Laufende Projekte

ECMath finanziert

  • SE16

    Numerical solution of dynamic metabolic resource allocation problems for bioenergy production

    Prof. Dr. Alexander Bockmayr / Prof. Dr. Volker Mehrmann

    Projektleiter: Prof. Dr. Alexander Bockmayr / Prof. Dr. Volker Mehrmann
    Projekt Mitglieder: Dr. Markus Arthur Köbis
    Laufzeit: 01.06.2017 - 31.12.2018
    Status: laufend
    Standort: Freie Universität Berlin

    Beschreibung

    In the field of sustainable energies, microbial cell factories such as yeasts and cyanobacteria are receiving increasing interest due to their potential to produce biofuels. A major question is how the metabolism of these microorganisms is coordinated in a dynamic environment such that the correct macromolecules are synthesized at the right time in order to enable growth and survival. Recent mathematical modeling approaches have made it possible to study this problem using an optimal dynamic resource allocation formalism such as dynamic enzyme-cost flux balance analysis (deFBA). The goal of this project is to study the mathematical properties properties of the underlying optimal control problem involving differential-algebraic constraints and to develop efficient numerical solution strategies.

  • SE17

    Stochastic methods for the analysis of lithium-ion batteries

    Prof.Dr. Jean-Dominique Deuschel / Prof. Dr. Wolfgang Dreyer / Prof. Dr. Peter Karl Friz / Dr. Clemens Guhlke

    Projektleiter: Prof.Dr. Jean-Dominique Deuschel / Prof. Dr. Wolfgang Dreyer / Prof. Dr. Peter Karl Friz / Dr. Clemens Guhlke
    Projekt Mitglieder: Dr. Pierre-Ètienne Druet / Dr Mario Maurelli
    Laufzeit: -
    Status: laufend
    Standort: Technische Universität Berlin / Weierstraß-Institut

    Beschreibung

    Currently lithium-ion batteries are the most promising storage devices to store and convert chemical energy into electrical energy. An important class of modern lithium batteries contain electrodes that consist of many nano-particles. During the charging process of a battery, lithium is reversibly stored in the ensemble of the nano-particles and the particles undergo a phase transition from a Li-rich to a Li-poor phase. For this type of batteries a successful mathematical model was developed in the previous ECMath project SE8, based on a stochastic mean field interacting particle system. The new project focuses on modeling, analysis and simulations of extreme conditions in battery operation like fast charging, mostly full/empty discharge states, mechanical stresses within the electrode. The aim of the project is to achieve deeper understanding of the behavior of lithium-ion batteries in extreme conditions.

  • SE18

    Models for heat and charge-carrier flow in organic electronics

    PD Dr. Annegret Glitzky / Dr. Matthias Liero

    Projektleiter: PD Dr. Annegret Glitzky / Dr. Matthias Liero
    Projekt Mitglieder: Dr. Doan Duy Hai
    Laufzeit: 01.06.2017 - 31.12.2018
    Status: laufend
    Standort: Weierstraß-Institut

    Beschreibung

    Organic materials lead to innovative electronic components with fine-tuned properties and promise more sustainable, eco-friendly electronic technologies. The potential for greater sustainability extends across the entire life cycle of organic electronics, beginning with the use of materials that are synthesized rather than mined from the earth, over low temperature production of devices, and ending with potentially biodegradable or recyclable devices. The aim of the project is to develop a thermodynamically correct energy-drift-diffusion model for organic semiconductor devices and its discretization and implementation in a simulation tool. First, the transport of charge carriers in the isothermal case will be described on the basis of a drift-diffusion model, taking the distinctive features of organic materials into account. Second, the model will be extended in a thermodynamically consistent way to include the self-heating and the resulting feedback as well. In both points, the aspects of modeling, analysis, numerics, and simulation are considered. There are several mathematical challenges regarding a drift-diffusion description of organic devices: In organic semiconductors, the energy levels are Gaussian-distributed with disorder parameter σ such that the densities of electrons and holes are described by the Gauss-Fermi integrals. This leads to a generalized Einstein relation and results in a nonlinear diffusion enhancement in the relation between drift and diffusion current. Moreover, the mobility functions for organic semiconductor materials with Gaussian disorder are increasing with respect to temperature, carrier density, and electrical field strength. The outcome of the project is a fundamental building block for a more efficient multi-scale and multi-physics description and simulation of organic devices.

