Simulation and Virtual Design Department

The energy transition requires a comprehensive, concerted transformation of almost all industrial processes in order to switch production from fossil fuels to renewable energy sources by 2045. For this to succeed, this transformation must achieve climate targets, preserve jobs and living standards, and secure energy supply at the same time. The Simulation and Virtual Design Department contributes to the institute's goals for reducing industry-related CO2 emissions through the development of decarbonization strategies and the establishment of digital twins for emission-intensive industrial processes, as well as through their cross-sector coupling in smart energy systems.

The Department

With the development of decarbonization strategies, the most effective transformation path is devised in collaboration with the industry and based on existing plants at the production site in order to gradually convert fossil production into CO2-neutral processes. Therefore, the entire process or selected sections, e.g. the steam network or a production line, are first transferred into a virtual image using the most suitable simulation tools. For this purpose, new software tools are being developed in the SVD department, with the help of which virtual industrial processes can be created and scientifically investigated. Measurement data from the real plants are used to validate these virtual images. These images are examined and systematically modified, e.g. by substituting fossil emission sources with new, sustainable technologies such as high-temperature heat pumps, low-carbon energy sources, energy storages or renewable electricity sources. Eventually, the optimal transformation concept for the production site is found, the implementation of which can be pursued further by the company or with the DLR in joint projects

The transition to renewable energy sources is always associated with a fluctuating energy supply, which must be compensated by storages and load-flexible operation of plant components. Future industrial processes must be controlled predictively in order to be operated at optimal cost. This requires the development of digital twins. This term refers to realistic, virtual images of industrial processes that are operated in parallel with the real process and are linked to it via real-time measurements. Based on price and weather forecasts, the digital twin determines the emission- or cost-optimal operation for the next few hours. The real plant then follows the optimal operational plan. This requires detailed models of all process steps, very accurate prediction models and efficient optimization algorithms, which are continuously developed and improved in the SVD department.

In the future, energy demand must also be reduced by efficiently linking industrial processes with other sectors. In particular, the development of intelligent energy networks plays a major role in the interconnection of energy supply and consumers. To achieve maximum effficiency, solar and wind energy, power plants, storage systems, residential and public buildings, and industrial plants must be interconnected. AI-supported programs control how the available energy is distributed, stored, converted or retrieved at any given time. The simulation environment developed by the SVD department enables low-loss distribution of electricity, mechanical work, heat, and basic materials across sectors. Future cities and industrial sites will have to use energy more efficiently. For this very reason, the realization of these networks is of great importance as an essential part of energy research.

Within the institute, the SVD department also supports research work on high-temperature heat pumps, a key component of the heat transition, by developing a flexible simulation environment and investigating the detail degree in which the process steps providing high-temperature heat can be simulated. In cooperation with other DLR institutes and research facilities, we are working on the further development of simulation methods that optimize the interaction of the components of future energy systems.