Flow Modelling for Energy and Process Engineering Dr.-Ing. Juan Alí Medina Méndez

The division of Flow Modelling for Energy and Process Engineering focuses on the modelling and simulation of technically relevant flows in the field of Process Engineering. Complex technical flows are mostly simplified down to a canonical level in order to investigate physically relevant interactions between the turbulence, or the turbulent transport, and the molecular or long-range body forces interacting with the fluid.

One key issue in technically relevant process engineering flows is their usual multiphysical (and multiscale) character. In this context, current and past investigations address, or have addressed, the following types of flows:

  • Turbulent combustion of hydrocarbon fuels with detailed chemical kinetics
  • Turbulent jet diffusion flames
  • Turbulent internal flows with negligible and variable thermal inertia in planar and cylindrical geometries
  • Turbulent internal flows with smooth and rough walls
  • Turbulent internal air flows subject to corona discharge (electrohydrodynamic flows)
  • Electrostatic precipitation of aerosol-loaded flows
  • Falling film evaporation
  • Porous media CO2 reacting flows in structured catalysts (CO2 methanation)

There are several challenges associated to the simulation and modelling of technically relevant process engineering flows. A feature of the intended research is its transdisciplinary character. Depending on the accuracy required for an intended application, it is possible to choose between models of low dimensional order (zero, one-dimensional models), or full three-dimensional models. For turbulent flow applications, the use of reduced order stochastic turbulence models is favoured over filter-based turbulence models, in order to avoid additional empiricism due to the modelling of small-scale unresolved physics. Reduced order stochastic models such as One-Dimensional Turbulence (ODT) also favor the use of simpler numerical methods, due to the computational advantage of the model, which can resolve very small time-steps and very small length-scales, hence yielding relatively low discretization error magnitudes.

Stochastic turbulence models such as ODT can also be applied in a hybrid way with filter-based 3-D models. This is a way to exploit the advantages of both approaches. An example is the hybrid bottom-up approach of Large Eddy Simulation (LES) constructed upon the One-Dimensional Turbulence model (ODTLES).

Team and Alumni

  • B.Sc. Dikshant Sharma
  • B.Sc. Juan F. Patiño
  • B.Sc. Thierry Tchouto
  • B.Sc. Katja Hertha

Relevant Publications