Skip to content

Month: February 2024

New Android App “paint2sim” Released

Introducing the app “paint2sim” – A Digital Twin for 2D Fluid Flow Simulations

Paint2sim is a mobile application using a Lattice Boltzmann Method realized by the open-source simulation framework OpenLB. This innovative app allows users to scan hand-drawn domains and visualize 2D fluid flow simulations just-in-time on their mobile devices. Whether you’re a student, researcher, or engineer, explore fluid dynamics with an intuitive interface with your fingertips. The app is freely available for download.

For in-depth technical insights, refer to our latest paper, “Just-in-Time Fluid Flow Simulation on Mobile Devices Using OpenVisFlow and OpenLB

Dennis Teutscher and his team developed the app paint2sim as part of the “teaching4future” project, with funding from the Lattice Boltzmann Research Group at KIT and the Ministry of Science, Research, and Arts of Baden-Württemberg, Germany.

Use Case: Scanning a hand-drawn domain and simulating it on a mobile device

OpenLB Community YouTube Channel Update

We have just released a new video on our OpenLB YouTube Channel:

Heterogeneous Load Balancing in OpenLB: Cooperatively Utilizing CPUs and GPUs for a Turbulent Mixing Simulation

Following up on the turbulent micromixer simulation showcased here, the present video illustrates OpenLB’s heterogeneous computation capabilities.

The performance of the simulation case is improved by up to 87% when using heterogeneous CPU-GPU based compared to GPU-only execution. This is achived by distributing the two computationally expensive turbulent inlet regions onto CPUs while the comparatively cheaper bulk regions are processed on GPUs. The underlying inhomogeneous spatial domain decomposition was obtained using a novel genetic algorithm for cost-aware optimization.

A single accelerated CPU-GPU node of the HoreKa supercomputer (2x Intel Xeon Platinum 8368, 4x NVIDIA A100) was used for the showcased simulation consisting of 355 million lattice cells.
OpenLB enabled the cooperative usage of MPI, OpenMP, AVX-512 vectorization and CUDA, reaching a throughput of ~19.25 billion (NSE-only) resp. ~4.79 billion cell updates per second for the fully coupled case.

Simulation setup: Fedor Bukreev
Heterogeneous Load Balancing, Performance engineering, Visualization: Adrian Kummerländer

For further information please vist the associated show case: Heterogeneous Load Balancing