Scientific Computing Seminar

Date and Place: Thursdays in Room 32-349. For detailed dates see below!

Content

In the Scientific Computing Seminar we host talks of guests and members of the SciComp team as well as students of mathematics, computer science and engineering. Everbody interested in the topics is welcome.

List of Talks

  • Thu
    20
    Apr
    2017

    11:30SC Seminar Room 32-349

    Dr. Josef Schüle, SciComp/RHRK

    Title:
    Using GPUs in SU2 code

    Abstract:

    GPUs may be used to reduce the required wall clock time in SU2 calculations. In this talk some early experiments on the usage of one GPU in implicit RANS calculations with SU2 are given. As the parallelism degree for the calculations is rather limited, one CPU thread will not be able to saturate several or even one GPU. Therefore this work focusses on sharing one or few GPUs by several threads, instead. Currently one step in the preconditioning of the linear equation solver has been analyzed there classical Gauss-Elimination of very small dense matrices has to be performed for all nodes repeatedly. Preliminary  results and ideas for further GPU usage will be given.

  • Mon
    08
    May
    2017
    Wed
    10
    May
    2017

    Venue: Computing Center (ZDV) at JGU Mainz

    Within the framework of the DFG Project EPE (Enabling Performance Engineering), the SciComp Team co-organizes a workshop at JGU Mainz. The workshop is open for everybody, especially the participants of the Scientific Computing Seminar. Just contact the SciComp Team for further information!

    The topics include:

    • Algorithmic Differentation: Theory and CoDiPack (TU Kaiserslautern/TU Darmstadt)
    • Vc: SIMD types for C++ — portable and intuitive data parallelism (Uni Frankfurt)
    • Improving Collective I/O Performance Using Non-Volatile Memory Devices (JGU Mainz)
    • Introduction and Tutorials Extra-P und Score P (TU Darmstadt)
  • Fri
    02
    Jun
    2017

    14:00SC Seminar Room 32-349

    Dr. Emre Özkaya, SciComp

    Title:
    Robust Aerodynamic Shape Optimization Using Adjoint Assisted Surrogate Modeling

    Abstract:

    In the present work, we present an hybrid optimization framework for robust aerodynamic shape optimization. The suggested method combines a Kriging (also known as Gaussian process regression) based surrogate model with an adaptive sampling strategy assisted by the gradient information obtained from a discrete adjoint solver. In this way, it is possible to incorporate the uncertainties in design variables into the optimization algorithm. The feasibility of the suggested method is demonstrated by a comparative design optimization study using the benchmark test cases of the open source CFD software SU2.

  • Tue
    13
    Jun
    2017

    11:30SC Seminar Room 32-349

    Prof. Charles Swanson, NASA Langley Research Center and Old Dominion University, USA

    Title:
    Transport Equations: Mathematical and Discrete Issues

    Abstract:

    There is considerable effort to solve the problem of well-posedness, regularity, and global existence for the Navier-Stokes equations. While progress has been made for some particular cases, this Millennium problem remains unsolved in general. These issues also apply to the transport equation or equations for modeling the effects of turbulence. Without this mathematical foundation, one cannot know a priori if there exist a solution or possibly multiple solutions. Currently, scientists and engineers generally rely only upon numerical demonstrations to determine if there exist a realistic eddy viscosity from turbulence models, which primarily depends on solving transport equations. In this presentation, the mathematical and discrete issues concerning a representative two-equation turbulence model are examined. The challenges for convergence in steady state problems as well as unsteady problems, when considering a dual time-stepping algorithm, are discussed. Although the focus will be on a two-equation model, some representative convergence behaviors of a full Reynolds Stress Model (RSM) are also presented. Results are shown for three different airfoil cases at different flow conditions, which includes transonic flow conditions. Various issues in verification
    of turbulence models are also briefly considered. A perspective on requirements for an effective and efficient numerical algorithm to solve the transport equations is also examined. In particular, the following issues are considered: (1) stiffness of the governing transport equations, (2) boundary conditions, (3) positivity and realizability, (3) boundary conditions, (4) strong solution algorithms, (5) linear and nonlinear stability, (6) analysis concerning behavior of the transport equations.

    A pdf version of the slides can be found here.

  • Thu
    22
    Jun
    2017

    11:30SC Seminar Room 32-349

    Prof. Marc Pfetsch, Research Group Optimization, TU Darmstadt

    Title:
    Compressed Sensing and Discrete Optimization

    Abstract:

    The goal of this talk is to give an overview on compressed sensing from an discrete optimization/geometry point of view. The main problem in compressed sensing – the sparse representation problem – is to find the sparsest solutions of underdetermined linear equation systems. This problem is computationally hard, but can be efficiently solved by a linear program if certain conditions are satisfied. The talk will review some well known conditions and show that they are computationally hard to check. Moreover, the solution of this problem to global optimality will be discussed. The next step consists of presenting results on the unique recovery of integer solutions. Of course, even obtaining some feasible solution is hard in this case. Nevertheless, the potential of investing computational resources to obtain optimal solutions will be discussed.