    http://www.wias-berlin.de/projects/ECMath-SE18/
  • SE19

    Optimal Network Sensor Placement for Energy Efficiency

    Dr. Carlos Rautenberg

    Projektleiter: Dr. Carlos Rautenberg
    Projekt Mitglieder: Simon Rösel
    Laufzeit: -
    Status: laufend
    Standort: Humboldt Universität Berlin

    Beschreibung

    The estimation of the temperature and airflow distribution in buildings via the location of sensor networks is a nonlinear and multiscale problem where dynamics and measurements are (in general) stochastically perturbed. The goal here is to reliably outline temperature and airflow in certain areas by placing sensors on prescribed admissible locations while optimizing several criteria. A trustworthy estimation, provided that closed-loop controllers are in place, becomes a main step in reducing the energy consumption of such control systems.

  • 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: laufend
    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
  • SE21

    Data Assimilation for Port-Hamiltonian Power Network Models

    Dr. Raphael Kruse / Prof. Dr. Volker Mehrmann / Dr. Matthias Voigt

    Projektleiter: Dr. Raphael Kruse / Prof. Dr. Volker Mehrmann / Dr. Matthias Voigt
    Projekt Mitglieder: Riccardo Morandin
    Laufzeit: -
    Status: laufend
    Standort: Technische Universität Berlin

    Beschreibung

    In this project we will study the modeling of power networks by employing the port-Hamiltonian framework. Energy based modeling with port-Hamiltonian descriptor systems has many advantages, e. g., it accounts for the physical interpretation of its variables, it is best suited for the modular structure of the network, since coupled port-Hamiltonian systems form again a port-Hamiltonian system and it encodes these properties in algebraic and geometric properties that simplify Galerkin type model reduction, stability analysis, and also efficient discretization techniques. To improve the predictions that one obtains from such models we suggest to employ data assimilation and state estimation techniques by incorporating the measurement data. These would allow to take the uncertainty in the measurements and the presence of unmodeled dynamics as well as data and modeling errors into account. The improved predictions can then be used to control the network such that (the expected value of) the load is kept as constant as possible. To control the network we propose to use techniques of model predictive control (MPC) which solve a sequence of finite horizon optimal control problems. The method uses predictions of the state and computes a local optimal control which is then used for the model simulation in the next iteration. This framework is very flexible, since it allows control in real time and the incorporation of nonlinear dynamics and/or inequality constraints. It has already been used successfully within other areas of energy network control. Our new ansatz will also incorporate the stochastic effects into the model predictive control framework using data assimilation. Our vision is to develop numerical methods for network operators that allows the incorporation of model uncertainities for improving simulation and control of power networks.

    http://www3.math.tu-berlin.de/numerik/NumMat/ECMath/SE21/
  • SE22

    Decisions in energy markets via deep learning and optimal control

    PD Dr. John Schoenmakers / Prof. Dr. Vladimir Spokoiny

    Projektleiter: PD Dr. John Schoenmakers / Prof. Dr. Vladimir Spokoiny
    Projekt Mitglieder: Alexandra Suvorikova
    Laufzeit: 01.06.2017 - 31.12.2018
    Status: laufend
    Standort: Weierstraß-Institut

    Beschreibung

    One of the main goals in this project is a systematic numerical treatment of generic optimal decision problems in real-life applications that encounter in energy markets by incorporating ``deep Learning'', a recent concept for data analysis and prediction. Also it is intended to include principles of deep learning in methods for forecasting and estimating price distribution processes in a systematic way.

