Decision Support and Safety
Head: Dr. rer. nat. Sadeeb Simon Ottenburger
The UNF working group focuses on research and development of methods and tools to support decision-makers in the management and control of major emergencies and crisis situations.
Another focus is the research and development of AI-based concepts to improve the resilience of critical infrastructures against the background of increasing uncertainties and risks.
Activities focus on emergency protection after nuclear accidents and emergency preparedness and on increasing the resilience of critical infrastructures and urban resilience.
In the field of nuclear emergency preparedness, the UNF working group is researching the assessment of the impact of accidentally released radioactive substances from nuclear facilities on humans and the environment. To this purpose, simulation models and programming systems are being developed for operational use primarily in Europe, but also worldwide, which can be customized to national and regional conditions. The decision support system JRODOS has been developed by the UNF working group in the framework of several EU funded projects (EURANOS, PREPARE, CONFIDENCE). Currently, the main focus of the work is the integration of uncertainties in decision support and source term estimation in the early phase of an accident.
- How can meteorological ensembles (between 10 and 40 realizations of a weather event) and different source terms (realistic, optimistic, pessimistic) be used to represent the uncertainties in the early phase of an accident?
- Which mathematical methods can be used to estimate the source term from measured data and results of atmospheric dispersion models?
JRODOS is used worldwide in more than 30 countries and in Germany for nuclear emergencies.
Resilience of Critical Infrastructures The term resilience in the group Accident Management Systems is primarily aimed at maintaining the basic supply of society with critical services (e.g. electricity, water, health care). Our systemic research investigates degrees of freedom in the design of critical infrastructures (e.g. supply networks) and in the area of robust and dynamic measure development with the aim of improving the resilience of individual infrastructures or infrastructure systems. In this context, AI-based concepts, methods and algorithms are developed for the following main topics:
- Planning and (real-time) operation of resilient and adaptive critical infrastructures and supply networks - in particular smart energy systems, water supply and transport systems (motorways) are considered.
- Dynamic decision support in crisis management - methods for the multi-criteria development of robust and coordinated measures including different critical infrastructures such as healthcare systems, power and water supply (urban resilience) are investigated.
This activity is also supported from 2012 onward as part of the HGF (Helmholtz-Gemeinschaft Deutscher Forschungszentren e.V.) portfolio activity "security".
o Research Fields:
- Simulation models for dispersion processes and dose estimations
- Simulation models for protection and countermeasures
- Models and algorithms for dynamic measure development
- Basic research and development of concepts to increase resilience using smart technologies in the field of smart grids, transport systems, urban systems (health system, water supply, etc.)
- Development of operationalizable resilience metrics, e.g. in border areas of engineering and socio-technical disciplines
- Multi-criteria analysis o Knowledge databases and case-based reasoning
- Risk-based scenario techniques o Dealing with uncertainties
- Computer-aided decision support systems
- Operating software for decision support systems including databases and measurement value processing
o System development
- Realization of user-friendly interfaces for data input and output, and system operation
- Software components well-balanced in their content and programming techniques
- Application oriented presentation of results
- GIS and web-based applications and services Integration of simulation models and system components
- Interdisciplinary dialogue between model and system developers
- continuous exchange of experiences with the end-users