The NEXTFLOW Project
The more you know, the better you can control:
complete flow description for flow control
NEXTFLOW (Next-Generation flow diagnostics for control) is a 5-years ERC Starting Grant project (1.5M€ budget). The main objective is to develop novel flow diagnostics concepts to achieve from incomplete descriptions a complete flow description directly usable for flow control.
Flow control is among the objectives of fluid-dynamics investigation with the most tremendous technological and economical impact. Minimization of the aerodynamic drag of transportation translates directly into fuel savings, with consequent reduction of pollutant emissions and operative costs. Unsteady aerodynamics control has the potential to lead to safer transport (with tuneable reaction to lateral gusts, which are a most relevant cause of accidents for trucks) and more efficient micro-air vehicles to perform missions in environments not accessible to manned aircraft. These are just a few examples of applications where flow control provides opportunities for disruptive technological progress.
A strong scientific barrier is on sensing and control. A profound understanding of the flow dynamics is needed to know the optimal location for flow sensing and to estimate the flow state from sparse measurements. The choice of the control action requires a deep knowledge of the dynamic response of the flow, with the simplest possible models to allow real-time actuation. In summary, a complete thermo-fluid-dynamic description of the flow is an asset.
None of the existing measurement techniques being capable of reaching such a complete level of characterization. If we aim to flow control for medium-to-high Reynolds number flows, a dramatic paradigm change is needed to improve accuracy, resolution, and achieve a complete thermo-fluid-dynamic description.
Objectives and roadmap
The main goal of NEXTFLOW is to develop the next-generation flow-diagnostics approach for control applications. A roadmap with the main intermediate objectives is identified (see Figure):
- Development of a generalized technique to obtain full-field time-resolved measurements for real applications (generalized 4D-4C measurements);
- Develop disruptive data-driven concepts to achieve a step change in spatial resolution and accuracy of velocimetry techniques and enable “beyond-Nyquist” fully-resolved measurements (full-resolution compressed-sensing measurements);
- Establish methods to deliver a more compact output directly usable for flow control tuning.
The methodology of NEXTFLOW builds onto three pillars:
- Data-driven techniques to pursue complete 4D-4C measurements by combination of “incomplete” techniques.
- Machine-learning methods to overcome the sampling limits of measurement techniques and obtain “beyond-Nyquist” measurements.
- Data-driven discovery of low-order models and simplified governing equations.