  • SE23

    Multilevel adaptive sparse grids for parametric stochastic simulation models of charge transport

    Dr. Sebastian Matera

    Projektleiter: Dr. Sebastian Matera
    Projekt Mitglieder: -
    Laufzeit: 01.06.2017 - 31.12.2018
    Status: laufend
    Standort: Freie Universität Berlin

    Beschreibung

    Many computational models are stochastic and the model output needs require some sort of sampling. Besides this intrinsic stochasticity, the models usually depend on a number of uncertain parameters. We develop a multi-level adaptive sparse grid strategy to address this parametric uncertainty, where the sampling effort is adjusted to the level of the sparse grid. This methodology is applied to stochastic simulation models of charge transport, as they appear in photovoltaics and photocatalysis.

    http://www.mi.fu-berlin.de/math/groups/ag-photo/forschung/EC-Math-SE23/index.html




Anders finanziert

  • SE-AP2

    Pattern formation in systems with multiple scales

    Prof. Dr. Alexander Mielke

    Projektleiter: Prof. Dr. Alexander Mielke
    Projekt Mitglieder: -
    Laufzeit: 01.01.2011 - 31.12.2018
    Status: laufend
    Standort: Technische Universität Berlin

    Beschreibung

    Pattern formation in nonlinear partial differential equations depends on nontrivial interactions between different internal length scales and nonlinearities of the system as well as on the size and geometry of the underlying domain. The challenge is to understand how effects on the small scales generate effective pattern formation on the larger scales. Using well-chosen model problems reflecting the focus applications of the CRC, we will investigate the mathematical foundations of the derivation of effective models for pattern formation in multiscale problems. Controls for the effective models will be used to construct controls for the original system.

    http://www.itp.tu-berlin.de/collaborative_research_center_910/sonderforschungsbereich_910/project_groups/a_theoretical_methods/tp_a5/
  • SE-AP7

    Understanding of microstructure evolution in copper indium selenide (CISe) and copper gallium indium selenide (CGSe)

    Prof. Dr. Ralf Kornhuber

    Projektleiter: Prof. Dr. Ralf Kornhuber
    Projekt Mitglieder: -
    Laufzeit: 01.11.2012 - 31.12.2018
    Status: laufend
    Standort: Freie Universität Berlin

    Beschreibung

    Photovoltaic devices, which operate by directly converting solar energy into electricity, have become rapidly one of the most important “clean” energy sources. The optimization of thin-film solar cells for such use has been mainly a trial-and-error process. A detailed understanding of the relationships between growth processes, structural defects, strain, and electrical properties would benefit the development of these devices considerably. In the Virtual Institute "Microstructure control for thin-film solar cells", the formation of structural defects and related strain during the growth of thin film solar cells is investigated by combining various experimental as well as simulation approaches. The goal of this subproject is to derive vector-valued phase field models describing CISe (CIGSe) systems (Wagner, TU Berlin) together with fast, robust, and reliable numerical solution techniques (Kornhuber, FU Berlin). Suitable free energies will be determined by density functional theory (Albe, TU Darmstadt).

  • SE-AP8

    Entwicklung eines reduzierten Modells eines Pulsed Detonation Combustors

    Prof. Dr. Volker Mehrmann

    Projektleiter: Prof. Dr. Volker Mehrmann
    Projekt Mitglieder: -
    Laufzeit: 01.07.2012 - 30.06.2020
    Status: laufend
    Standort: Technische Universität Berlin

    Beschreibung

    In this project a model reduction of reactive flows is developed. Model reduction aims to replace complex, high-dimensional models by models of much smaller dimension. Goal of this project is to improve the existing techniques for systems where transport phenomena are dominant. To this end an appropriate error estimator is developed and combined with a model reduction. The small model can then be adaptively improved by adding physically motivated ansatz-functions. By this approach a low order model of a pulsed combustion is derived. This is used for control and design of a pulsed detonation combustor.

    The reduced order models shall not only describe the process of the combustion but also show the changes due to specific manipulation. The controllability in the context of mathematical fluid dynamics is determined via adjoint equations. For this the adjoint equations for reactive flows have to be differentiated and implemented.

