Dymola uses acausal (or non-causal) modelling methods for the definition of component and system models. This contrasts with other general purpose modelling packages that use causal modelling methods such as block diagrams.

The key advantage to acausal modeling is that it speeds up the model development process as it simplifies what the user must do. Component models are simply dragged into the model and connected in an intuitive manner without consideration of the calculation order. It also simplifies model maintenance as there is only one definition of each component model that needs to be maintained.

Dymola is able to rearrange the model equations to suit the particular problem but this is only done once the entire model is ready to be simulated. Sophisticated symbolic manipulation techniques mean that even extremely large, complex models can be handled.

Acausal, or non-causal, modelling essentially means that models are defined without consideration of the order in which the variables need to be calculated. This enables models to be defined in a more general way simplifying the model development and maintenance tasks.  A tool that uses true acausal modelling methods must, therefore, also do symbolic manipulation so that the model equations can be re-ordered automatically when the model is run.

If we take a 1D rigid inertia component as an example we can start to see the benefits. In Dymola the component is defined once in a general way, see the figure below.  This enables the model equations to be written in a convenient and recognisable form enabling easy understanding of components. This component can then be re-used in any model regardless of the causality required in any particular case.

In a causal modelling tool (such as a block diagram modelling tool or conventional programming language) we would require four different models of our 1D rigid shaft to ensure we have a model for every possible causality, see the figure below.  This is because a causal modelling tool carries out the calculations in a fixed order that is defined by the model developer.  To ensure we have a model for every causality the model developer has to manually rearrange the equations into the four correct forms and then implement them in the modelling tool.