No compromises

Hardly any technical system today is created without the aid of computer simulations. With their help, developers can assess the success of their previous work more quickly than would be possible through the construction of gradually improved prototypes. However, if a simulation is to take several physical properties into account at the same time, things become complicated and time consuming. A research team at the University of Stuttgart is using artificial intelligence methods to make such multiphysical simulations for electrotechnical problems better and faster.

Companies can use simulations to significantly reduce the product development times. Although the object in question still goes through all the traditional phases of development - from conceptualization, design and detailing to prototype construction, testing and revision - they are all shorter. Using simulated application scenarios helps developers to assess how a product will behave in various, possibly even extreme, situations at a much earlier stage. However, such simulations can become almost arbitrarily complex, both in terms of the computing effort required, which can quickly become enormous, and for the developers, who need to have an extremely precise understanding of all parameters to ensure that the results actually reflect reality. “This becomes particularly evident in multiphysical simulations,” says Prof. Jens Anders, Head of the Institute for Smart Sensors (IIS) at the University of Stuttgart. “These consider several areas of physics simultaneously, such as a system’s thermal and mechanical behavior.” If, for example, a pump moves its piston, it heats up at the same time. Thus, not only does the shape of the magnetic field in which the rotor rotates have a significant bearing on the behavior of an electric motor, but also the temperature the motor reaches during operation. Moreover, there are interdependencies between the various sub-areas. In terms of the motor, for example, this means that increasing electrical currents also generate more heat.

“For example, to simulate the behavior of such a motor, we would have to calculate the temperature and the magnetic field at many points,” explains Anders. “If an unlimited amount of computing power were available, these points could be placed over the entire model of the engine like a close-meshed network.” But because computing power still has to be used economically today, despite increasingly improving computer technology, this network has to be plotted as coarsely as possible. “Only as accurately as necessary is the rule when simulating,” says Anders. The choice between computing speed and accuracy involves constant compromise - but no important physical effect should be missed.

The experts know, for example, that they have to plot the mesh nodes very closely to calculate the magnetic field, especially along corners and edges. On the other hand, the calculation points in the metal body of the motor can be spaced further apart. The temperature calculation is different: corners and edges or the air gap of the motor are of very little interest in this context, on the other hand, the metal body dissipates a large part of the generated heat, so that a close-meshed network is required for calculations. “Different networks are needed for each physical sub-area, but they have to be adapted to each other in such a way that the results make sense overall,” says Anders.

Due to this complex starting position, science and industry have not yet fully exploited the potential of multiphysical simulations. This was the reason why the IIS and the Institute of Industrial Automation and Software Engineering (IAS) at the University of Stuttgart launched a joint research project a few years ago with funding from the German Research Foundation (DFG). “Most development engineers not only have to deal with multiphysical simulations but also many other tasks,” says Prof. Michael Weyrich, Head of the IAS. So it can happen that they fail to set up complex multiphysical simulations properly, require many attempts or even need external help from specialists. “This costs at least time and money and, in extreme cases can even leads to the decision to abandon multiphysical simulations altogether,” said Weyrich.

The researchers at both institutes want to eliminate this hurdle. “We have provided the user with an intelligent assistant for multiphysical simulations of electrotechnical systems, which executes various tasks independently, so that the user no longer has to have such a profound knowledge of the topic,” Weyrich explains. “Based on available simulation results that make physical sense, the assistant recommends approaches to existing simulation problems.” The project participants created a database for this purpose in which the assistant can search for similar known cases for a given subproblem of the simulation in hand. “If it finds a corresponding match, it uses it as a basis for proposing solutions to the user for the relavant subproblem,” says Weyrich.

In fact, the structure and operation of this assistant are much more complex than described here. It actually coordinates several subordinate assistants, each of which is responsible for a specific subtask such as mechanics or temperature. In addition, there are always various options that the downstream assistants can choose for themselves to select the most resource- efficient assistant for achieving the objective.