Completed Physics & Astronomy Computing & AI

AIRTUK: The UK Artificial Intelligence for Turbulence Research Hub

In plain English

AI plain-English summary

Turbulent air swirling around a plane wing or rushing through a wind turbine costs the global economy billions in wasted fuel and lost efficiency each year, and this hub will use artificial intelligence to finally tame it. Turbulence is one of physics’ most stubborn unsolved problems. Current computer models either oversimplify the chaotic flows or require supercomputers that run for weeks. This gap means engineers design aircraft, wind farms, and pipelines with large safety margins, wasting energy and materials. AIRTUK will combine AI with classical fluid dynamics to create faster, more accurate predictions that capture turbulence’s real behaviour. If successful, the hub could slash the carbon footprint of aviation and renewable energy by enabling lighter, more aerodynamic designs. It will also build a national database of wind-tunnel data, open to all UK researchers, and develop energy-efficient computing methods that reduce the environmental cost of running large simulations. The AI techniques created here may spill over into other fields that grapple with complex, chaotic systems—such as weather forecasting or ocean modelling. This is fundamental science with a clear practical target: making the invisible, costly chaos of turbulence predictable enough to engineer around.

View original technical description
We propose the creation of AIRTUK, a national centre of excellence dedicated to pioneering artificial intelligence (AI) techniques for the analysis, prediction, and control of turbulent flows. This hub will unite the UK's foremost experts in turbulence, fluid dynamics, high-performance computing, and machine learning to address one of the most complex and enduring challenges in physics, with far-reaching implications for aerospace, energy & environmental sciences. It will be created in close partnership with the Alan Turing institute. AIRTUK will position the UK at the global forefront of AI-enhanced turbulence research, creating a sustainable ecosystem that drives innovation across disciplines while addressing critical industrial challenges and environmental concerns. The objectives of the hub are (1) Research Excellence: Deliver groundbreaking research that fundamentally transforms how turbulent flows are studied, modelled, and understood by integrating cutting-edge AI methodologies with classical fluid dynamics approaches, (2) Data Infrastructure: Develop and maintain a comprehensive national infrastructure for curating large-scale datasets (in collaboration with the National Wind Tunnel Facility), creating an invaluable resource accessible to the entire UK research community, (3) AI Innovation: Drive forward UK's AI capabilities by developing novel techniques, algorithms, and analytical approaches with applications extending beyond turbulence research into diverse scientific and industrial domains, (4) Environmental Sustainability: Pioneer environmentally sustainable approaches to turbulence research through energy-efficient computing techniques (sharing best practice and latest developments in on-the-fly post processing), and applications focused on reducing carbon footprints, (5) Diversity & Inclusion: Address diversity challenges through targeted knowledge exchange activities, and various outreach activities; share ideas/tools required for a broader up-skill of the UK high education landscape.

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Researchers

Alistair Revell (Co-Investigator)Andrew Wheeler (Co-Investigator)David Emerson (Co-Investigator)Jeyan Thiyagalingam (Co-Investigator)Julia Handl (Co-Investigator)Luca Magri (Co-Investigator)Sylvain Laizet (Principal Investigator)Umair Ahmed (Co-Investigator)

Related Research

Grants with similar aims, by meaning.

The UK Turbulence Consortium
UK Turbulence Consortium
UK National Wind Tunnel Facility
High performance computing support for united kingdom consortium on turbulent reacting flows (ukctrf)
CCP Turbulence

Original classification

Research Grant

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