Detection of flow separation and stagnation points using artificial hair sensors

Recent interest in fly-by-feel approaches for aircraft control has motivated the development of novel sensors for use in aerial systems. Artificial hair sensors (AHSs) are one type of device that promise to fill a unique niche in the sensory suite for aerial systems. In this work, we investigate the capability of an AHS based on structural glass fibers to directly identify flow stagnation and separation points on a cylindrical domain in a steady flow. The glass fibers are functionalized with a radially aligned carbon nanotube (CNT) forest and elicit a piezoresistive response as the CNT forest impinges on electrodes in a micropore when the hair is deflected due to viscous drag forces. Particle image velocimetry is used to measure the flow field allowing for the resulting moment and force acting on the hair to be correlated with the electrical response. It is demonstrated that the AHS provides estimates for the locations of both the stagnation and separation in steady flow. From this, a simulation of a heading estimation is presented to demonstrate a potential application for hair sensors. These results motivate the construction of large arrays of hair sensors for imaging and resolving flow structures in real time.