Real-time flow measurement system: physics-informed reconstruction and sampling strategy

Abstract

In this work, we focus on a multi-hole pressure-probe-based flow measurement system for wind tunnel measurements that provides real-time feedback to a robot probe-manipulator, rendering the system autonomous. The system relies on a novel, computationally efficient flow analysis technique that translates the probe's point measurements of velocity and pressure into an updatable mean flow map that is accompanied by an uncertainty metric. The latter provides guidance to the manipulator when planning the optimal probe path. The probe is then guided by the robot in the flow domain until an available time budget has been exhausted, or until the uncertainty metric falls below a prescribed target threshold in the entire flow domain. We assess the capabilities of our new measurement system using computational fluid dynamics data, for which the ground truth is available in the form of a mean flow field. An application in a real wind tunnel setting is provided as well.

Keywords

• High-speed flow
• Control theory
• Big data
• Computational methods
• Machine learning
• Drag reduction
• Flow imaging and velocimetry