We start with existing model components,
we create new models to add functionality,
and then our new models become BIGGER… (i.e. complex models)
Dresden, June 11 2013 Nicolai – Co-Simulation of Modelica andComplex Models
Slide 6
Co-Simulation – Overview & Motivation
Modelica
Why don‘t we just pick one flexible simulation tool and buildeverything in it?
We start with existing model components,
we create new models to add functionality,
and then our new models become BIGGER… (i.e. complex models)
… and then we have a problem a challenge!
Dresden, June 11 2013 Nicolai – Co-Simulation of Modelica andComplex Models
Slide 7
Co-Simulation – Overview & Motivation
Modelica
Some things to consider when dealing with complex models:
• Modeling Effort(expert modeling knowledge and the model itself may exist alreadysomewhere else)
• rather reuse an existing model than recreate it
• Pre- and Post-Processing (different complex models may have very different data needs)
• e.g. grid generation for CFD and transport models
• BIM data import for multizone models
• model-specific structured data representation
• Simulation Performance(you can‘t use complex models if you wait forever for results!)
• different models may require very different approaches to be solvedefficiently
• may require changes in modeling environment/solver
Dresden, June 11 2013 Nicolai – Co-Simulation of Modelica and Complex Models
Slide 8
Co-Simulation – Overview & Motivation
Co-Simulation between Modelica and Complex Models:
Dresden, June 11 2013 Nicolai – Co-Simulation of Modelica and Complex Models
Slide 9
Co-Simulation – The Principle
Modelica
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Co-Simulation between Modelica and Complex Models:
1. Data Handling: Re-use existing pre and post-processing tools foreach complex model
2. Interfaces: What data are exchanged? Complex models shouldsupport interface standard (� FMI)
3. Coupled Solution Method: Model Exchange/Co-Simulation
Dresden, June 11 2013 Nicolai – Co-Simulation of Modelica and Complex Models
Slide 10
Co-Simulation – The Principle
Co-S
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Co-S
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ModelicaModelicaModelicaModelica ModelModelModelModel
Complex Models developed at the IBK:
• 2D/3D Transport Model DELPHIN(Hygrothermal + Pollutants + Air flow + Salts + Ice)
• Room Simulation Models THERAKLES and HAJAWEE(non-geometric and geometric models)
• Building Simulation Model NANDRAD(Multizone model with generic model component support)
Dresden, June 11 2013 Nicolai – Co-Simulation of Modelica and Complex Models
Slide 11
Characteristics of Complex Models
Dresden, June 11 2013 Nicolai – Co-Simulation of Modelica andComplex Models
2D/3D Heat and Mass Transport Model (Delphin)
Slide 12
• Flexible Transport Model:
• Heat and Moisture
• Pollutants
• Air flow
• Salts and Ice
• Pre- and Post-prozessing
• Problem Specific Solver Optimization
Dresden, June 11 2013 Nicolai – Co-Simulation of Modelica andComplex Models
Slide 13
Thermal Bridge Example(5943 Elements)
Jacobi-Matrix (11886 x 11886)
2D/3D Heat and Mass Transport Model (Delphin)
Dresden, June 11 2013 Nicolai – Co-Simulation of Modelica andComplex Models
2D/3D Heat and Mass Transport Model (Delphin)
Slide 14
• Dynamic time step adjustment regime (accuracy controlled):
Dresden, June 11 2013 Nicolai – Co-Simulation of Modelica andComplex Models
Non-Geometric Room Model THERAKLES
Slide 15
• Considers in Detail:
• Thermal Storage and Heat Transmission
• Solar Gains
• User Loads
• Simple Pre- and Post-prozessing
• Problem Specific Solver Optimization
Performance Benchmark Intel i7-2860QM 2.5 GHz
(Annual Room Simulation, Wall Clock Time in Seconds)
Dresden, June 11 2013 Nicolai – Co-Simulation of Modelica andComplex Models
Geometric Room Model HAJAWEE
Slide 16
• Includes in Addition to THERAKLES:
• Long-wave Radiation Exchange
• Thermal Comfort Analysis
• Humidity and CO2 Balances
• Interface to HVAC Component Models and Control Algorithms
Dresden, June 11 2013 Nicolai – Co-Simulation of Modelica andComplex Models
Building Simulation Model NANDRAD
Slide 17
• Classical Multizone Model
• Integrated BIM Data Model (+ Import)
• Generic Model Evaluation Engine(graph-based algorithms)
• Solver Optimization for LARGE Building Models
Jacobian Matrix Sparsity PatternGeneric Component Model Support
Dresden, June 11 2013 Nicolai – Co-Simulation of Modelica and Complex Models
Slide 18
Co-Simulation Master Algorithms
Tim
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• Master exchanges data between models
• Master signals models to integrate tonext communication point
• Master collects data, compares withpreviously exchanged data� If not converged, redo integration
• Master may decide to update only fast models
• Once model results no longer differ, completeintegration step
Communication Point
Communication Point
Model integration can bedone in parallel, fastmodels may have to wait
Communication Point
Communication Point
Integration Step Completed
Begin Integration Step
Co-Simulation Benefits
• Individual models can use their native solver� Improved efficiency of coupled simulation
• Existing models can be re-used� Saves development time, allows problem-specific pre-
and postprocessing
Research Challenges• Good support for FMI standard in complex models
• Intelligent master algorithms and good implementations
• Whole Co-Simulation procedure should be still "manageable"
Dresden, June 11 2013 Nicolai – Co-Simulation of Modelica and Complex Models
Slide 19
Co-Simulation Benefits and Research Challenges
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