Co-Simulation of Modelica and Complex Models using High

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

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Co-Simulation – Overview & Motivation

Why don‘t we just pick one flexible simulation tool and buildeverything in it?


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We start with existing model components …


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Modelica


We start with existing model components,

we create new models to add functionality …


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Modelica



we create new models to add functionality,

and then our new models become BIGGER… (i.e. complex models)


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Modelica



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!


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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

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Co-Simulation between Modelica and Complex Models:


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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


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Co-Simulation – The Principle

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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)


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Characteristics of Complex Models


2D/3D Heat and Mass Transport Model (Delphin)

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• Flexible Transport Model:

• Heat and Moisture

• Pollutants

• Air flow

• Salts and Ice

• Pre- and Post-prozessing

• Problem Specific Solver Optimization


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Thermal Bridge Example(5943 Elements)

Jacobi-Matrix (11886 x 11886)




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• Dynamic time step adjustment regime (accuracy controlled):


Non-Geometric Room Model THERAKLES

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• 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)


Geometric Room Model HAJAWEE

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• Includes in Addition to THERAKLES:

• Long-wave Radiation Exchange

• Thermal Comfort Analysis

• Humidity and CO2 Balances

• Interface to HVAC Component Models and Control Algorithms


Building Simulation Model NANDRAD

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• 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


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Co-Simulation Master Algorithms

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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"


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Co-Simulation Benefits and Research Challenges

Documents

Co-Simulation of Modelica and Complex Models using High