  • Tue
    27
    Jun
    2017

    16:00SC Seminar Room 32-349

    Prof. Stefan Ulbrich, Nonlinear Optimization, TU Darmstadt

    Title:
    Robust Nonconvex PDE-Constrained Optimization based on Second Order Approximation Techniques and Reduced Order Models

    Abstract:

    We consider robust optimization techniques for nonconvex PDE-constrained problems involving uncertain parameters. The parameters are assumed to be contained in a given uncertainty set. This type of robust optimization problems are difficult to treat computationally and hence suitable approximations and solution methods are required. We propose and investigate an approximate robust formulation that employs a quadratic approximation (or only a linear approximation when appropriate) and can be solved efficiently by using a full-space formulation as mathematical program with equilibrium constraints (MPEC) or a reduced formulation. Moreover, we consider the application of reduced order models with a posteriori error estimation within the optimization method to reduce the number of required PDE-solves during the optimization.
    We show applications to the robust geometry optimization of a permanent magnet synchronous motor and to the robust geometry optimization of load-carrying structures governed by the elastodynamic equations.

    This is joint work with Oliver Lass and Philip Kolvenbach, TU Darmstadt.

  • Thu
    06
    Jul
    2017

    11:30SC Seminar Room 32-349

    Lisa Kusch, SciComp

    Title:
    Topology Optimization of Structures using the One-Shot Approach – Challenges and First Steps

    Abstract:

    The one-shot approach is traditionally used in the context of shape optimization with an underlying expensive partial differential equation constraint. If the solution process for the partial differential equation can be interpreted as a fixed point iteration, it can be augmented with an adjoint solver. Then, in the one-shot approach state and adjoint feasibility are pursued simultaneously with optimality using a suitable preconditioner.
    We transfer the ideas of one-shot optimization to the field of topology optimization. The structural analysis involving geometrical and material non-linearities is realized with a Newton-like solver, that can be augmented by an adjoint solver. Several new challenges for one-shot topology optimization like projection methods and filter methods are discussed. Results are presented for topology optimization of nonlinear elastic structures in a two-dimensional setting to minimize compliance.

  • Wed
    12
    Jul
    2017

    11:30SC Seminar Room 32-349

    Ole Burghardt, SciComp

    Title:
    Conjugate heat transfer support within SU2

    Abstract:

    Computing estimations for heat fluxes from a heated solid’s wall into a cooling fluid flow is based on a good assumption of the wall’s temperature distribution. If such a distribution is not available, one in fact has to solve a coupled problem: The heating of the coolant fluid flow and the corresponding cooling of the solid itself, commonly called conjugate heat transfer or “CHT”.
    In this talk, we will give an introduction to SU2’s recently developed multi-zone driver with CHT support and a simple test case so that one can quantify the errors that are made between a guessed (isothermal) temperature distribution and the physically more accurate one obtained by the coupling.

    The specialty of our implementation then is its entire integration into the (heat and fluid flow) solver’s fixed point formulation that is used throughout in SU2. This way we can automatically solve the discrete adjoint problem of the coupled PDEs, resulting in physically accurate heat flux sensitivities for shape optimizations which we will address in the second half of the talk.

  • Thu
    20
    Jul
    2017

    11:30SC Seminar Room 32-349

    Tim Albring, SciComp

    Title:
    Constrained-based Shape Parameterization

    Abstract:

    The ability to drive a baseline shape to one with superior performance hinges on the quality of the shape parameterization. On the one hand it is often useful to delimit the design space, both to simplify the optimization problem and to avoid exploring regions known to be off-limits, often for non-aerodynamic reasons. On the other hand, pre-set deformation mechanisms can restrict the design space in irrelevant ways, needlessly hindering the discovery of valid designs. Deliberate, informed tailoring of the design space to individual problems is crucial for effective design.

    In this talk we discuss a technique for creating custom, design-appropriate shape parameterizations of discrete surfaces. The idea is to leverage the smooth deformation properties of the Free-Form deformation, but to automate them and remove them from the designer’s direct control. Instead, the designer directly selects and manipulates any number of points on the surface and prescribes geometric constraints, while an automated system solves for a smooth deformation of the remainder of the surface that satisfies the constraints. Several applications in the field of internal and external flows will be presented, along with a comparison with traditional approaches. Furthermore the workflow in SU2 to set up an optimization using these features from the user’s perspective will be shown.