Flow Sensor Arrays Research Articles

Smart Industrial Internet of Things Framework for Composites Manufacturing.

Composite materials are increasingly important in making high-performance products. However, contemporary composites manufacturing processes still encounter significant challenges that range from inherent material stochasticity to manufacturing process variabilities. This paper proposes a novel smart Industrial Internet of Things framework, which is also referred to as an Artificial Intelligence of Things (AIoT) framework for composites manufacturing. This framework improves production performance through real-time process monitoring and AI-based forecasting. It comprises three main components: (i) an array of temperature, heat flux, dielectric, and flow sensors for data acquisition from production machines and products being made, (ii) an IoT-based platform for instantaneous sensor data integration and visualisation, and (iii) an AI-based model for production process forecasting. Via these components, the framework performs real-time production process monitoring, visualisation, and prediction of future process states. This paper also presents a proof-of-concept implementation of the framework and a real-world composites manufacturing case study that showcases its benefits.

Bioinspired Artificial Hair Sensors for Flight-by-Feel of Unmanned Aerial Vehicles: A Review

Flight-by-feel is an emerging approach to flight control that uses distributed arrays of pressure, strain, and flow sensors to guide aircraft. Among these, hair-type flow sensors have received the least attention yet hold some advantages over conventional sensors. This paper reviews hair-like flow microsensors developed since 2013, focusing on developments in design, construction, and application. Hair-like flow sensors can be found in artificial cochleae, submersible navigation, terrestrial robots, and, rarely but increasingly, on aircraft. In this survey, we categorize hair-like flow sensors into three types (long whisker-like hairs, ultrasensitive microscale hairs, and short trichoid-like hairs), and primarily cover sensors that may be suitable for use on aircraft. The recent progress in flow-based flight control using distributed sensing is also discussed, along with the optimization of sensor placement and the potential for flight-by-feel in sixth-generation military and civilian aircraft designs. This survey aims to provide a consolidated account of the history and state-of-the-art of artificial hair-cell flow sensors, motivate consideration of flight-by-feel as a viable flight control paradigm, and define avenues for future research. As engineering and biological science continue to converge, we hope that researchers in both fields find this survey an inspirational and useful resource.

Flexible Skin for Flight Parameter Estimation Based on Pressure and Velocity Data Fusion

Accurate perception of flight parameters is essential for the safe and stable flight of small unmanned aerial systems (SUASs). However, traditional flush air data sensing (FADS) systems require complex tubing and fuselage openings. Herein this study, a flexible skin system integrated with a pressure and thermal flow sensor array, and a dual‐sensor fusion algorithm for determining the angle of attack (AOA) and airspeed (V ∞ ), are proposed. By establishing an error back‐propagation neural network, the dual‐sensor fusion modality demonstrates higher estimation accuracy than other modalities that utilize only pressure data or only flow velocity data, achieving mean absolute errors of the AOA and V ∞ of less than 0.16° and 0.37 ms−1, respectively. Moreover, the simulation and experimental results show that sensors placed closer to the leading edge of the wing can provide higher estimation accuracy of the flight parameters. The flight parameters determined using the flexible air data sensing system and dual‐sensor fusion algorithm demonstrate potential application in the flight control of SUASs.

The Quadrature Method: A Novel Dipole Localisation Algorithm for Artificial Lateral Lines Compared to State of the Art.

The lateral line organ of fish has inspired engineers to develop flow sensor arrays—dubbed artificial lateral lines (ALLs)—capable of detecting near-field hydrodynamic events for obstacle avoidance and object detection. In this paper, we present a comprehensive review and comparison of ten localisation algorithms for ALLs. Differences in the studied domain, sensor sensitivity axes, and available data prevent a fair comparison between these algorithms from their original works. We compare them with our novel quadrature method (QM), which is based on a geometric property specific to 2D-sensitive ALLs. We show how the area in which each algorithm can accurately determine the position and orientation of a simulated dipole source is affected by (1) the amount of training and optimisation data, and (2) the sensitivity axes of the sensors. Overall, we find that each algorithm benefits from 2D-sensitive sensors, with alternating sensitivity axes as the second-best configuration. From the machine learning approaches, an MLP required an impractically large training set to approach the optimisation-based algorithms’ performance. Regardless of the data set size, QM performs best with both a large area for accurate predictions and a small tail of large errors.

