NHR4CES Community Workshop:

Performance Engineering for Numerical Methods in Computational Fluid Dynamics

Computational Fluid Dynamics (CFD) simulations are a crucial yet costly driving force behind many scientific computing, research, and industrial design endeavors. As such, they are responsible for the consumption of a large portion of available computing time in High-Performance Computing (HPC) systems and a worthwhile target for performance optimizations and studies.

Short abstract:

Numerical experiments via CFD applications enable, for example, research towards low emissions in combustion engines or green fuels. Due to their relevance for and ever-changing requirements by research institutions and industry alike, they are constantly subjected to new developments and optimizations targeted towards the simulation of new or increasingly large problems with high accuracy. As a result of the complexity of the software, the amount of required computational resources, and the complexity of modern HPC systems used for the simulations, the importance and benefits of applying performance engineering techniques are evident.

 

Date: June 13 (1.00 pm – 5.00 pm) and June 14 (1.00 pm – 5.00 pm), 2024

Format: Online

 

This workshop is designed to highlight recent activities, developments, and new concepts for analyses and improvements centered around numerical methods used in CFD applications. Emphasis will be placed in particular on optimizations, applicable performance analyses and engineering techniques, as well as the presentation of new and innovative computational methods for specific problems. The workshop aims to connect research groups from different domains with the goal of increasing the performance, efficiency, and capabilities of modern CFD applications by sparking discussions, potential collaborations, and an active exchange of ideas.

 

Language: English

Capacity: 300

Registration

Contact

Fabian Orland

RWTH Aachen University

Felipe González

RWTH Aachen University

Fabian Czappa

TU Darmstadt

Marco Vivenzo

RWTH Aachen University

Thomas Hösgen

RWTH Aachen University

Lukas Rothenberger

TU Darmstadt

Xiaoyu Wang

TU Darmstadt