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Figure 8: Simple stand alone use case. Master and slave are running in the same process. The slave contains model and solver, no additional tool is needed. The next use case is carried out on different pro- cesses. A complete simulation tool acts as slave. In this case, the FMI is implemented by a simulation tool dependent wrapper which communicates by a (proprietary) interface with the simulation tool.

Figure 8 Simple stand alone use case. Master and slave are running in the same process. The slave contains model and solver, no additional tool is needed. The next use case is carried out on different pro- cesses. A complete simulation tool acts as slave. In this case, the FMI is implemented by a simulation tool dependent wrapper which communicates by a (proprietary) interface with the simulation tool.

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Figure 2: Top level part of the FMI XML schema Both approaches share a bulk of common parts that
Figure 2: Top level part of the FMI XML schema Both approaches share a bulk of common parts that
In Figure 2, the top- FMI-cases, with excep mentation”. If present, ion of evel par nition is shown. All parts are t of the schema defi- he same for the twc he element “Imple- he import tool should inter- pret the model description as applying to co- simulation. As a consequence, select the C-functions for model exchange. An impor he import tool mus: for co-simulation, otherwise ant part of the “Im. plementation” is the definition of capability flags tc define the capabilities supports: hat the co-simulation slave
In Figure 2, the top- FMI-cases, with excep mentation”. If present, ion of evel par nition is shown. All parts are t of the schema defi- he same for the twc he element “Imple- he import tool should inter- pret the model description as applying to co- simulation. As a consequence, select the C-functions for model exchange. An impor he import tool mus: for co-simulation, otherwise ant part of the “Im. plementation” is the definition of capability flags tc define the capabilities supports: hat the co-simulation slave
At every event instant #,, variables might be discon- tinuous and therefore have two values at this time instant, the left” and the ’right” limit. x(¢,), m(#) are always defined to be the right limit at 4, whereas x (t;), m™ (t,) are defined to be the “left” limit at 4, e.g.: m (f;) = m(¢,.,). In the following figure, the two variable types are visualized: An event instant ¢; is defined by one of the following conditions that gives the smallest time instant:
At every event instant #,, variables might be discon- tinuous and therefore have two values at this time instant, the left” and the ’right” limit. x(¢,), m(#) are always defined to be the right limit at 4, whereas x (t;), m™ (t,) are defined to be the “left” limit at 4, e.g.: m (f;) = m(¢,.,). In the following figure, the two variable types are visualized: An event instant ¢; is defined by one of the following conditions that gives the smallest time instant:
Figure 7: Three tools are controlled by one master 4.1 Master Slave Principle
Figure 7: Three tools are controlled by one master 4.1 Master Slave Principle
The slave (which can be a simulation tool as in Fig- ure 9 too) and the master run on different computers. The data exchange is handled by a communication layer which is implemented by a special FMI wrap- per. Neither the master nor the slave notice the tech- nology used by the communication layer. Both uti- lize the FMI for Co-Simulation only.
The slave (which can be a simulation tool as in Fig- ure 9 too) and the master run on different computers. The data exchange is handled by a communication layer which is implemented by a special FMI wrap- per. Neither the master nor the slave notice the tech- nology used by the communication layer. Both uti- lize the FMI for Co-Simulation only.
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