One of the key research areas in the Digital Bauhaus Lab is the development and improvement of simulation methods in order to predict physical fields in structures and components. Joint research project of engineers and computer scientists will focus on new methods and algorithms along the process chain of »modelling – simulation – visualization – validation«. Within this research area, entitled as »Digital Engineering«, the integration of highly sophisticated material models on different scales into engineering models taking into account physical nonlinear material behavior, such as damage and fracture, and stochastic variations of the different input variables and an automated and adaptive model construction are resulting in large and highly nonlinear equations systems, which have to be solved in parallel on appropriate cluster hardware.
Efficient hybrid parallel solver techniques, combining computing and graphic processors can be applied to solve large nonlinear equation systems with up to 70 mio. d.o.f. on multi-core workstations. The compute cluster installed in Bauhaus-Digital Lab will enable the participating researchers to improve spatial and temporal resolution of their models and therewith improve the prediction quality of their models further.
Prof. Dr.-Ing. habil. Carsten Könke, Prof. Dr. rer. nat. Tom Lahmer, Prof. Dr.-Ing. Timon Rabczuk (Institute of Structural Mechanics)
Selected Projects
Multiscale models to predict damage and fracture behavior in technical and biological materials
Starting with high resolution image data to describe the heterogeneous material structure of either composite materials, such as concrete, or biological materials, such as human bone material, concurrent multiscale models using Finite Element Methods are generated and solved in order to predict the damage behavior of these materials.
Parallel hybrid solver strategies with reduced main memory requirements
Large linearized equation systems with several million degrees of freedom can be efficiently solved with parallel iterative solver techniques on compute clusters. In order to reduce the necessary main memory requirements of the algorithms a new method is developed which allows a matrix free solution technique. Therewith only local stiffness information is compiled and can be easily distributed among the CPU/GPU multi-core hardware. Additional efficiency is obtained by applying hybrid discretization techniques using regular grid type meshes in regions of linear-elastic behavior and aligned meshes in zones with localized damage behavior.
Large Scale Multi-physics Simulation with Cross-Correlated Random Fields
Heterogeneous material distributions in existing engineering constructions can be characterized by multi-dimensional random fields.
In multi-physics situations, e.g. coupled thermal-hydro-mechanical systems like dams, dikes or geologic deposits, there are a series of sensitive material properties which can be modeled via random fields.
These fields exhibit a certain correlation. It is taken into account by applying cross-correlated random fields based on a polymorphic uncertainty model, where the degree of cross- and spatial auto-correlation is described with vague information.
Stochastic simulations using the proposed polymorphic material description demand efficient algorithms and simulation techniques. Among others, spectral decomposition techniques, domain decomposition approaches and the application of surrogate modeling are used to facilitate a more realistic modeling approach in the field of stochastic simulations and reliability analysis.
Funding: DFG - Sachbeihilfe ,,Polymorphe Unschärfemodellierung von heterogenen thermisch-hydraulisch-mechanisch gekoppelten Systemen unter vagen Annahmen zu Korrelationen der Parameter'' (2016- 2019), Antragsteller: T. Lahmer, C. Könke. Projekt im Schwerpunktprogramm SPP 1886 Polymorphic uncertainty modelling for the numerical design of structures
Environmentally best practices and optmisation in hydraulic fracturing for shale gas/oil development
This research brings together the complementary expertise of our consortium members to gain a better understanding of the physics in hydraulic fracturing (HF) with the final goal to optimize HF practices and to assess the environmental risks related to HF. This requires the development and implementation of reliable models for HF, scaled laboratory tests and available on-site data to validate these models. The key expertise in our consortium is on modelling and simulation of HF and all partners involved pursue different computational approaches. However, we have also some partners in our consortium which focus on scaled laboratory tests and one company which can provide on-site data.
The choice of the best model for HF still remains an open question and this research promises to quantify uncertainties in each model and finally provide a guideline how to choose the best model with respect to a specific output parameter.
Funding by EU: H2020-MSCA-RISE-2016.
Prof. Dr.-Ing. Timon Rabczuk
COMBAT
Gegenstand dieses Forschungsantrages ist ein Mehr-Skalen-Ansatz zur Modellierung von quasi-sprödem Materialversagen, der die mikroskopische Rissbildung mit einem makroskopischen Ansatz koppelt. Hierbei wird auf beiden Skalen auf neue moderne Diskretisierungsverfahren zurückgegriffen, die in der Lage sind, beliebige Risspfade automatisch ohne aufwendiges Remeshing zu handhaben. Dazu sind erweiterte (extended) netzfreie Verfahren sowie die ’EXtended’ Finite Element-Methode (XFEM) vorgesehen. Es werden besondere mikromechanische Eigenschaften bezüglich des jeweils betrachteten Werkstoffes berücksichtigt. Bei dem vorgestellten Mehr-Skalen-Ansatz werden die Eigenschaften auf der mikroskopischen Skale in das makroskopische Materialverhalten eingebettet. Hierzu wird ein Homogenisierungsansatz verwendet. Homogenisierungsansätze sind bislang erfolgreich auf Problemstellungen bei gleichbleibender Mikrostruktur angewendet worden. In diesem Forschungsvorhaben wird dieser Ansatz auf ’veränderliche’ Mikrostrukturen erweitert, wie sie bei der Initiierung und Ausbreitung von Mikrorissen entstehen. Dabei ist die Beziehung der Grösse der beiden Skalen, die Korrelationslänge sowie die Elementgrösse zu berücksichtigen. Eine Hauptaufgabe wird sein, die relativen Längenskalen-Parameter der mikroskopischen und makroskopischen Skale zu identifizieren.
Prof. Dr.-Ing. Timon Rabczuk & Dipl.-Ing. Daniel Arnold