Multi-Functional Flexible Sensor Sheets for Iot Society

Multiple sensing functionalities enable to collect a variety of information for the next class of Internet of Things (IoT) concept such as medical/healthcare, robotics, environment, and infrastructures. In addition to the functionalities, mechanical flexibility allows to attach the devices over non-planar surfaces conformally, resulting in that more information can be monitored precisely. In particular, multi-functional electronic skin (e-skin) can contribute to the applications of robotic skin and infrastructure due to low-cost, macro-scale devices that can cover a variety of objects. In this talk, soft robotics with e-skin functions and air flow mapping for the infrastructure applications are mainly discussed.For the soft robots with e-skin, tactile pressure distribution and temperature of the holding object are simultaneously detected by integrating these sensors on the tip of soft robot finger. Next, for the infrastructure monitoring, flexible air flow sensor array is demonstrated to monitor air flow distribution on the curved surfaces. For the flow sensor, calorimetric methodology is used to monitor the temperature distribution generated by a printed flexible heater. This heat distributions are detected by printed temperature sensors. Integration of these heaters and temperature sensors, as the first proof-of-concept, realizes real-time air flow mapping on the curved surface successfully.

Aerodynamic State and Loads Estimation Using Bioinspired Distributed Sensing

Flying animals exploit highly nonlinear dynamics to achieve efficient and robust flight control. It appears that the distributed flow and force sensor arrays found in flying animals are instrumental in enabling this performance. Using a wind-tunnel wing model instrumented with distributed arrays of strain and pressure sensors, we characterized the relationship between the distributed sensor signals and aerodynamic and load-related variables. Estimation approaches based on nonlinear artificial neural networks (ANNs) and linear partial least squares were tested with different combinations of sensor signals. The ANN estimators were accurate and robust, giving good estimates for all variables, even in the stall region when the distributed array pressure and strain signals became unsteady. The linear estimator performed well for load estimates but was less accurate for aerodynamic variables such as angle of attack and airspeed. Future applications based on distributed sensing could include enhanced flight control systems that directly use measurements of aerodynamic states and loads, allowing for increase maneuverability and improved control of unmanned aerial vehicles with high degrees of freedom such as highly flexible or morphing wings.

Shape Classification Using Hydrodynamic Detection via a Sparse Large-Scale 2D-Sensitive Artificial Lateral Line

Artificial lateral lines are fluid flow sensor arrays, bio-inspired by the fish lateral line organ, that measure a local hydrodynamic environment. These arrays are used to detect objects in water, without relying on light, sound, or on an active beacon. This passive sensing method, called hydrodynamic imaging, is complementary to sonar and vision systems and is suitable for collision avoidance and near-field covert sensing. This sensing method has so far been demonstrated on a biological scale from several to tens of centimeters. Here, we present measurements using a large-scale artificial lateral line of 3.5 meters, consisting of eight all-optical 2D-sensitive flow sensors. We measure the fluid flow as produced by the motion of five different objects, towed across a swimming pool. This results in repeatable stimuli, whose measurements demonstrate a complementary aspect of 2D-sensing. These measurements are both used for constructing temporal hydrodynamic signatures, which reflect the object’s shape, and for flow-feature based near-field object classification. For the latter, we present a location-invariant feature extraction method which, using an Extreme Learning Machine neural network, results in a classification F1-score up to 98.6% with selected flow features. We find that, compared to the traditional sensing dimension parallel to the sensor array, the novel transverse fluid velocity component bears more information about the object shape. The classification of objects via hydrodynamic imaging thus benefits from 2D-sensing and can be scaled up to a supra biological scale of several meters.