    The reduced models are then used to design the combustor and to control the combustion process.

    https://www.sfb1029.tu-berlin.de/menue/teilprojekte/a02/parameter/en/
  • 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 - 30.09.2018
    Status: laufend
    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
  • SE-AP16

    Phase field modeling for multi-phase systems applied to the growth of Cu(In,Ga)Se2 thin films

    Prof. Dr. Barbara Wagner

    Projektleiter: Prof. Dr. Barbara Wagner
    Projekt Mitglieder: -
    Laufzeit: 01.04.2013 - 30.09.2018
    Status: laufend
    Standort: Weierstraß-Institut

    Beschreibung

    • phase field models involving surface diffusion, temperature, and elasticity for growing poly-Si film; • mathematical modeling of the evolution of In and Ga distributions during the growth of CIGSe thin films. Numerical simulations (in cooperation with PhD-6); • advanced phase field models for grain growth in ternary (CISe and CGSe) as well as for quaternary (CIGSe) thin films

    http://www.helmholtz-berlin.de/projects/hvi/microstructure/phd-projects_en.html
  • SE-AP20

    Analysis, numerical solution and control of delay differential-algebraic equations

    Prof. Dr. Volker Mehrmann

    Projektleiter: Prof. Dr. Volker Mehrmann
    Projekt Mitglieder: -
    Laufzeit: 01.01.2011 - 31.12.2018
    Status: laufend
    Standort: Technische Universität Berlin

    Beschreibung

    Delay differential-algebraic equations (DDAEs) arise in a variety of applications including flow control, biological systems and electronic networks. We will study existence and uniqueness as well as the development of numerical methods for general nonlinear DDAEs. For this, regularization techniques need to be performed that prepare the DDAE for numerical simulation and control. We will derive such techniques for DDAEs on the basis of a combination of time-differentiations and time-shifts, in particular for systems with multiple delays. We also plan to extend the spectral stability theory, i.e. the concepts of Lyapunov, Bohl and Sacker-Sell spectra, to DDAEs. We will also develop numerical methods for the computation of these spectra using semi-explicit integration methods. Another goal is to study the solution of algebraically constrained partial delay-differential equations arising in flow control and to derive discretization as well as optimal control methods in space and time.

    http://www.itp.tu-berlin.de/collaborative_research_center_910/sonderforschungsbereich_910/project_groups/a_theoretical_methods/tp_a2/
  • SE-AP25

    Optimizing metabolic regulation in yeast production strains for dynamic conditions

    Prof. Dr. Alexander Bockmayr

    Projektleiter: Prof. Dr. Alexander Bockmayr
    Projekt Mitglieder: Alexandra-M. Reimers
    Laufzeit: 01.12.2015 - 30.11.2018
    Status: laufend
    Standort: Freie Universität Berlin

    Beschreibung

    Microbial strains used in biotechnological industry need to produce their biotechnological products at high yield and at the same time they are desired to be robust to the intrinsic nutrient dynamics of large-scale bioreactors, most noticeably to transient limitations of carbon sources and oxygen. The engineering principles for robustness of metabolism to nutrient dynamics are however not yet well understood. The ROBUSTYEAST project aims to reveal these principles for microbial strain improvement in biotechnological applications using a systems biology approach. This will contribute to establishing evolutionary optimization protocols for making microbial production strains robust against dynamic nutrient conditions. The consortium will study the robustness of Saccharomyces cerevisiae in experiments during the dynamics associated with two cyclic nutrient transitions that are each of major relevance to industry: repeated cycles of glucose and ethanol growth and of aerobic and anaerobic growth. We shall monitor the physiological changes during the evolutionary adaptation of yeast to those transitions, using laboratory-evolution in lab-scale bioreactors (chemostat mode). By combining this data with computational modelling we shall identify the metabolic features that make yeast robust to these industrially relevant condition cycles. The theoretical and computational approaches that the consortium will develop involve optimisation methods applicable to metabolism transiting from one steady state to the next via dynamic regulation. We shall iterate experiments and modelling to improve our models given experimental data, to identify new measurements critical to improve our understanding, and to finally identify key regulatory mechanisms for a robust metabolism of S. cerevisiae, given changes in glucose, ethanol and oxygen concentrations. The robustness of metabolic regulation under dynamic conditions will be evaluated from the kinetic models, and the regulatory interactions that confer such robustness will be determined.

    https://www.erasysapp.eu/calls/2nd-call/robustyeast