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Veranstaltungskalender

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<< 2017

Veranstaltungskalender 2018

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Seite 1 von 2 12 >>

  • April
  • 20. April
    16:00 - 17:30
    Ort: 48-208
    Graduate School

    Persch, Johannes: Optimization Methods in Manifold-Valued Image Processing

  • 17. April
    13:45 - 15:15
    Ort: 48-210
    Graduate School

    Rodriguez Cruz, Yolanda Rocio: Model Order Reduction for Stochastic and Bilinear Systems

  • 13. April
    15:00 - 16:30
    Ort: 48-208
    Graduate School

    Korell, Philipp: Combinatorics of Valuations on Curve Singularities

  • März
  • 23. März
    16:00 - 17:30
    Ort: 48-210
    Graduate School

    Faltings, Ulrike: On the Characters of the Sylow 2-Subgroup of F4(2n) and Decomposition Numbers

  • 23. März
    13:00 - 14:30
    Ort: 48-210
    Graduate School

    Küsters, Ferdinand: Switch observability for differential-algebraic systems: Analysis, observer design and application to power

  • 01. März
    11:30 - 13:00
    Ort: 32-349

    D.Baumgärtner,TU Munich:Node-based shape optimization including CAD-reconstruction within an open-source multiphysics framework

    The current state of the art in shape optimization is dominated by approaches utilizing computer-aided design (CAD) or morphing boxes. On the contrary, node-based approaches have not reached the same industrial acceptance, although e.g. the Vertex Morphing Method showed with many practical problems promising characteristics like high optimization potential, minimum modeling effort or fast design space exploration. One major reason for this limited popularity is the missing link to CAD being the primary design tool in many industrial branches.
    Within this seminar, an approach is presented to close this gap. The focus is put on the seamless integration of the Vertex Morphing Method in given CAD workflows as well as the resulting design advantages. The applicability of the combined optimization and reconstruction process is demonstrated with selected examples from different industrial branches. In all examples, an application for shape optimization within the open-source multiphysics framework “KratosMultiphysics” is utilized. Its close link the popular open-source CFD code “SU2” will be discussed throughout the seminar.

  • Januar
  • 25. Januar
    17:00 - 18:00
    Ort: 48-436
    AG Algebra, Geometrie und Computer Algebra

    Petra Schwer, KIT Karlsruhe: Reflection length in affine Coxeter groups

    Affine Coxeter groups have a natural presentation as reflection groups on some affine space. Hence the set R of all its reflections, that is all conjugates of its standard generators, is a natural (infinite) set of generators. Computing the reflection length of an element in an affine Coxeter group means that one wants to determine the length of a minimal presentation of this element with respect to R. In joint work with Joel Brewster Lewis, Jon McCammond and T. Kyle Petersen we were able to provide a simple formula that computes the reflection length of any element in any affine Coxeter group. In this talk I would like to explain this formula, give its simple uniform proof and allude to the geometric intuition behind it.

  • 19. Januar
    13:45 - 15:15
    Ort: 48-210
    Graduate School

    Lo, Pak Hang: An Iterative Plug-in Algorithm for Optimal Bandwidth Selection in Kernel Intensity Estimation for Spatial Data

  • 18. Januar
    17:00 - 18:00
    Ort: 48-436
    AG Algebra, Geometrie und Computer Algebra

    Florian Eisele, University of London: A counterexample to the first Zassenhaus conjecture

    There are many interesting problems surrounding the unit group U(RG) of the ring RG, where R is a commutative ring and G is a finite group. Of particular interest are the finite subgroups of U(RG). In the seventies, Zassenhaus conjectured that any u in U(ZG) is conjugate, in the group U(QG), to an element of the form +/-g, where g is an element of the group G. This came to be known as the "Zassenhaus conjecture". In recent joint work with L. Margolis, we were able to construct a counterexample to this conjecture. In this talk I will give an introduction to the various conjectures surrounding finite subgroups of U(RG), and how they can be reinterpreted as questions on the (non-)existence of certain R(GxH)-modules, where H is another finite group. This establishes a link with the representation theory of finite groups, and I will explain how, p-locally, our example is made up of certain p-permutation modules.

  • 18. Januar
    11:30 - 13:00
    Ort: 32-349

    Dr. Görtz, DLR: Surrogate and Reduced-Order Models for Use in Aerodynamic Applications, MDO and Robust Design

    Reduced Order Models (ROMs) have found widespread application in fluid dynamics and aerodynamics. In their direct application to Computational Fluid Dynamics (CFD) ROMs seek to reduce the computational complexity of a problem by reducing the number of degrees of freedom rather than simplifying the physical model. Here, parametric nonlinear ROMs based on high-fidelity CFD are used to provide approximate flow solutions, but at lower evaluation time and storage than the original CFD model. ROMs for both steady and unsteady aerodynamic applications are presented. We consider ROMs combining proper orthogonal decomposition (POD) and Isomap, which is a manifold learning method, with surrogate-based interpolation methods as well as physics-based ROMs, where an approximate solution is found in the POD-subspace by minimizing the corresponding steady or unsteady flow-solver residual. The issue of how to best “train” the ROM with high-fidelity CFD data is also addressed. The goal is to train ROMs that yield a large domain of validity across all parameters and flow conditions at the expense of a relatively small number of CFD solutions. The different ROM methods are demonstrated on a wide-body transport aircraft configuration at transonic flow conditions.
    In the second part of this talk we present a robust design optimization framework for aircraft design and show results for robust aerodynamic design. As a first step, we focus on quantifying uncertainties in the drag coefficient using non-intrusive methods. To reduce the computational effort required to compute the output uncertainties we make use of a Sobol sequence-based quasi Monte Carlo method (QMC) and a gradient-enhanced Kriging (GEK) surrogate model. A small number of samples is computed with the full-order CFD code TAU and its adjoint version to construct the GEK model. The statistics are computed by interrogating the surrogate model with a QMC method using a sufficiently large number of samples. In terms of the input uncertainties, we are interested both in operational and geometrical uncertainties. Our strategy to model the inherently large number of geometrical uncertainties is by using a truncated Karhunen-Loève expansion (tKLE), which introduces some elements of model uncertainty. Then, a Subplex algorithm is used to optimize different robustness measures. The test case used here to demonstrate the framework is a transonic RAE2822 airfoil.
    Finally, current work aiming to extend our framework for uncertainty quantification and management (UQ&M) based on high-fidelity CFD to the loads process, especially at extremes of the flight envelope.

Seite 1 von 2 12 >>