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SParse AND paRsimonious Event-based fLow Sensing

Project description

New simplified framework for modelling and controlling complex turbulent flows

Most flows in nature and engineering processes are unsteady and turbulent, with nonlinear behaviour. Accurately modelling, characterising and controlling unsteady turbulent flows is made more challenging by the need to account for a huge range of scales from the microscopic to the macroscopic. In contrast to the high-frequency sampling and large number of sensors required by current methods, nature relies on signal processing with minimal event-based sensors. Many complex flows can be represented on low-dimensional manifolds. Leveraging this, the ERC-funded SPANDRELS project aims to develop a novel simplified framework to enable turbulent flow control by coupling manifold learning on low-dimensional manifolds and rational placement of a minimal number of event-based sensors.

Objective

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.
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.
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.
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, and 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.

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

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(opens in new window) ERC-2024-COG

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Host institution

UNIVERSIDAD CARLOS III DE MADRID
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 2 000 000,00
Address
CALLE MADRID 126
28903 Getafe (Madrid)
Spain

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Region
Comunidad de Madrid Comunidad de Madrid Madrid
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 2 000 000,00

Beneficiaries (1)

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