Thermo-Fluid Systems

Research objectives

The research of the Chair for Simulation of Reacting Thermo-Fluid Systems deals with the numerical modelling of reacting and mostly turbulent flows from gaseous, liquid and solid energy carriers. The chair is held by Prof. Dr. Oliver T. Stein. The major aims of our research are to (i) increase our fundamental understanding of complex reacting multiphase flows, to (ii) develop predictive simulation tools for such systems and to thereby (iii) contribute to the global transition towards a sustainable energy economy. We closely collaborate with our experimental colleagues both from Engler-Bunte-Institut and external project partners. We achieve our research goals by applying and developing a set of modelling and analysis tools as listed below.

Scientific research topics

  • Direct numerical simulation (DNS) of turbulent reacting flows: DNS resolves all scales of turbulent flows, which results in highly accurate predictions of flows with low to intermediate turbulence levels for fundamental analyses.

  • Large eddy simulation (LES) of turbulent flows with chemical reactions: LES resolves the large scales of turbulence, while modelling the small-scale processes, leading to accurate predictions of intermediate to highly turbulent flows in research and applications.

  • Development of advanced LES models for reacting multiphase flows (liquid droplets and solid particles): The LES of multiphase flows usually requires the modelling of phase transitions to allow for reliable simulations.

  • Sparse-Lagrangian Multiple-Mapping Conditioning (MMC) modelling for LES: MMC is a complex but highly efficient closure model for the LES of single and multiphase flow systems.

  • LES techniques for population balance equation (PBE) modelling of particulate systems: Interaction processes like e.g. nucleation, condensation, coagulation, agglomeration and others within particle or droplet ensembles require advanced LES closure models.

  • Machine learning approaches for turbulent reacting flows: Modern machine and deep learning techniques allow for the efficient modelling of complex systems, pattern recognition and feature analysis.

 

Research projects

Further research activities