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Since 2019, Matheon's application-oriented mathematical research activities are being continued in the framework of the Cluster of Excellence MATH+
www.mathplus.de
The Matheon websites will not be updated anymore.

Prof. Dr. Rupert Klein

Leiter AG Theoretical and Computational Fluid Dynamics

Institut für Mathematik
Arnimallee 6
14195 Berlin
+49 (0) 30 838 75 414
Rupert.Klein@math.fu-berlin.de
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Forschungsschwerpunkte

Atmosphärenströmungen, Verbrennungsgasdynamik, Mehrskalenanalyse, numerische Strömungsmechanik

Projekte als Projektleiter

  • SE20

    Exciton dynamics in organic semiconductors

    Prof. Dr. Rupert Klein

    Projektleiter: Prof. Dr. Rupert Klein
    Projekt Mitglieder: PD Dr. Burkhard Schmidt
    Laufzeit: 01.06.2017 - 31.12.2018
    Status: beendet
    Standort: Freie Universität Berlin

    Beschreibung

    The performance of optoelectronic devices, such as photovoltaic cells, is critically influenced by the transport of excitonic energy because the majority of photo-excitations occur in the bulk of the crystal from where the energy has to be transported to the interfaces with the electrodes, where charge generation often only occurs. In organic semiconductors, e.g. molecular crystals, polymer chains or dendrimers, the excitons are strongly localized, and the energy transport is normally modeled in terms of excitons diffusively hopping between sites. The present proposal aims at an improved understanding of the excitonic energy transport in organic semiconductors, which is relevant for the characterization of organic solar cells, on a microscopic basis, with emphasis on the role of the electron-phonon coupling. Using mixed quantum-classical dynamics schemes, the electronic degrees of freedom (excitons) are to be treated quantum-mechanically while the nuclear motions (phonons, molecular vibrations) are treated classically. To this end, stochastic surface-hopping algorithms shall be applied and further developed.

    https://sites.google.com/site/quantclassmoldyn/research/quantum-classical
  • SE6

    Plasmonic concepts for solar fuel generation

    Prof. Dr. Rupert Klein / Prof. Dr. Frank Schmidt

    Projektleiter: Prof. Dr. Rupert Klein / Prof. Dr. Frank Schmidt
    Projekt Mitglieder: Dr. Sven Burger / Dr. Martin Hammerschmidt
    Laufzeit: -
    Status: beendet
    Standort: Konrad-Zuse-Zentrum für Informationstechnik Berlin

    Beschreibung

    Artificial photosynthesis and water splitting, i.e. the sustainable production of chemical fuels like hydrogen and carbohydrates from water and carbon dioxide, has the potential to store the abundance of solar energy that reaches the earth in chemical bonds. Fundamental in this process is the conversion of electromagnetic energy. In photoelectrochemical water splitting semiconductor materials are employed to generate electron hole pairs with sufficient energy to drive the electrochemical reactions. In this project we investigate the use of metallic nanoparticles to excite plasmonic resonances by means of numerical simulations. These resonances localize electromagnetic nearfields which is beneficial for the electrochemical reactions. We develop electromagnetic models and numerical methods to facilite in depth analysis of these processes in close contact with our collaboration partners within the ECMath and the joint lab ``Berlin Joint Lab for Optical Simulation for renewable Energy research'' (BerOSE) between the ZIB, FU and HZB.

    http://www.zib.de/projects/plasmonic-concepts-solar-fuel-generation
  • SE-AP11

    Multiscale tensor decomposition methods for partial differential equations

    Prof. Dr. Rupert Klein / Prof. Dr. Reinhold Schneider / Prof. Dr. Harry Yserentant

    Projektleiter: Prof. Dr. Rupert Klein / Prof. Dr. Reinhold Schneider / Prof. Dr. Harry Yserentant
    Projekt Mitglieder: -
    Laufzeit: 01.10.2014 - 30.06.2018
    Status: beendet
    Standort: Freie Universität Berlin / Technische Universität Berlin

    Beschreibung

    Novel hierarchical tensor product methods currently emerge as an important tool in numerical analysis and scienti.c computing. One reason is that these methods often enable one to attack high-dimensional problems successfully, another that they allow very compact representations of large data sets. These representations are in some sense optimal and by construction at least as good as approximations by classical function systems like polynomials, trigonometric polynomials, or wavelets. Moreover, the new tensor-product methods are by construction able to detect and to take advantage of self-similarities in the data sets. They should therefore be ideally suited to represent solutions of partial differential equations that exhibit certain types of multiscale behavior.
    The aim of this project is both to develop methods and algorithms that utilize these properties and to check their applicability to concrete problems as they arise in the collobarative research centre. We plan to attack this task from two sides. On the one hand we will try to decompose solutions that are known from experiments, e.g., on Earthquake fault behavior, or large scale computations, such as turbulent flow fields. The question here is whether the new tensor product methods can support the devel­opment of improved understanding of the multiscale behavior and whether they are an improved starting point in the development of compact storage schemes for solutions of such problems relative to linear ansatz spaces.
    On the other hand, we plan to apply such tensor product approximations in the frame­work of Galerkin methods, aiming at the reinterpretation of existing schemes and at the development of new approaches to the ef.cient approximation of partial differential equations involving multiple spatial scales. The basis functions in this setting are not taken from a given library, but are themselves generated and adapted in the course of the solution process.
    One mid-to long-term goal of the project that combines the results from the two tracks of research described above is the construction of a self-consistent closure for Large Eddy Simulations (LES) of turbulent flows that explicitly exploits the tensorproduct approach’s capability of capturing self-similar structures. If this proves successful, we plan to transfer the developed concepts also to Earthquake modelling in joint work with partner project B01.

    http://sfb1114.imp.fu-berlin.de/research/index.php?option=com_projectlog&view=project&id=8