A cooperation of TU Darmstadt
and RWTH Aachen University

Simulation and Data Lab

The SDL Energy Conversion enables 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.

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:
  • Data-driven combustion modeling for laminar and turbulent flames
  • Data-driven turbulence modeling for Large Eddy Simulations
  • Evaluation of chemical kinetics and transport properties on heterogeneous architectures
  • Development of Fortran-Python interface for machine learning (ML) inference on CPUs in collaboration with CSG Parallelism and Performance
  • Inference of ML-based combustion models using GPUs in collaboration with CSG Parallelism and Performance
Our repositories:

 

Our channels:

Project partners

Members

Mohammed Elwardi Fadeli

TU Darmstadt

Prof. Dr. Christian Hasse

TU Darmstadt

Driss Kaddar

TU Darmstadt

‎Dr. Magnus Kircher

TU Darmstadt

‎Dr. Holger Marschall

TU Darmstadt

Ludovico Nista

RWTH Aachen University

Prof. Dr. Heinz Pitsch

RWTH Aachen University

Marco Vivenzo

RWTH Aachen University

Hesheng Bao

RWTH Aachen University

Publications

2024

2023

2022