Underwater flow sensing is important for many robotics and military applications, including underwater robots and vessels. We report the development of micromachined, distributed flow sensors based on a biological inspiration, the fish lateral line sensors. Design and fabrication processes for realizing individual lateral line sensor nodes are discussed in this paper, along with preliminary characterization results.

https://iopscience.iop.org/article/10.1088/0960-1317/12/5/322/pdf

Design and fabrication of artificial lateral line flow sensors

We discuss two types of micromachined flow sensors realized by using novel microfabrication processes—a hot-wire an- emometer ~based on thermal transfer! and a biologically inspired flow sensor ~based on momentum transfer!. Both sensors are enabled by a new, efficient three-dimensional assembly technique called the plastic deformation magnetic assembly method. The sensors can be packaged in high-density, two-dimensional arrays efficiently, with each sensor node capable of performing two-component or three- component flow sensing. We first discuss the development of new hot-wire anemometers ~HWA!. The HWA uses a thermal element ~hot wire! that is made of Pt/Ni/Pt film with a measured temperature coefficient of resistance of 2,700 ppm/°C. The thermal element is elevated out of plane by using support beams made of polyimide, a polymer material. Both steady-state and transient characteristics of the sensor have been experimentally obtained. The second type of flow sensor is based on momentum transfer principles and inspired by fish lateral line sensors. Each sensor consists of a vertical cilium attached to a horizontal cantilever. Fluid flow imparts moment on the vertical cilium, and causes the horizontal cantilever to bend. The fabrication process and preliminary measurement data are presented.

Two-Dimensional Micromachined Flow Sensor Array for Fluid Mechanics Studies

Experimental investigation of a simplified 3D high lift configuration in support of CFD validation

Flush air data sensing systems have been widely applied to large (manned) aircraft, where pressure orifices are typically located at the nosetip. This paper investigates the feasibility of a flush air data sensing system designed to estimate the air data states of a small unmanned air vehicle flown at speeds as low as Mach 0.07. Furthermore, due to the presence of a nose propeller, the pressure orifices are located at the wing leading edge. The motivation behind this project is the fact that traditional air data booms are physically impractical for small unmanned air vehicles. Overall, an 80 and 97% reduction in instrumentation weight and cost, respectively, were achieved. Both parametric and multilayer perceptron neural network models have been previously applied in the literature to model the aerodynamic relationship between aircraft surface pressure and the air data states. In this paper, an extended minimum resource allocating network radial basis function neural network is used as the flush air data sensing system model, due to its good generalization capabilities and compact structure. Computational fluid dynamic simulations are implemented to identify the ideal pressure port locations, and wind-tunnel tests are carried out to train and test the extended minimum resource allocating network radial basis function neural network.

Neural-network-based flush air data sensing system demonstrated on a mini air vehicle

We present the development of miniature multihole pressure probes and a novel neural-network-based calibration algorithm for them. Seven-hole probes of tip diameters as low as 0.035 in. (0.9 mm) were successfully fabricated with high tip surface quality. Any of the typical probe tip geometries, i.e., hemispherical, conical, or faceted, could be fabricated. The miniature probes were calibrated and tested in a wind tunnel. A backpropagation-based neural-network calibration algorithm was developed for these probes, with flexibility in network architecture design and network self-optimization capabilities. In the feedforward mode the algorithm yields computational speeds an order of magnitude higher than those typically achieved by similar accuracy interpolation algorithms. The new algorithm has prediction accuracies of 0.28 deg in the flow angles and 0.35% in the velocity magnitude

Rajesh Vijayagopal

Papers

Miniature Multihole Pressure Probes and Their Neural-Network-Based Calibration

Miniature multi-hole pressure probes - Their neural network calibration and frequency response enhancement