SDL Energy Conversion

To the overview of the Simulation and Data Labs

A cooperation of TU Darmstadt
and RWTH Aachen University

Simulation and Data Lab

The SDL Energy Conversion aims to enable computationally efficient simulations of real-scale combustion devices by developing HPC-ready rCFD software and methods by a co-design process.

In the foreseeable future, combustion will still meet the major part of the world’s primary energy demand, but technologies will need to change for low-carbon and carbon-free energy conversion, using synthetic fuels as key contributors. System redesigns will have to heavily rely on high-performance reactive CFD (rCFD) with sophisticated numerics and accurate predictive physical models.

However, combustion applications are particularly challenging with respect to HPC due to the strong non-linearities and the large ranges of scales arising from the highly-coupled turbulence and chemistry interaction, the large number of partial differential equations for a detailed description of the multiscale phenomena, and the inherent multi-physics aspects.

The SDL Energy Conversion aims to enable computationally efficient simulations of real-scale combustion devices by developing HPC-ready rCFD software and methods by a co-design process.

Highly optimized numerical approaches are being developed, tested, validated, and packaged in different forms. Resulting HPC modules are being optimized for Tier-2 architectures, also providing efficient usage on Tier-1 machines.

Several simulation frameworks will be used, such as the direct numerical simulation (DNS) code CIAO developed at RWTH Aachen University and the open-source code OpenFOAM, customized for large-eddy simulations (LES) and 3D URANS combustion applications at TU Darmstadt. Modeling-, numerics-, and HPC-aspects will be addressed.

We will provide, for example, libraries for combustion data-driven models based on reduced manifolds in terms of tabulated functionals and Lagrangian methods for point particle transport useful for dispersed solid and liquid fuels, non-diffusive scalar transport, and transported probability density function-based combustion models.

Contact the SDL Energy Conversion!

If you have questions for other groups or general questions like access to the HPC infrastructure, have a look at our support website.

Current research topics:

Efficient Multicomponent Transport Library for direct numerical simulations
Data-driven combustion modeling for laminar and turbulent flames
Data-driven turbulence modeling for Large Eddy Simulations
Development of Fortran-Python interface for machine learning (ML) inference on CPUs (RWTH) in collaboration with CSG Parallelism and Performance
ML inference using GPUs (TUDa) in collaboration with CSG Parallelism and Performance

Training offers 2023:

Support activities:

ML models for rCFD based on the OpenFoam solver (interface to pyTorch/Tensorflow)
Dynamic Load Balancing for rCFD in OpenFOAM (usable as dynamically linked library)