Research Focus

Nuclear power will be still a part of national power production until 2022. Ensuring system and reactor safety for the remaining runtime is one of the key topics of this department.

For more than 15 years, we have been working on nuclear safety research projects that mainly focus on the controllability of loss-of-coolant accidents. Both experimentally and simulatively, we contribute to this in proven cooperation with the Helmholtz Centre Dresden-Rossendorf (HZDR) and the TU Dresden.

Another core theme of the department is Soft Computing. While the Machine Learning branch is experiencing an overwhelming renaissance right now, the department already has longtime and extensive experience, among others with modeling and practical application of artificial neural networks and fuzzy logic.

Nuclear safety research

• assured sump intake, Loss-of-Coolant-Accidents with particle formation and release, methodical and experimental investigation of particle flows

System and reactor safety

• safety and reliablility analysis of components and systems
• modeling/simulation (model-based measurement methods, soft computing)
• thermohydraulics

Soft computing, maschine learning

• Fuzzy systems (Mamdani, Takagi-Sugeno-Kang)
• Maschine learning: artificial neuronal networks (multilayer perceptron, self-organising map... )
• Support-vector machines (SVM)

non-linear systems

• genetic algorithms
• fractal algorithms
• multi-agent systems

Control engineering, process control

• power and processing technology

Image processing

• object recognition
• tracking
• optical quality control

Theoretical and experimental investigation

• two-phase flows water/steam/gases

Simulation systems

• simulation codes ATHLET, RELAP, CFD, COCOSYS

Nuclear energy was still involved in national electricity generation until 2023. The plant and reactor safety to be ensured for the remaining lifetime was one of the focal points of the Nuclear Technology/Soft Computing department.

For more than 20 years, we have been working on nuclear safety research projects that mainly focus on the controllability of loss-of-coolant accidents. We contribute both experimentally and simulatively in proven cooperation with the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) and the TU Dresden.

Another focus of the department is soft computing. While the field of machine learning is currently experiencing an overwhelming renaissance, the department can already draw on many years of extensive experience in this area, including in the modeling and practical application of artificial neural networks (ANN) and fuzzy technologies.

Nuclear safety research

• Safe sump suction, loss-of-coolant accidents with particle formation and release, methodical and experimental investigations on particle flows

Plant and reactor safety

• Safety and reliability analyses of components and systems
• Modeling/simulation (model-based measurement methods, soft computing)
• Thermohydraulics

Soft computing, machine learning

• Fuzzy systems (Mamdani, Takagi-Sugeno-Kang)
• Machine Learning: Artificial Neural Networks (Multilayer Perceptron, Self-Organizing Map... )
• Support Vector Machines (SVM)

Nonlinear Dynamic Systems

• Genetic algorithms
• Fractal Algorithms
• Multi Agent Systems

Control engineering, process control

• Energy and process engineering

Image processing

• Object recognition
• tracking
• Optical quality control

Theoretical and experimental investigations

• Two-phase flows of water/steam/inert gases

Simulation technology

• Simulation codes ATHLET, RELAP, CFD, COCOSYS, DynStar