Fabrication and Characterization of Dual Coordinate Self Examined Thermal Flow Sensor Arrays Based on Longitudinal Heat Conduction

Thermal flow sensors have been widely used in wind energy utilization, dangerous chemicals detection, automobiles, intelligent robots and unmanned aerial vehicles (UAV). New applications put forward higher requirements for detecting parameters of a two-dimensional (2-D) gas-flow vector over a full range of 360°. A novel thermal flow sensor on Si/SiO2/Pt/AlN substrate is presented to optimize sensor parameters. Eight thermal elements patterned as equally divided sectors of a regular octagon compose the sensor probe. Dual coordinate systems are created based on the regular octagon distribution to realize the self examined function. The longitudinal heat conduction mode shortens the heat transfer distance from mm level to micron level. And the a-axis oriented AlN film improves the heat transfer efficiency due to its high thermal conductivity. Dual coordinate self examined method is proven through experiments for the first time. Measurement results show that the maximum measurement flow velocity is up to 37m/s, with an output voltage sensitivity of 28.1 mV/(m/s). Flow direction was measured over a full range of 360° with an accuracy of 0.5 m/s at low velocity and 5% at high velocity. We believe that the sensor presented in this work will pave the way for novel applications in calorimetric sensors and array sensors.

Highly Precise Multifunctional Thermal Management-Based Flexible Sensing Sheets.

Elaborate manipulation of heat transfer renders proper operation of diverse thermal-related technologies. However, accurate implementation of thermal-based or transduction sensing on a thin flexible film over unusual surfaces remains challenging. Herein, efficient thermal management realizes highly accurate flexible multifunctional sensor sheets using a low thermal conductive medium as a thermal barrier. An approximately 50-fold enhancement in the thermal sensing accuracy, which is nearly independent of the changes in the external surroundings, is achieved. Such rational control of heat convection and conduction allows to not only dynamically monitor air flow, but also sight the large-scale air flow distribution on curved surfaces using a flexible thermal flow sensor array. Additionally, accurate wearable skin temperature monitoring independent of the sudden surrounding variations is achieved. This work addresses the formidable challenge of untethered heat transfer induced imprecise thermal related sensing, which universally exists in skin-inspired Internet of Things (IoT) applications.

Cupula-Inspired Hyaluronic Acid-Based Hydrogel Encapsulation to Form Biomimetic MEMS Flow Sensors.

Blind cavefishes are known to detect objects through hydrodynamic vision enabled by arrays of biological flow sensors called neuromasts. This work demonstrates the development of a MEMS artificial neuromast sensor that features a 3D polymer hair cell that extends into the ambient flow. The hair cell is monolithically fabricated at the center of a 2 μm thick silicon membrane that is photo-patterned with a full-bridge bias circuit. Ambient flow variations exert a drag force on the hair cell, which causes a displacement of the sensing membrane. This in turn leads to the resistance imbalance in the bridge circuit generating a voltage output. Inspired by the biological neuromast, a biomimetic synthetic hydrogel cupula is incorporated on the hair cell. The morphology, swelling behavior, porosity and mechanical properties of the hyaluronic acid hydrogel are characterized through rheology and nanoindentation techniques. The sensitivity enhancement in the sensor output due to the material and mechanical contributions of the micro-porous hydrogel cupula is investigated through experiments.

Micro-Machined Flow Sensors Mimicking Lateral Line Canal Neuromasts

Fish sense water motions with their lateral line. The lateral line is a sensory system that contains up to several thousand mechanoreceptors, called neuromasts. Neuromasts occur freestanding on the skin and in subepidermal canals. We developed arrays of flow sensors based on lateral line canal neuromasts using a biomimetic approach. Each flow sensor was equipped with a PDMS (polydimethylsiloxane) lamella integrated into a canal system by means of thick- and thin-film technology. Our artificial lateral line system can estimate bulk flow velocity from the spatio-temporal propagation of flow fluctuations. Based on the modular sensor design, we were able to detect flow rates in an industrial application of tap water flow metering. Our sensory system withstood water pressures of up to six bar. We used finite element modeling to study the fluid flow inside the canal system and how this flow depends on canal dimensions. In a second set of experiments, we separated the flow sensors from the main stream by means of a flexible membrane. Nevertheless, these biomimetic neuromasts were still able to sense flow fluctuations. Fluid separation is a prerequisite for flow measurements in medical and pharmaceutical applications.

