Henrik Rusche
Rusche, Henrik
Computational Fluid Dynamics of Dispersed Two-phase Flows at High Phase Fractions.
PhD thesis, Imperial College London, 2002. A foundational work on multiphase flow modeling that underpins much of his later OpenFOAM development contributions.
Shanmugasundaram, R. k., Rusche, Henrik, Windt, C., Kirca, Ö., Sumer, B. M., & Goseberg, N.
Towards the Numerical Modelling of Residual Seabed Liquefaction Using OpenFOAM.
OpenFOAM® Journal, Vol. 2:16, 2022. Develops a finite-volume OpenFOAM solver for seabed liquefaction analysis.
Ranjith Khumar Shanmugasundaram, Henrik Rusche, Christian Windt, V. S. Özgür Kirca, B. Mutlu Sumer, & Nils Goseberg.
Numerical Modeling of Wave-Induced Seabed Liquefaction: A Drift-Flux Model for Liquefied Soil.
Journal of Waterway, Port, Coastal and Ocean Engineering, Vol. 151, No. 6, 2025. Advanced numerical model for seabed soil liquefaction simulation.
Rusche, Henrik, Jasak, H., Popovac, M.
Implementation and Numerical Stabilisation of Adjoint Flow and Turbulence Model in OpenFOAM.
Proceedings of the European Conference on Computational Fluid Dynamics (ECFD VI), 2014. A notable contribution to turbulence modeling and adjoint flow methods in OpenFOAM.
Rusche, Henrik
Recent Developments in OpenFOAM.
Proceedings of the III International Conference “Cloud computing. Education. Research. Development.”, Moscow, 2013. A summary of key advancements in the OpenFOAM framework to that date.
Session
Efficient simulation of multiphase flows remains a major challenge, particularly for cloud microphysical processes in which interactions between turbulent airflow and suspended droplets must be resolved in detail. We present a novel asynchronous two-way coupled Euler–Lagrange simulation framework that exploits heterogeneous computing architectures to achieve unprecedented scalability.
The proposed method executes Eulerian field calculations on CPUs using the OpenFOAM software package, coupled asynchronously to Lagrangian particle tracking on GPUs implemented in Julia, minimizing computational idling times and synchronization barriers. Data transfers are initiated immediately upon data availability, with Eulerian source terms predicted from previous time steps and subsequently corrected to ensure conservation of mass and momentum. Particles are organized into cache-friendly chunks with maintained bounding boxes, enabling dynamic load balancing across GPUs and optimized CPU-GPU data transfers.
Comprehensive testing on a local workstation and on a EuroHPC JU supercomputer revealed dramatic improvements: the algorithm achieves excellent scalability up to 256 billion droplets. The overall time-to-solution improved by a factor of 4.5, while energy efficiency improved 3.4 times compared to established methods. Weak and strong scaling tests demonstrated very good efficiency and speedup using up to 2500 cores paired with 256 GPUs.
The software is available in a public GIT repository at https://github.com/Wikki-GmbH/SCALE-TRACK