Research

By now simulation is one of the most important and in many cases the only viable technique for analysis and optimization of large networks. Telecommunication networks, traffic and logistic networks and energy networks have much in common. The complexity of the networks with its many parallel existing nodes and the flows between nodes is difficult to comprehend and often cannot be controlled with other techniques than simulation. The actual behavior of such a network is often different from the assumed behavior. The construction, operation, modification and optimization of such networks usually represent an infrastructure task that is associated with considerable costs. To avoid undesirable developments, simulation is used as an important tool to determine the properties of a network, the behavior of the critical performance characteristics and parameters at an early stage.

The advances in material science have always defined the state of development of a society. Material science is one of the key topics in the german industrial landscape which provides the basis for many innovations in other areas of industry.

In materials science, a highly interdisciplinary approach is already astablisched, which is divied into the strongly overlapping areas of "Computational Materials Science", "Computational Physics" and "Computational Chemistry". Simulations have developed into a living and research-oriented scientific branch in material science and their neighboring natural sciences, which is also increasingly perceived and actively promoted by the industry.

Problems in material sciences are very diverse and are typically based on multiple lengths and time scales. Therefore, the field of material simulations is characterized by a variety of different simulation methods, which are tailored to the respective problem class:

On the smallest scale, atomic processes are simulated parameter-free on the basis of natural laws in so-called ab-initio simulations. These quantum mechanics methods usually require high-performance computers, but allow to achieve a variety of quantitative results. Such a program package is developed and distributed in Clausthal.

On the macroscopic length scale, the material is considered to be a continuum whose behavior is determined by material parameters that are either known from an experiment or determined by more basic simulations. Since the simulation techniques are largely uniform here, particularly commercial program packages are used. The main interest here is the modeling of the problem and the determination of the parameters and equations as well as the verification and visualization of the results.

Between these poles, there are a number of other methods. Some will be listed as following: Monte Carlo methods, molecular mechanics, molecular dynamics, phase field theories, microstructural simulation of dislocation networks and grain boundaries.

Therefore one of the "Grand Challenges in Computational Materials Science" is the multiscale simulation, which ideally extends from ab initio simulation to the simulation of forming processes and manufacturing processes. On the one hand, this challenge is addressed by the fact that simulation parameters are deliberately transmitted from the microscopic simulations to the macroscopic simulations. On the other hand, different simulation methods are integrated into a uniform simulation environment in order to describe effects in which different length and time scales can no longer be decoupled. These activities require the cooperation of different scientific disciplines and benefit from mergers as planned at Simulation Science Center.

In contrast to several other applications of simulation methods, simulations in the field of material science are very often characterized by the fact that they have to exploit all available information processing resources to their limits in order to achieve usable results. Questions of algorithmic complexity and efficiency of implementations are therefore of crucial importance for the progress of the simulation methods in this area.

Numerical simulation of technical and scientific problems traditionally is one of the disciplines with the highest demand of computing power. Accordingly, such problems are processed on supercomputers with vector and parallel computing architecture. The most powerful parallel computers in the world have been installed in the US for simulation applications. The most powerful parallel computers in Germany together have only a fraction of the power of these machines and are concentrated at three sites in Munich (LRZ and RZG), Stuttgart (HLRS) and Jülich (FZ). In Niedersachsen there are HLRN II (North German Network for High-Performance Computing) and some smaller systems in university computer centers available for large simulation tasks. To get even more computing power, it is necessary to split the numerical problems so they can be calculated on the existing hardware infrastructure at universities and their data centers. Unfortunately generally the effort needed to create the software and middleware that sits between applications and operating systems is very high and there are enormous constraints on the applications. Therefore it has paid only in individual cases, to distribute simulations on multiple PC clusters so far. With the advent of Grid Computing is becoming apparent, that the cost of the distribution of simulation applications comes on existing PCs and workstations in an area where it will be interesting for many users.

In this project area we will examine models and methods with which simulations can be distributed on a grid and the resulting problems of software testing and quality assurance can be solved. "Distributed Simulation" also means the simulation of highly distributed real systems, such as Supply chains, supply chain management, which cannot be detected by a conventional, closed model.