Project description
Predicting nature’s extreme events, from rogue waves to flashbacks
The global shift towards decarbonisation is exposing new challenges, including extreme events in fluid dynamics: rare, sudden disruptions that can dramatically alter flow evolution. Whether manifested as atmospheric blocking, rogue oceanic waves or flame flashbacks in hydrogen combustors, these are driven by complex, non-linear interactions that defy standard prediction. The ERC-funded CONTEXT project aims to address this by fusing deep learning with physical constraints. The goal is to develop a framework capable of forecasting and suppressing these events. The methodology will be tested across various systems. Its main focus is to solve such extreme events issues in clean hydrogen combustion.
Objective
Climate change and the race to decarbonise our society is making extreme events in fluids more prevalent. These are rare events where the flow suddenly takes extreme states far from its normal state. These can be found in any flow systems, such as in the atmosphere with atmospheric blocking causing extreme heatwaves, or in our oceans with rogue waves (waves of extreme heights) capable of capsizing boats, or in engineering flows in hydrogen-based clean combustors with flashback events where the flame suddenly moves back into the injection system.
Currently, we cannot accurately predict such extreme events due to several roadblocks. First, the chaotic nature of these turbulent flows makes them hard to predict: any infinitesimal perturbation leads to drastically different evolutions (the butterfly effect). Second, extreme events originate from complex nonlinear interactions which are very different for systems with different physical mechanisms. This makes any past development difficult to generalize across different flow systems. Third, we have very limited observations of such events.
To revolutionize how we tackle extreme events, the CONTEXT project will create a cutting-edge scientific machine learning framework that blends deep learning with physics-based techniques. CONTEXT’s framework will provide the means to (i) identify precursors and mechanisms of extreme events, (ii) forecast the flow evolution before and during extreme events and (iii) control the flows to prevent extreme events. CONTEXT’s framework will be able to handle diverse and disparate physics, with this being demonstrated across different flows of increasing complexity and with different physics, culminating in a demonstration of the practical impact of the framework on the engineering-relevant multiphysics test case of a flashbacking hydrogen combustor.
CONTEXT will provide a comprehensive framework to achieve the understanding, prediction, and prevention of extreme events in turbulent flows.
Fields of science (EuroSciVoc)
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CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC - HORIZON ERC Grants
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Call for proposal
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(opens in new window) ERC-2024-STG
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SW7 2AZ London
United Kingdom
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