Flexible Micropost Arrays for Shear Stress Measurement

Increased fuel costs, heightened environmental protection requirements, and noise abatement continue to place drag reduction at the forefront of aerospace research priorities. Unfortunately, shortfalls still exist in the fundamental understanding of boundary-layer airflow over aerodynamic surfaces, especially regarding drag arising from skin friction. For example, there is insufficient availability of instrumentation to adequately characterize complex flows with strong pressure gradients, heat transfer, wall mass flux, three-dimensionality, separation, shock waves, and transient phenomena. One example is the acoustic liner efficacy on aircraft engine nacelle walls. Active measurement of shear stress in boundary layer airflow would enable a better understanding of how aircraft structure and flight dynamics affect skin friction. Current shear stress measurement techniques suffer from reliability, complexity, and airflow disruption, thereby compromising resultant shear stress data. The state-of-the-art for shear stress sensing uses indirect or direct measurement techniques. Indirect measurements (e.g., hot-wire, heat flux gages, oil interferometry, laser Doppler anemometry, small scale pressure drag surfaces, i.e., fences) require intricate knowledge of the studied flow, restrictive instrument arrangements, large surface areas, flow disruption, or seeding material; with smaller, higher bandwidth probes under development. Direct measurements involve strain displacement of a sensor element and require no prior knowledge of the flow. Unfortunately, conventional "floating" recessed components for direct measurements are mm to cm in size. Whispering gallery mode devices and Fiber Bragg Gratings are examples of recent additions to this type of sensor with much smaller (m) sensor components. Direct detection techniques are often single point measurements and difficult to calibrate and implement in wind tunnel experiments. In addition, the wiring, packaging, and installation of delicate micro-electromechanical devices impede the use of most direct shear sensors. Similarly, the cavity required for sensing element displacement is sensitive to particulate obstruction. This work was focused on developing a shear stress sensor for use in subsonic wind tunnel test facilities applicable to an array of test configurations. The non-displacement shear sensors described here have minimal packaging requirements resulting in minimal or no disturbance of boundary layer flow. Compared to previous concepts, device installation could be simple with reduced cost and down-time. The novelty lies in the creation of low profile (nanoscale to 100 m) micropost arrays that stay within the viscous sub-layer of the airflow. Aerodynamic forces, which are related to the surface shear stress, cause post deflection and optical property changes. Ultimately, a reliable, accurate shear stress sensor that does not disrupt the airflow has the potential to provide high value data for flow physics researchers, aerodynamicists, and aircraft manufacturers leading to greater flight efficiency arising from more in-depth knowledge on how aircraft design impacts near surface properties

Shear Stress Sensing with Elastic Microfence Structures

In this work, elastic microfences were generated for the purpose of measuring shear forces acting on a wind tunnel model. The microfences were fabricated in a two part process involving laser ablation patterning to generate a template in a polymer film followed by soft lithography with a two-part silicone. Incorporation of a fluorescent dye was demonstrated as a method to enhance contrast between the sensing elements and the substrate. Sensing elements consisted of multiple microfences prepared at different orientations to enable determination of both shear force and directionality. Microfence arrays were integrated into an optical microscope with sub-micrometer resolution. Initial experiments were conducted on a flat plate wind tunnel model. Both image stabilization algorithms and digital image correlation were utilized to determine the amount of fence deflection as a result of airflow. Initial free jet experiments indicated that the microfences could be readily displaced and this displacement was recorded through the microscope

Business analytics in industry 4.0: a systematic review

Recently, the term “Industry 4.0” has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet-of-Things, implementation of digital production support information technologies). Business Analytics is often used within the Industry 4.0, thus incorporating its data intelligence (e.g., statistical analysis, predictive modelling, optimization) expert system component. In this paper, we perform a Systematic Literature Review (SLR) on the usage of Business Analytics within the Industry 4.0 concept, covering a selection of 169 papers obtained from six major scientific publication sources from 2010 to March 2020. The selected papers were first classified in three major types, namely, Practical Application, Reviews and Framework Proposal. Then, we analysed with more detail the practical application studies which were further divided into three main categories of the Gartner analytical maturity model, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. In particular, we characterized the distinct analytics studies in terms of the industry application and data context used, impact (in terms of their Technology Readiness Level) and selected data modelling method. Our SLR analysis provides a mapping of how data-based Industry 4.0 expert systems are currently used, disclosing also research gaps and future research opportunities.The work of P. Cortez was supported by FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. We would like to thank to the three anonymous reviewers for their helpful suggestions

