SDL Energy Conversion
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.
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)
Training activities:
- Workshop on data-analysis for DNS
- Two-day meeting on Internal Combustion Engine Simulations using OpenFOAM Technology
Gallery
Project partners
- Center of Excellence in Combustion (EuroHPC JU H2020)
- exaFOAM (EuroHPC JU H2020)
- CRC TRR 150
- CRC 129
- CRC 1194
- DFG FOR 2687
Members
Publications
2022
- Investigation of the generalization capability of a generative adversarial network for large eddy simulation of turbulent premixed reacting flows (Ludovico Nista, Christoph Schumann, Temistocle Grenga, Antonio Attili, Heinz Pitsch), Proceedings of the Combustion Institute (accepted: 24.09.2022).
- Scalability Analysis of Super-Resolution Generative Adversarial Network Training for Turbulence Closure Modeling (HPC2022 Conference) (submitted)
- Investigation of the generalization capability of a generative adversarial network for large eddy simulation of turbulent premixed reacting flows
- Combined effects of heat loss and curvature on turbulent flame-wall interaction in a premixed dimethyl ether/air flame (Driss Kaddar, Matthias Steinhausen, Thorsten Zirwes, Henning Bockhorn, Christian Hasse, Federica Ferraro), Proceedings of the Combustion Institute.