ML4Fluids Challenges

We are pleased to announce two exciting challenges associated with the 3rd ERCOFTAC Workshop on ML4Fluids, each designed to advance machine learning applications in computational fluid dynamics.

Data-Driven Turbulence Modelling Challenge

Building on NASA's successful 2022 turbulence modelling challenge, this initiative focuses on advancing data-driven turbulence modelling approaches for 3D cases of industrial relevance. Participants will develop and test novel RANS models, enhanced turbulence closures, hybrid RANS-LES approaches, or other data-driven turbulence modeling techniques on a set of challenging test cases spanning various flow phenomena such as periodic hills, smooth-body separation, automotive wake flows, and wing-body junction flows. Evaluation is based on accuracy, computational cost, and generalizability. The community-driven challenge welcomes test case suggestions from participants. Visit the dedicated challenge webpage at github.com/rmcconke/ml-turbulence-benchmark for complete technical specifications, final test case details, training datasets, boundary conditions, and evaluation protocols.

Organizers: Tyler Buchanan, Paola Cinnella, Richard Dwight, and Ryley McConkey
To participate: Send an expression of interest to t.s.b.buchanan@tudelft.nl