Structure
| complex | titel | leadership | related subprojects |
|---|---|---|---|
| A | Data models, data acquisition and data assimilation | TU Berlin (Dr. Eigel, Prof. Hömberg, Prof. Petryna) |
TP1, TP4, TP7, TP16, TP23 |
| B | Structures and numerical design | TU Dresden (Prof. Graf, Prof. Kaliske) |
TP1, TP2, TP5, TP6, TP7, TP9, TP10, TP11, TP13, TP14, TP15, TP19, TP21, TP23 |
| C | Interactions and multi-physical behaviour considering uncertain data | RU Bochum (Dr. Freitag, Prof. Meschke) |
TP5, TP6, TP8, TP12, TP13, TP17, TP18, TP20, TP24 |
| D | Efficiency, surrogate models and reduction methods | FAU Erlangen-Nürnberg (Prof. Leyendecker, Prof. Steinmann, Prof. Willner) |
TP3, TP4, TP8, TP10, TP11, TP12, TP14, TP15, TP16, TP17, TP18, TP21, TP23, TP24 |
| E | Evaluation | TU München (Prof. Bletzinger, Prof. Duddeck, Prof. Straub) |
TP2, TP3, TP4, TP9, TP19, TP20 |
Complex A: Data models, data acquisition and data assimilation
Beside the development of efficient approaches of data acquisition the following fields are relevant for polymorphic uncertainty models:
- characterizations of problem dependent polymorphic uncertainty models
- methods for the acquisition of data out of expert knowledge
- analysis algorithms for uncertain structural characteristics
- models to assimilate uncertain quantities
Complex B: Structures and numerical design
Reliable numerical structural analysis is the basis of design. The numerical design of structures considering uncertain data in different engineering fields aims at the development of:
- structural analysis considering polymorphic uncertain quantities (spatial and time dependent)
- formulation and solution of uncertain optimization tasks
- solutions for inverse problems
Complex C: Interactions and multi-physical behaviour considering uncertain data
Numerical structural designs need the modelling of significant interactions and multi-physical behaviour. Within the SPP, developments with uncertain data are aimed to:
- couple uncertainty at different length and time scales
- develop methods for considering uncertain interactions
- create solutions for multi-physical behaviour with uncertain data
Complex D: Efficiency, surrogate models and reduction methods
The approval of numerical design methods is reachable only with efficient approaches. Therefore, it is necessary to develop:
- methods to reduce dimensionality
- approximation methods for uncertain data
- surrogate models and substructure methods considering uncertain data
The numerical design of structures considering uncertain data and information needs to be evaluated based on scientific methods. Therefore, research in the following fields is expected:
- fundamentals for the evaluation of uncertainty, calibration and validation of uncertain measurements
- methods to interpret uncertain results, suitable for engineers
- development decision supporting systems under consideration of uncertainty