Hola,
¿eres nuevo aquí?

Regístrate gratis y conecta tu empresa con financiación pública, partners y proyectos.

Tengo cuenta

Regístrate

Ver video

CONTEXT

Financiado

Cerrado

Control of Extreme Events in Turbulent Flows with Scientific Machine Learning

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... ver más

31/03/2030

TU Delft

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

TECHNISCHE UNIVERSITEIT DELFT No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-10-07

ERC-2024-STG: ERC STARTING GRANTS Scope:Objectives

I+D

Cerrada hace 1 año

0% 100% 100%

Características del participante

Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

TU Delft

1.50M€ | Lider

Conecta tu I+D

¿Tienes un proyecto y buscas un partner? Gracias a nuestro motor inteligente podemos recomendarte los mejores socios y ponerte en contacto con ellos. Te lo explicamos en este video

Duración del proyecto: 65 meses Fecha Inicio: 2024-10-07
Fecha Fin: 2030-03-31

Líder del proyecto

TECHNISCHE UNIVERSITEIT DELFT No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 1M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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.

Conecta tu I+D

Entra hoy

¿Olvidé mi contraseña?

Financiación

Empresas

CTIs/Universidades

Proyectos

Investigadores