Research
Research projects

Current Research Projects at the IBNM

  • Computational mechanics in terms of mixed aleatory and epistemic uncertain random fields
    This project investigates probability box (p-box) approach including probabilistic and possibilistic aspects. This way, both kind of uncertainties - aleatory and epistemic - can be considered within a random field.
    Leaders: Udo Nackenhorst, Amélie Fau
    Team: Mona Madlen Dannert, Rodolfo Fleury
    Year: 2016
    Sponsors: Priority Programme SPP 1886 of German Research Foundation (DFG), State of Lower Saxony
  • Reduced Order Modelling in Non-Linear Structural Mechanics
    Finite Element Methods are well established in structural mechanics, however, in many engineering applications fast numerical evaluations of parametric solutions are required, e.g. for optimisation, sensitivity analysis or uncertainty quantification. Model Order Reduction (MOR) are currently under investigation for drastically reduction of the computational effort in comparison to FEM simulations. However, to this point no clear guidelines on the treatment of non-linearity have been developed. In this project kernel based methods will be investigated with regard to their performance on tackling non-linear structural dynamics problems, in particular for structural failure to loss of material resistance. A goal oriented comparison of different branches of kernel-based methods, i.e. kernel POD, support vector regression and Kriging with special emphasis on damage and plasticity is performed.
    Leaders: Udo Nackenhorst
    Team: Steffen Funk
    Year: 2017
  • Meso-scale finite element modeling of concrete damage under fatigue loading
    Within the scope of this project, the mechanism of concrete damage under cyclic loading conditions will be invistigated at the meso-scale. At this scale, concrete will be considered as non-homogeneous three-phase composite material which consists of cement matrix (mortar), aggregates and interfacial transition zone (ITZ).
    Leaders: Udo Nackenhorst
    Team: Mohammed Hammad
    Year: 2018
    Sponsors: DAAD (German Academic Exchange Service)
  • Development of a Coupled BCHM-Model for Numerical investigations of MICP treatment of soil
    Microbially induced calcite precipitation (MICP) offers the potential for the development of environmentally friendly and cost-effective solutions to a wide range of geotechnical engineering problems, from “improvement of the soft underground” to “control of groundwater contamination”.
    Leaders: Udo Nackenhorst
    Team: Xuerui Wang
    Year: 2020
    Sponsors: German Research Foundation (DFG)
    Lifespan: 2020-2022
    Figures: Schematic view of the relevant processes in MICP (left) and the BCHM couplings (right) Figures: Schematic view of the relevant processes in MICP (left) and the BCHM couplings (right)
  • A Consistent Hybrid Approach with Machine Learning for Time Dependent Constitutive Modelling
    Machine learning is currently uncovering new possibilities in data-driven and meta-modelling for the field of computational mechanics. In particular, material modelling can be augmented or completely replaced with experimental results. Such techniques exist in the literature, such as data-driven material modelling.
    Leaders: Udo Nackenhorst
    Team: Darcy Beurle
    Year: 2020
  • Variational multiscale based data driven modeling of subgrid scales for fluid mechanics applications
    In recent work, we have cast various non-standard finite element formulations for advection based PDEs in the variational multiscale framework. Examples are Nitsche’s formulation and discontinuous Galerkin methods. This formalism has revealed the implicit multiscale decomposition inherent to these methods. Now, we extend upon this formalism and aim to develop localization techniques for the remaining fine scale interaction. Even after localization, determining the true scale interaction remains computationally expensive. During an offline stage, we produce training data of precise scale interaction for ranges of element shapes and underlying advective fields. A trained machine learning algorithm is used during the online phase to approximate the interaction with the unresolved scales, i.e., the turbulent subgrid scales. Our goal is thus to develop a highly capable, data driven, turbulence modeling tool with a mathematically rigorously foundation.
    Leaders: Dominik Schillinger
    Team: Stein Stoter
    Year: 2020
    Sponsors: Deutsche Forschungsgemeinschaft (DFG) via the Emmy Noether Award SCH 1249/2-1
    Field of influence of the fine scales for a classical method (left) and a discontinuous Galerkin method (right). Field of influence of the fine scales for a classical method (left) and a discontinuous Galerkin method (right).
  • Concurrent material and structure optimization of multiphase hierarchical systems
    In this project, we develop a concurrent material and structure optimization framework for hierarchical systems that relies on continuum micromechanics estimates for multiscale analysis. The analytical nature of these estimates enables simple constraint optimization problems at the material level that are essentially independent of the number of hierarchical scales, rendering our framework computationally tractable for multiphase hierarchical systems. After successfully establishing the framework for overall linear elastic behavior, we are currently working on extending our framework to inelasticity that originates from the material microscales in hierarchical systems. The methodology developed in this project could open up new possibilities for genetic tailoring of plant materials, multiscale bone remodeling, or fabrication of bioinspired engineering materials.
    Leaders: Dominik Schillinger
    Team: Tarun Gangwar
    Year: 2020