📋 Tender Overview
Unraveling Turbulence Through Ensemble Decomposition
Developing New Perspectives On Highly Turbulent Flowsturbulence Is An Omnipresent Phenomenon In Nature And Technology. Yet, Developing Theories Of Turbulence, Which Ultimately All Modelling Applications Rely On, Remains An Outstanding Scientific Challenge. Even Massive Simulations On The Largest Supercomputers Struggle To Capture Highly Turbulent Flows. By Combining Data-Driven Theory And Large-Scale Computations, The Eu-Funded Project United Aims At Developing New Approaches To This Long-Standing Problem. United Will Enhance Our Fundamental Understanding Of Highly Turbulent Flows And Lay The Foundation For New Modelling Approaches In Various Fields, Including Computational Engineering, Earth Sciences, Renewable Energy, And Plasma Physics.
Funded Under
Excellent Science - European Research Council (Erc)
Total Cost
€ 2 000 000,00
Eu Contribution
€ 2 000 000,00
Coordinated By
Universitat Bayreuth
Germany
Objective
Turbulence governs essentially all large-scale flows on our planet, including our atmosphere and oceans. With a vast number of engineering applications in transportation technology, renewable energies, and more, turbulence has a direct impact on our lives. However, developing predictive theories of turbulence, which ultimately all modeling applications rely on, remains one of the outstanding scientific challenges. Moreover, while massive simulations on the largest supercomputers are nowadays an established tool, reaching realistically high Reynolds numbers remains prohibitive. Already today, analyzing the sheer amount of peta-scale simulation data requires new paradigms for making meaningful progress. Fundamentally new approaches are needed to achieve a breakthrough. UniTED will deliver such an approach by a unique, synergistic combination of data-driven theory and large-scale computations. How? Recently, I showed that the complex statistics of turbulence can be disentangled into much simpler sub-ensembles. This significant reduction of complexity points toward exciting new theoretical pathways and novel computational methodologies, which I will explore in this project. In UniTED, we will (A) dissect the multi-scale structure of turbulence through massively parallel computations. This will (B) provide the foundation for a statistical theory of turbulence which is based on a novel ensemble decomposition approach. Combining (A) and (B), we will (C) develop a novel ensemble-based simulation approach, enabling unprecedented insights into turbulence at high Reynolds numbers. We will then use this approach to (D) provide big data for modeling small-scale turbulence using physics-informed machine learning. UniTED will boost our fundamental understanding of turbulence at very high Reynolds numbers and provide new modeling approaches in a breadth of fields such as computational engineering, the Earth sciences, renewable energy, and plasma physics.