Active compliant wall for skin friction reduction

In order to reduce skin friction drag, an active laminarisation method is developed. Laminar-turbulent boundary layer transition caused by Tollmien–Schlichting (TS) waves is delayed by attenuation of these convective instabilities. An actively driven compliant wall is integrated as part of a wing’s surface. Different configurations of piezo-based actuators are combined with an array of sensitive surface flow sensors. Wall-normal actuation as well as inclined wall displacement are investigated. Together with a realtime-control strategy, transition onset is shifted downstream by six average TS-wave lengths. Using the example of flow velocity, the influence of variable flow conditions on TS-damping rates was investigated. Besides, the boundary layer flow downstream of the active wall area as well as required wall deflections and the global damping effect on skin friction are presented in this paper.

Underwater tracking of a moving dipole source using an artificial lateral line: algorithm and experimental validation with ionic polymer–metal composite flow sensors

Motivated by the lateral line system of fish, arrays of flow sensors have been proposed as a new sensing modality for underwater robots. Existing studies on such artificial lateral lines (ALLs) have been mostly focused on the localization of a fixed underwater vibrating sphere (dipole source). In this paper we examine the problem of tracking a moving dipole source using an ALL system. Based on an analytical model for the moving dipole-generated flow field, we formulate a nonlinear estimation problem that aims to minimize the error between the measured and model-predicted magnitudes of flow velocities at the sensor sites, which is subsequently solved with the Gauss–Newton scheme. A sliding discrete Fourier transform (SDFT) algorithm is proposed to efficiently compute the evolving signal magnitudes based on the flow velocity measurements. Simulation indicates that it is adequate and more computationally efficient to use only the signal magnitudes corresponding to the dipole vibration frequency. Finally, experiments conducted with an artificial lateral line consisting of six ionic polymer–metal composite (IPMC) flow sensors demonstrate that the proposed scheme is able to simultaneously locate the moving dipole and estimate its vibration amplitude and traveling speed with small errors.

Boundary Layer Separation and Reattachment Detection on Airfoils by Thermal Flow Sensors

A sensor concept for detection of boundary layer separation (flow separation, stall) and reattachment on airfoils is introduced in this paper. Boundary layer separation and reattachment are phenomena of fluid mechanics showing characteristics of extinction and even inversion of the flow velocity on an overflowed surface. The flow sensor used in this work is able to measure the flow velocity in terms of direction and quantity at the sensor's position and expected to determine those specific flow conditions. Therefore, an array of thermal flow sensors has been integrated (flush-mounted) on an airfoil and placed in a wind tunnel for measurement. Sensor signals have been recorded at different wind speeds and angles of attack for different positions on the airfoil. The sensors used here are based on the change of temperature distribution on a membrane (calorimetric principle). Thermopiles are used as temperature sensors in this approach offering a baseline free sensor signal, which is favorable for measurements at zero flow. Measurement results show clear separation points (zero flow) and even negative flow values (back flow) for all sensor positions. In addition to standard silicon-based flow sensors, a polymer-based flexible approach has been tested showing similar results.

Towards a high-resolution flow camera using artificial hair sensor arrays for flow pattern observations

Flow-sensor arrays uncover the potential to measure spatio-temporal flow patterns rather than flow measurements at just a single point. We present in this paper the developments in design, fabrication and interfacing of biomimetic flow-sensor arrays, inspired by flow-sensitive organs (cerci) of crickets. For the purpose of high-resolution flow field visualization by our artificial hair flow-sensor arrays, various array-interfacing schemes are discussed and compared. Frequency division multiplexing (FDM) is shown to be an attractive method for efficient interrogation of capacitive array sensors. Using silicon-on-insulator technology with deep trench isolation structures, hair-based flow-sensors with differential capacitive read-out, arranged in single-chip arrays, have been successfully fabricated. FDM is implemented and used to interrogate individual hair sensors providing simultaneous real-time flow measurements from multiple hairs. This powerful approach is demonstrated by reconstruction of the field of a harmonic dipole field at the position of the hairs and by localizing this dipole source relative to the array elements.

