### Doktorandenseminar des WIAS

## FG3: Numerische Mathematik und Wissenschaftliches
Rechnen /

*RG3: Numerical Mathematics
and Scientific Computing*/

LG5: Numerik für innovative
Halbleiter-Bauteile

*LG5: Numerics for
innovative semiconductor devices*

## Seminar Numerische Mathematik / *Numerical mathematics seminars*

aktuelles Programm / *current program*

_{Archiv}

**
Donnerstag, 27. 02. 2020, 14:00 Uhr (ESH)
**

Dr. Anton Stephan(Institut für Physik der Atmosphäre)

Modeling Navier--Stokes equations for high Reynolds number flows and complex geometries in aerodynamic applicationsThe flight of an aircraft through a turbulent environment presents a difficult numerical problem. Though it can be easily described by the Navier-Stokes equations, it is impossible to perform a direct numerical simulation resolving all physically relevant scales. For several decades, numerous different models have been developed which perform very well for very specific problems. However, since the aircraft flight includes complex geometries as well as large spatial dimensions these models fail to capture all important features of the problem.

The vision of establishing a simulation system for virtual flight in a realistic environment is addressed by the coupling of two separate flow solvers in a bi-directional manner. This enables for example the simulation of a flight through gusts, trailing vortices and clear air turbulence, including the effects on the aircraft and the roll-up of trailing vortices and their further development until final decay. A compressible Reynolds-Averaged Navier-Stokes (RANS) solver resolves the near-field around an aircraft model. An incompressible Large-Eddy Simulation (LES) solver is used to model the atmosphere around the aircraft with its wake footprint in the LES domain. This hybrid simulation setup is used to investigate complex physical problems occurring in different flight scenarios.

**
Donnerstag, 06. 02. 2020, 14:00 Uhr (ESH)
**

Dr. Holger Stephan(WIAS Berlin)

A general concept of majorization and a corresponding Robin Hood methodThe Robin Hood method is a numerical method for constructing a double-stochastic matrix that maps a given vector to another vector that is also given. This is possible if the preimage majorizes the image. The majorization of vectors is a special order sometimes called the Lorentz order. A double-stochastic matrix is a special case of a Markov (or stochastic) matrix. In the case of a general Markov matrix, it is not yet clear when such a construction is possible, what kind of condition similar to the majorization must be fulfilled by the given vectors, nor is a numerical method for determining the matrix known (regardless some heuristic iterative versions of the Robin-Hood method).

In the talk, we give a complete solution to all these problems, including a general direct Robin Hood method. It turns out that the construction of a Markov matrix from two given pairs of two vectors is possible if and only if the pairs satisfy a certain order condition. If one interprets the vectors as states of a physical system, then this order is exactly the order known as the natural time order or the second law of thermodynamics.

**
Donnerstag, 09. 01. 2020, 14:00 Uhr (ESH)
**

Lia Strenge(Technische Universität Berlin)

A multilayer, multi-timescale model approach for economic and frequency control in power grids using JuliaPower systems are subject to fundamental changes due to the increasing infeed of decentralized renewable energy sources and storage. The decentral nature of the new actors in the system requires new concepts for structuring the power grid, and achieving a wide range of control tasks ranging from seconds to days. Here we introduce a multilayer dynamical network model covering a wide range of control time scales. Crucially we combine a decentralized, self-organized low-level control and a smart grid layer of devices that can aggregate information from remote sources. The stability critical task of frequency control is performed by the former, the economic objective of demand matching dispatch by the latter. Having both aspects present in the same model allows us to study the interaction between the layers. Remarkably we find that adding communication in the form of aggregation does not improve the performance in the cases considered. Instead the self-organised state of the system already contains the information required to learn the demand structure in the entire power grid. The model introduced here is highly flexible, and can accommodate a wide range of scenarios relevant to future power grids. We expect that it will be especially useful in the context of low-energy microgrids with distributed generation. All simulations and numerical experiments for control design and analysis with sampling-based methods are performed in Julia 1.1.0. The overall model is implemented as stiff nonlinear ordinary differential equation (ODE) with periodic callbacks for the control actions. The ODE has dimension 4 N, where N is the number of edges of the graph representing the power grid (i.e., N feed-in/load connections). It is planned to use automatic differentiation to learn more about the overall nonlinear model.