Refine
H-BRS Bibliography
- yes (7) (remove)
Departments, institutes and facilities
- Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE) (7) (remove)
Document Type
- Article (3)
- Conference Object (3)
- Contribution to a Periodical (1)
Year of publication
- 2013 (7) (remove)
Keywords
- GROW (1)
- atomistic models (1)
- derivative-free optimization (1)
- force field parameterization (1)
- force fields (1)
- ionic liquids (1)
- molecular dynamics (1)
- molecular simulations (1)
- quantum mechanics (1)
- smoothing procedures (1)
Qualitätsverbesserung und Zeitersparnis bei der Stipendienvergabe durch automatisierten Workflow
(2013)
Für die Vergabe der Deutschlandstipendien hatte die Hochschule anfangs ein Verfahren festgelegt, das viel manuelle Arbeitsschritte umfasst: Die Studierenden hatten ihre Bewerbungsunterlagen schriftlich einzureichen. Dazu gehörten neben einem Motivationsschreiben, einem Ausdruck des aktuellen Notenspiegels alle weiteren Referenzen zur Einschätzung der Bewerbung gemäß den gesetzlichen Auswahlkriterien. Als Grundlage zur Bewertung der „sozialen Kriterien“ sollten die Bewerberinnen und Bewerber ein Gutachten eines Professors oder einer Professorin der Hochschule einholen.
Molecular modeling is an important subdomain in the field of computational modeling, regarding both scientific and industrial applications. This is because computer simulations on a molecular level are a virtuous instrument to study the impact of microscopic on macroscopic phenomena. Accurate molecular models are indispensable for such simulations in order to predict physical target observables, like density, pressure, diffusion coefficients or energetic properties, quantitatively over a wide range of temperatures. Thereby, molecular interactions are described mathematically by force fields. The mathematical description includes parameters for both intramolecular and intermolecular interactions. While intramolecular force field parameters can be determined by quantum mechanics, the parameterization of the intermolecular part is often tedious. Recently, an empirical procedure, based on the minimization of a loss function between simulated and experimental physical properties, was published by the authors. Thereby, efficient gradient-based numerical optimization algorithms were used. However, empirical force field optimization is inhibited by the two following central issues appearing in molecular simulations: firstly, they are extremely time-consuming, even on modern and high-performance computer clusters, and secondly, simulation data is affected by statistical noise. The latter provokes the fact that an accurate computation of gradients or Hessians is nearly impossible close to a local or global minimum, mainly because the loss function is flat. Therefore, the question arises of whether to apply a derivative-free method approximating the loss function by an appropriate model function. In this paper, a new Sparse Grid-based Optimization Workflow (SpaGrOW) is presented, which accomplishes this task robustly and, at the same time, keeps the number of time-consuming simulations relatively small. This is achieved by an efficient sampling procedure for the approximation based on sparse grids, which is described in full detail: in order to counteract the fact that sparse grids are fully occupied on their boundaries, a mathematical transformation is applied to generate homogeneous Dirichlet boundary conditions. As the main drawback of sparse grids methods is the assumption that the function to be modeled exhibits certain smoothness properties, it has to be approximated by smooth functions first. Radial basis functions turned out to be very suitable to solve this task. The smoothing procedure and the subsequent interpolation on sparse grids are performed within sufficiently large compact trust regions of the parameter space. It is shown and explained how the combination of the three ingredients leads to a new efficient derivative-free algorithm, which has the additional advantage that it is capable of reducing the overall number of simulations by a factor of about two in comparison to gradient-based optimization methods. At the same time, the robustness with respect to statistical noise is maintained. This assertion is proven by both theoretical considerations and practical evaluations for molecular simulations on chemical example substances.
The simulation of fluid flows is of importance to many fields of application, especially in industry and infrastructure. The modelling equations applied describe a coupled system of non-linear, hyperbolic partial differential equations given by one-dimensional shallow water equations that enable the consistent implementation of free surface flows in open channels as well as pressurised flows in closed pipes. The numerical realisation of these equations is complicated and challenging to date due to their characteristic properties that are able to cause discontinuous solutions.
Cost efficient energy monitoring in existing large buildings demands for autonomous indoor sensors with low power consumption, high performance in multipath fading channels and economic implementation. Good performance in multipath fading channels can be achieved with noncoherent chaotic modulation schemes such as chaos on-off keying (COOK) or differential chaos shift keying (DCSK). While COOK stands out in the area of power consumption, DCSK excels when it comes to its performance in noisy and multipath fading channels. This paper evaluates a combination of both schemes for autonomous indoor sensors. The simulation results show 50% less power consumption than DCSK and more than 3dB SNR gain in Rayleigh fading channels at BER=10-3 as compared to COOK, making it a promising candidate for low power transmission in autonomous wireless indoor sensors. We further present an enhanced version of this scheme showing another 1 dB SNR improvement, but at 25% less power consumption than DCSK.