Optimisation of Coherent Fourier Scatterometry for Side Wall Angle Estimation of Printed Structures

The electronic industry strives continuously to increase the performance of electronic components by adding new functionality, by making them more energy efficient or by increasing their absolute performance. This last possibility is mainly achieved by a higher density of electrical components. In a photo-lithographic process, this is associated with the ability of printing finer and finer details on a wafer, without impacting the speed and the accuracy of the overall process. In industry, this concept is expressed with a single word: yield. The higher the yield, the more profits for the chip producer. One of the often chosen procedures to improve the yield is through a tight control of key quantities related to the chip making process such as dose, focus, overlay and other relevant parameters in order to ensure the creation of a defect free device. In other words, through metrology. The root of this word is derived from the ancient Greek and it stands for the science of measuring. In the description of a target, it is often convenient to parametrize it with few geometrical quantities that are chosen as representative of dimensions or specific features. In the field of metrology applied to the semiconductor industry, one of the most used test targets are periodic gratings. These objects are usually described in terms of four quantities: height h, middle critical dimension MidCD, period (often named pitch) p and the side wall angle SWA; this last quantity represents the angle between the edge of a trench and the bottom of a line. Particularly, we focus our efforts on improving the estimation of the side-wall angle. Improving the quality of printed target relying on metrology techniques is the underlying motivation of this dissertation and the ultimate goal behind it…ImPhys/Optic

Modeling Value of Information in Remote Sensing from Correlated Sources

This paper investigates remote sensing networks and discusses different models to characterize the Value of Information (VoI), a metric that describes how informative the data transmitted by the sensors are. For each sensor, the VoI evaluations comprise the average node-specific Age of Information (AoI), the average cost spent for sending updates, and the AoI of neighbor nodes, assumed to be correlated sources of information and therefore benefiting the VoI of other sensors nearby. We discuss how this metric can be tracked through a two-dimensional Markov chain, but we also show how this representation can be simplified by including the impact of neighbor nodes within the transition probabilities, so as to obtain a simpler model that gives the same insight in terms of VoI evaluations

Challenges of the Age of Information Paradigm for Metrology in Cyberphysical Ecosystems

We are facing a transition towards interconnection of computing systems, people, and things, where boundaries are disappearing and new challenges are emerging. This trend also applies to smart living environments, which are becoming a cyberphysical ecosystem of devices and individuals. Generally, meta-descriptors such as age of information are exploited to obtain efficient content representation and semantic characterization, with the advantage of better data handling. However, the strong relevance of living support in the involved applications imposes to rethink of this approach whenever it is important to factor the human-in-the-loop. In this paper, we discuss how the investigations related to age of information, in particular aimed at statistical descriptions and/or network operation modeling, can be influenced in such scenarios, for what concerns overarching machine learning for data classification and its impact on the sensing frequency, as well as the presence of data correlation that allows for a parsimonious handling of the updates

Tackling Age of Information in Access Policies for Sensing Ecosystems

Recent technological advancements such as the Internet of Things (IoT) and machine learning (ML) can lead to a massive data generation in smart environments, where multiple sensors can be used to monitor a large number of processes through a wireless sensor network (WSN). This poses new challenges for the extraction and interpretation of meaningful data. In this spirit, age of information (AoI) represents an important metric to quantify the freshness of the data monitored to check for anomalies and operate adaptive control. However, AoI typically assumes a binary representation of the information, which is actually multi-structured. Thus, deep semantic aspects may be lost. In addition, the ambient correlation of multiple sensors may not be taken into account and exploited. To analyze these issues, we study how correlation affects AoI for multiple sensors under two scenarios of (i) concurrent and (ii) time-division multiple access. We show that correlation among sensors improves AoI if concurrent transmissions are allowed, whereas the benefits are much more limited in a time-division scenario. Furthermore, we discuss how ML can be applied to extract relevant information from data and show how it can further optimize the transmission policy with savings of resources. Specifically, we demonstrate, through simulations, that ML techniques can be used to reduce the number of transmissions and that classification errors have no influence on the AoI of the system