SParse AND paRsimonious Event-based fLow Sensing

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.

Development of Flow Estimation Tools

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.

Challenges in Current Paradigms

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 Approach to Flow Estimation

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.

Recent Findings

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 Goals

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:

1. Reduce problem dimensionality and represent turbulent unsteady flows on low-dimensional manifolds.
2. Identify parsimonious methods for sensor choice and location in complex flows.
3. 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.