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Month: March 2026

The Spring School 2026 in Liverpool (UK) was a success

What a great week at the University of Liverpool! Bringing together the LBM-community, with 42 ๐—ฝ๐—ฎ๐—ฟ๐˜๐—ถ๐—ฐ๐—ถ๐—ฝ๐—ฎ๐—ป๐˜๐˜€ ๐—ณ๐—ฟ๐—ผ๐—บ ๐Ÿญ๐Ÿญ ๐—ฐ๐—ผ๐˜‚๐—ป๐˜๐—ฟ๐—ถ๐—ฒ๐˜€ for our 9th Spring School on Lattice Boltzmann Methods. We would like to thank our friends at University of Liverpool, John Bridgeman, Davide Dapelo and Mohaddeseh Mousavi Nezhad for hosting this year’s Spring School, which allowed us to introduce and help so many people to the methods, theory and best practices of LBM. The spring school provides both an environment for learning and understanding LBM, but also for developing new and interesting research problems. Also a big thank you to Shota Ito, Tikhon Riazantsev, Mathias J. Krause and Stephan Simonis for organizing this year’s OpenLB spring school.

This years poster session allowed insights into the research of the LBM-community, showing off the huge range of different problems that are tackled using LBM. We would also like to congratulate Maxence Desnoyers on winning this years poster session with his poster on a “Numerical Framework to Study Structural Wetting Properties of Proton Exchange Membrance Fuel Cell Catalyst Layers”.

We would also like to thank this yearโ€™s invited speakers: Fedor Bukreev, Isabelle Cheylan, Davide Dapelo, Shota Ito, Florian Kaiser, Mathias J. Krause, Timm Krรผger, Adrian Kummerlรคnder, Halim Kusumaatmaja, Tim Reis, and Stephan Simonis.

The preparations for next years spring school are already underway. It will take place from April 5-9 2027 in Erlangen, Germany. We are looking forward to seeing you there! Until then, stay tuned and happy research!

OpenLB on Aurora, the 3rd fastest Supercomputer!

Pushing onwards from our recent addition of AMD accelerator support in OpenLB Release 1.9, the developer team is happy to share that we now have preliminary support for Intel GPUs. This means OpenLB now supports hardware acceleration across all of the big three platforms: NVIDIA, AMD, and Intel!

Using a new SYCL-based backend for our platform-transparent model implementations, we scaled up to a problem size of 4000 billion single-precision D3Q19 cells. Utilizing 1,000 nodes (~10%) of the Aurora supercomputer at the Argonne Leadership Computing Facility (TOP500 #3), this yielded a peak performance of 21,120 billion cell updates per second (GLUPs).

This work was done in the context of our ALCF Directorโ€™s discretionary allocation project ๐˜–๐˜ฑ๐˜ฆ๐˜ฏ๐˜“๐˜‰ ๐˜ฐ๐˜ฏ ๐˜Œ๐˜น๐˜ข๐˜ด๐˜ค๐˜ข๐˜ญ๐˜ฆ: ๐˜Œ๐˜น๐˜ต๐˜ฆ๐˜ฏ๐˜ฅ๐˜ช๐˜ฏ๐˜จ ๐˜ฑ๐˜ญ๐˜ข๐˜ต๐˜ง๐˜ฐ๐˜ณ๐˜ฎ-๐˜ต๐˜ณ๐˜ข๐˜ฏ๐˜ด๐˜ฑ๐˜ข๐˜ณ๐˜ฆ๐˜ฏ๐˜ค๐˜บ ๐˜ต๐˜ฐ ๐˜๐˜ฏ๐˜ต๐˜ฆ๐˜ญ ๐˜Ÿ๐˜ฆ-๐˜๐˜—๐˜Š (OLEX).

Interested in this or any other aspect of OpenLB and Lattice Boltzmann Methods? Join our upcoming Spring School this March in Liverpool, UK! (Early bird registration ends today)