Aircraft Aerodynamic Parameter Detection Using Micro Hot-Film Flow Sensor Array and BP Neural Network Identification

Air speed, angle of sideslip and angle of attack are fundamental aerodynamic parameters for controlling most aircraft. For small aircraft for which conventional detecting devices are too bulky and heavy to be utilized, a novel and practical methodology by which the aerodynamic parameters are inferred using a micro hot-film flow sensor array mounted on the surface of the wing is proposed. A back-propagation neural network is used to model the coupling relationship between readings of the sensor array and aerodynamic parameters. Two different sensor arrangements are tested in wind tunnel experiments and dependence of the system performance on the sensor arrangement is analyzed.

A 3D analysis algorithm to improve interpretation of heat pulse sensor results for the determination of small-scale flow directions and velocities in the hyporheic zone

The hyporheic zone is strongly influenced by the adjacent surface water and groundwater systems. It is subject to hydraulic head and pressure fluctuations at different space and time scales, causing dynamic and heterogeneous flow patterns. These patterns are crucial for many biogeochemical processes in the shallow sediment and need to be considered in investigations of this hydraulically dynamic and biogeochemical active interface. For this purpose a device employing heat as an artificial tracer and a data analysis routine were developed. The method aims at measuring hyporheic flow direction and velocity in three dimensions at a scale of a few centimeters. A short heat pulse is injected into the sediment by a point source and its propagation is detected by up to 24temperature sensors arranged cylindrically around the heater. The resulting breakthrough curves are analyzed using an analytical solution of the heat transport equation. The device was tested in two laboratory flow-through tanks with defined flow velocities and directions. Using different flow situations and sensor arrays the sensitivity of the method was evaluated. After operational reliability was demonstrated in the laboratory, its applicability in the field was tested in the hyporheic zone of a low gradient stream with sandy streambed in NE-Germany. Median and maximum flow velocity in the hyporheic zone at the site were determined as 0.9×10−4 and 2.1×10−4ms−1 respectively. Horizontal flow components were found to be spatially very heterogeneous, while vertical flow component appear to be predominantly driven by the streambed morphology.

Fabrication of superficial neuromast inspired capacitive flow sensors**This paper is an extension of Izadi et al, ‘Fabrication of dense flow sensor arrays on flexible membranes’ presented at IEEE Transducer 2009, Denver, Colorado.

A fabrication process is presented which allows realizing the dense arrays of fully supported flexible SU-8 membranes with integrated electrodes underneath that support cylindrical hair-like structures on the top. Electrodes have been insulated from liquid to prevent short circuit or electrolysis. The process allows a controllable distance between counter electrodes. The excess squeeze film damping is eliminated with a closed membrane configuration with backside openings. A low-power differential capacitive measurement mechanism is employed as a sensor readout. These flow sensors provide a distributed sensing mechanism inspired by the superficial neuromast as found in the lateral line system of fish. This paper focuses on the fabrication of the sensors with preliminary mechanical results demonstrating the functionality of the fabricated devices.

Dipole-source localization using biomimetic flow-sensor arrays positioned as lateral-line system

Flow-sensor arrays can be used to extract spatio-temporal flow signatures rather than average or local flow quantities. In this paper, we focus on the equivalent of a fish lateral-line sensor array in air and assess the ability of our artificial flow-sensor arrays to detect flow-velocity distributions generated by a vibrating sphere. The effect of various source parameters on the source-distance determination is investigated. The measured flow patterns along a virtual lateral-line are found to be in excellent agreement with theory and are used successfully to demonstrate source-distance determination.

Dipole source localisation using bio-mimetic flow-sensor arrays

Flow sensor arrays can be used to extract spatio-temporal flow signatures rather than average or local flow quantities. We look at the equivalent of a fish lateral-line sensor array in air and assess the ability of our artificial hairs flow-sensor arrays to detect flow velocity distributions generated by a vibrating sphere. The measured flow patterns along a virtual lateral-line, carried out over a bandwidth of 0.3 Hz, are found to be in excellent agreement with theory and are used successfully to demonstrate source-distance determination.