Current Projects

ALOP — Algorithmic Optimization

Research Training Group (GRK 2126)

Funding AgencyDeutsche Forschungsgemeinschaft (DFG)
SpeakerProf. Dr. Volker Schulz
Principal InvestigatorProf. Dr. Nicole Marheineke
ParticipantsMarc Harmening, Lukas Mich, Britta Schmitt
Dates2017 – 2025

Mathematical optimization is a discipline of high importance for economy, science, industry, policy support, and the public and private sector. Due to practical demand and the rise of computing capabilities, experts proficient in this field are needed in research and development. This research training group (RTG) therefore focuses on education in the field of algorithmic optimization.

MathEnergy — Mathematical key techniques for energy networks in a state of change

Subproject: Analysis and application of reduced models

Funding AgencyFederal Ministry for Economic Affairs and Energy (BMWi)
PartnerFraunhofer SCAI, Fraunhofer ITWM, Max-Planck-Institut Magdeburg, TU Berlin, TU Dortmund, HU Berlin, PSI AG
ParticipantsProf. Dr. Nicole Marheineke, Björn Liljegren-Sailer, Nadine Stahl
Dates2016 – 2021

The main objective of this mathematically oriented subproject is the development of methods and analysis of reduced models or model hierarchies and their application to dynamic state estimations for model-predictive control.

proMT — Prognostic model-based online MR-thermometry during minimally invasive thermoablation for the treatment of liver tumors

Subproject: Model reduction for prognostic online MR-thermometry

Funding AgencyFederal Ministry of Education and Research (BMBF)
PartnerTU Kaiserslautern, Fraunhofer ITWM, In­sti­tute for Diagnostic and Interventional Radiology Frankfurt University Clinic, Siemens Healthcare GmbH
ParticipantsProf. Dr. Nicole Marheineke, Kevin Meligan
Dates2016 – 2020

In view of the intended prognostic online simulation capability, this subproject aims at the development and analysis of reduced models by means of model order reduction (MOR) techniques. The combination of MOR and space-mapping promises a further increase in performance, which is assessed qualitatively and quantitatively for the specific application of MR-thermometry.

Completed Projects

Simulation of highly dynamic turbulence-driven spinning processes

Funding AgencyGerman Research Foundation (DFG)
PartnerFraunhofer ITWM
ParticipantsProf. Dr. Nicole Marheineke, Manuel Wieland, Stefan Schießl
Dates2014 – 2018

The objective of this project is the simulation of highly dynamic spinning processes, geared to the key challenge of understanding the melt-blowing process with its complex dependency on the driving turbulent hot air streams together with the arising large stretching of the liquid fiber jet. For the underlying instationary viscous Cosserat rod model, system of partial and ordinary differential equations with random source term, numerical schemes are developed with focus on appropriate refinement strategies.

Mathematical modeling, simulation and optimization at the example of gas networks

TP C02: Hierarchical PDAE-surrogate models and stable PDAE-discretiziation for simulating large instationary gas networks

Funding AgencyCollaborative Research Centre (SFB) / Transregio 154
PartnerFAU Erlangen-Nürnberg, HU Berlin, TU Berlin, TU Darmstadt, ZIB, WIAS, Univ. Duisburg-Essen
ParticipantsProf. Dr. Nicole Marheineke, Björn Liljegren-Sailer, Prof. Dr. Caren Tischendorf (HU Berlin)
Dates2014 – 2018 (Ausscheiden wegen Universitätswechsel)

The online (possible real-time) control and parametric optimization of gas transport requires the stabil and fast simulation of large instationary networks that are modeled by huge coupled systems of partial differential and algebraic equations. This project aims at the development of an appropriate numerical discretization adapted to the network topology and in regard of a small perturbation index. Establishing a PDAE-surrogate model hierarchy by using model order reduction techniques promises to yield an efficient compromise between complexity and accuracy.

OPAL – Optimization of airlay-processes

TP1: Kinetic modeling and simulation of fiber-loaded flows

Funding AgencyFederal Ministry of Education and Research (BMBF)
PartnerFAU Erlangen-Nürnberg, TU Kaiserslautern, Fraunhofer ITWM, Autefa Solutions Germany GmbH, IDEAL Automotive GmbH
ParticipantsProf. Dr. Nicole Marheineke, Alexander Vibe
Dates2013 – 2016

The manufacturing of modern lightweight components is often based on aerodynamic laydown processes. In the airlay-process a porous structure is formed by a fiber-loaded air flow on a conveyor belt and then bonded. The material properties of the finalized product, such as structural strength or bulk modulus, depend on the parameters of the production process. OPAL aims the optimization of the production process and the material properties on basis of the consistent mathematical description of the process chain. The focus of this subproject lies on the kinetic modeling of the fiber-loaded flow.

Mathematical Modeling and Numerical Simulation of Steel Rolling Processes

FundingSiemens AG
ParticipantsProf. Dr. Nicole Marheineke, Kevin Meligan
Dates2013 – 2014

ProFil – Stochastic production processes in manufacturing of filaments and nonwoven materials

TP3: Turbulent fiber and fluid dynamics

Funding AgencyFederal Ministry of Education and Research (BMBF)
PartnerTU Kaiserslautern, Univ. Kassel, Fraunhofer ITWM, ADVANSA GmbH, Johns Manville GmbH, Oerlikon Neumag
ParticipantsProf. Dr. Nicole Marheineke, Thomas Cibis
Dates2010 – 2013

In the production process of nonwoven materials filaments are spun from a polymer melt by aerodynamic stretching, then entangled by turbulent air flows and finally delivered onto a conveyor belt. The quality of the product is fundamentally influenced by the stochastic properties of the production process. ProFil contributes to innovations in this field with regard to material savings and quality improvements through mathematical modeling and numerical simulation of the process chain «melting-spinning-entangling-deposition». This subproject deals with fiber-fluid interactions and turbulence modeling.