SubsidieMeestersSParse AND paRsimonious Event-based fLow Sensing
This project aims to develop a framework for estimating turbulent flows using manifold learning and event-based sensors, reducing data needs and enabling efficient flow control.
Projectdetails
Introduction
The closed-loop control of unsteady turbulent flows requires efficient strategies to sense the flow state. Despite the challenge posed by the non-linearities and the large range of scales of turbulent flows, their ubiquitous nature motivates unabated research efforts.
Current Challenges
Over the last years, we have developed linear and non-linear flow estimation tools, with relevant laboratory applications. Nevertheless, the state of the art requires an intractable number of sensors, making the data acquisition and analysis unfeasible in a practical scenario.
Moreover, the current paradigm of flow control requires continuous sensing and action in time, leading to very large data rates. Strangely, this seems at odds with what nature does.
Nature's Solution
Insects estimate the flow surrounding them with a few event-based sensors embedded in their wings. Algorithms for event-based signal processing avoid aliasing without the need for high-frequency periodic sampling, reducing the amount of data needed to estimate complex temporal series.
This could enable flow estimation with easy-to-handle and cheap-to-compute data. Furthermore, our recent findings show that many complex flows can be represented on low-dimensional manifolds. The availability of a reduced set of coordinates for state representation is a key enabler for the choice of a sparse set of sensors in space.
Project Objectives
This project will develop a novel framework for the estimation of turbulent and unsteady flows coupling manifold learning and event-based sensors. Tackling selected relevant laboratory problems, with and without control, we will:
- Reduce problem dimensionality and represent turbulent unsteady flows on low-dimensional manifolds.
- Identify parsimonious methods for sensor choice and location in complex flows.
- Define a theoretical framework for turbulent-flow measurements from event sensors.
Such a framework will be a key enabler for flow control and will open a novel research path in fluid mechanics.
Financiële details & Tijdlijn
Financiële details
| Subsidiebedrag | € 2.000.000 |
| Totale projectbegroting | € 2.000.000 |
Tijdlijn
| Startdatum | 1-10-2025 |
| Einddatum | 30-9-2030 |
| Subsidiejaar | 2025 |
Partners & Locaties
Projectpartners
- UNIVERSIDAD CARLOS III DE MADRIDpenvoerder
Land(en)
Vergelijkbare projecten binnen European Research Council
| Project | Regeling | Bedrag | Jaar | Actie |
|---|---|---|---|---|
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Sparse Measurement-Based Reduced-Order Models of Fluid-Structure-InteractionsSMARTFLUIDS aims to develop Reduced Order Models for fluid-structure interaction using deep learning to enhance understanding and prediction of FSI dynamics, reducing costs in engineering design and renewable energy. | ERC Starting... | € 1.499.159 | 2025 | Details |
Non-Stationary Non-Homogeneous TurbulenceThis project aims to revolutionize turbulent flow prediction through innovative laboratory, computational, and theoretical methods, leading to a new understanding of non-stationary and non-homogeneous turbulence. | ERC Advanced... | € 2.499.514 | 2022 | Details |
Control of Extreme Events in Turbulent Flows with Scientific Machine LearningThe CONTEXT project aims to develop a machine learning framework to identify, forecast, and control extreme events in turbulent flows, enhancing prediction and prevention across diverse systems. | ERC Starting... | € 1.499.068 | 2025 | Details |
Unravelling unsteady fluid flows in porous media with 3D X-ray micro-velocimetryFLOWSCOPY aims to revolutionize the understanding of fluid flows in opaque porous materials by developing a fast 3D X-ray imaging method to measure complex flow dynamics at micro and macro scales. | ERC Starting... | € 1.500.000 | 2023 | Details |
Beyond self-similarity in turbulence
This project aims to develop and validate a theory for intermediate-strain turbulence using machine learning and advanced simulations to enhance engineering applications like wind energy and UAV efficiency.
Sparse Measurement-Based Reduced-Order Models of Fluid-Structure-Interactions
SMARTFLUIDS aims to develop Reduced Order Models for fluid-structure interaction using deep learning to enhance understanding and prediction of FSI dynamics, reducing costs in engineering design and renewable energy.
Non-Stationary Non-Homogeneous Turbulence
This project aims to revolutionize turbulent flow prediction through innovative laboratory, computational, and theoretical methods, leading to a new understanding of non-stationary and non-homogeneous turbulence.
Control of Extreme Events in Turbulent Flows with Scientific Machine Learning
The CONTEXT project aims to develop a machine learning framework to identify, forecast, and control extreme events in turbulent flows, enhancing prediction and prevention across diverse systems.
Unravelling unsteady fluid flows in porous media with 3D X-ray micro-velocimetry
FLOWSCOPY aims to revolutionize the understanding of fluid flows in opaque porous materials by developing a fast 3D X-ray imaging method to measure complex flow dynamics at micro and macro scales.