Biologists have shown that bat wings contain distributed arrays of flow-sensitive hair receptors. The hair receptors are hypothesized to feedback information on airflows over the bat wing for enhanced stability or maneuverability during flight. Here, we study the geometric specialization of hair-like structures for the detection of changes in boundary layer velocity profiles (shapes). A quasi-steady model that relates the flow velocity profile incident on the longitudinal axis of a hair to the resultant moment and shear force at the hair base is developed. The hair length relative to the boundary layer momentum thickness that maximizes the resultant moment and shear-force sensitivity to changes in boundary layer shape is determined. The sensitivity of the resultant moment and shear force is shown to be highly dependent on hair length. Hairs that linearly taper to a point are shown to provide greater output sensitivity than hairs of uniform cross-section. On an order of magnitude basis, the computed optimal hair lengths are in agreement with the range of hair receptor lengths measured on individual bat species. These results support the hypothesis that bats use hair receptors for detecting changes in boundary layer shape and provide geometric guidelines for artificial hair sensor design and application.

The compressive electromechanical response of aligned carbon nanotube (CNT) arrays is evaluated for use as an artificial hair sensor (AHS) transduction element. CNT arrays with heights of 12, 75, and 225 µm are examined. The quasi-static and dynamic sensitivity to force, response time, and signal drift are examined within the range of applied stresses predicted by a mechanical model applicable to the conceptual CNT array-based AHS (0–1 kPa). Each array is highly sensitive to compressive loading, with a maximum observed gauge factor of 114. The arrays demonstrate a repeatable response to dynamic cycling after a break-in period of approximately 50 cycles. Utilizing a four-wire measurement electrode configuration, the change in contact resistance between the array and the electrodes is observed to dominate the electromechanical response of the arrays. The response time of the CNT arrays is of the order of 10 ms. When the arrays are subjected to constant stress, mechanical creep is observed that results in a signal drift that generally diminishes the responsiveness of the arrays, particularly at stress approaching 1 kPa. The results of this study serve as a preliminary proof of concept for utilizing CNT arrays as a transduction mechanism for a proposed artificial hair sensor. Such a low profile and light-weight flow sensor is expected to have application in a number of applications including navigation and state awareness of small air vehicles, similar in function to natural hair cell receptors utilized by insects and bats.

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Inspired by sensing strategies observed in birds and bats, a new attitude control concept of directly using real-time pressure and shear stresses has recently been studied. It was shown that with an array of onboard airflow sensors, small unmanned aircraft systems can promptly respond to airflow changes and improve flight performances. In this paper, a mapping function is proposed to compute aerodynamic moments from the real-time pressure and shear data in a practical and computationally tractable formulation. Since many microscale airflow sensors are embedded on the small unmanned aircraft system surface, it is highly possible that certain sensors may fail. Here, an adaptive control system is developed that is robust to sensor failure as well as other numerical mismatches in calculating real-time aerodynamic moments. The advantages of the proposed method are shown in the following simulation cases: (i) feedback pressure and wall shear data from a distributed array of 45 airflow sensors; (ii) 50% failure of the symmetrically distributed airflow sensor array; and (iii) failure of all the airflow sensors on one wing. It is shown that even if 50% of the airflow sensors have failures, the aircraft is still stable and able to track the attitude commands.

Accurate estimates of flow induced surface forces over a body are typically difficult to achieve in an experimental setting. However, such information would provide considerable insight into fluid-structure interactions. Here, we consider distributed load estimation over structures described by linear elliptic partial differential equations (PDEs) from an array of noisy structural measurements. For this, we propose a new algorithm using Tikhonov regularization. Our approach differs from existing distributed load estimation procedures in that we pose and solve the problem at the PDE level. Although this approach requires up-front mathematical work, it also offers many advantages including the ability to: obtain an exact form of the load estimate, obtain guarantees in accuracy and convergence to the true load estimate, and utilize existing numerical methods and codes intended to solve PDEs (e.g., finite element, finite difference, or finite volume codes). We investigate the proposed algorithm with a two-dimensional membrane test problem with respect to various forms of distributed loads, measurement patterns, and measurement noise. We find that by posing the load estimation problem in a suitable Hilbert space, highly accurate distributed load and measurement noise magnitude estimates may be obtained.