LEAVING TOIL TO MACHINES-BUILDING SIMULATION KERNEL OF EDUCATIONAL SOFTWARE IN MODERN SOFTWARE ENVIRONMENTS

In past, simulation models had been created directly in the software development environment used for making the educational software itself. Recen tly, specialized development tools rather than universal programming languages have been used for making and verifi cation of simulation models. Common choice is that of the block oriented environments (e.g. Simulink). In these environments, hierarchically connected blocks that process input information to output information are used and connected when creating a simulation model. Signals fl ow in connections between individual blocks, transmitting the values of individual variables from the output of one block to the input of other. The s tructure of the interconnected network preserves the algorithm of calculation, i.e. clarifi es how the values of particular variables are calculated step by step. The whole process is known as causal modelling. The limitation of the causal approach to modelling is that it refl ects more the calculation procedure than the actual structure of the modelled reality. The physical structure of the modelled world is captured only by the structure of calculation. Causal modelling is especially arduous for intricate, hierarchically organized models, for instance the models of complex physiological systems, which biomedical simulators are based on. At present, new simulation environments have become available. Fundamental innovation of these environments comprises of the possibility to describe individual parts of the model as a system of equations directly describing the behaviour of that part and not the algorithm of solving of these equations. Model description is declarative (the structure of mathematical relationships instead of algorithm of calculation is described) and notation is therefore acausal. Acausal modelling environments work with interconnected components (i.e. blocks) as well. A component represents an instance of class for which equations or parameters are defi ned. Components are linked through connectors that are defi ned more precisely then usual, as they themselves help to defi ne the system of equations. Thus, the connections between components do not defi ne the calculation procedure but rather the modelled reality. The exact algorithm and method of solving the equations is “left to the machines”. When large and complex systems are modelled in acausal simulation environments (e.g. based upon modelling language Modelica), the whole process can be facilitated so substantially, as to make it the breakthrough innovation in building the simulation kernel of biomedical educational software. Advantages of the acausal approach are demonstrated using Modelica implementation of a large-scale physiological system model “Quantitative Human Physiology” as an example.