Here you can find upcoming and past events organized by our group.
16:00SC Seminar Room 32-349
Robust Nonconvex PDE-Constrained Optimization based on Second Order Approximation Techniques and Reduced Order Models
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.
11:30SC Seminar Room 32-349
Lisa Kusch, SciComp
Topology Optimization of Structures using the One-Shot Approach – Challenges and First Steps
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.
11:30SC Seminar Room 32-349
Ole Burghardt, SciComp
Conjugate heat transfer support within SU2
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.
09:00 – 17:00TU Kaiserslautern
The aim of the summer school is to give an overview on how PDE-constrained optimization problems in computational fluid dynamics can be addressed using the modern open-source solver SU2 together with an adjoint approach.
The lectures will be complemented by a set of tutorials that will enable the participants to develop and contribute own code for their own specific optimization problems. This also includes changes and additions to the primal solvers.
You can find more detailed information in our summer school flyer.