SPANDRELS-SParse AND paRsimonious Event-based fLow Sensing Projects uri icon

type

  • European Research Project

reference

  • 101171280

date/time interval

  • October 1, 2025 - September 30, 2030

abstract

  • 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.

keywords

  • aerospace engineering; fluid mechanics; energy processes engineering; propulsion engineering, e.g. hydraulic, turbo, piston, hybrid engine; flow control, flow estimation, manifold learning, scientific machine learning, event-based sensors