Here, we detail and analyse a set of tailored experiments to capture the flow dynamics of coughs and sneezes. Specifically, conventional particle tracking-based flow experiments are tailored to capture the wide range of spatial and temporal scales in the flow through the use of high-speed and high-resolution imaging systems. In doing so, we captured droplet velocities over a wide range of particle sizes with a large field-of-view in the order of a metre to capture the full flow field, which is challenging to achieve through conventional methods. A simultaneous direct measure of droplet sizing and velocity is also obtained through back-illuminated imaging experiments, albeit over a smaller spatial extent. A statistical assessment of the droplets' flow-field reveals good agreement to prior works, now extending this over a much larger spatial extent. Through a statistical analysis of representative sneeze and cough samples, we highlight the potential of the presented experimental techniques to provide detailed flow dynamics. In particular, we observe that droplets are expelled over a significantly shorter period than the airflow generated in a cough; in contrast, droplets appear to be continuously expelled during the span of a sneeze. Furthermore, due to the lower momentum in coughs, droplets exhibit aerosolised behaviour at an earlier stage than sneezes, where the stronger flow-field expelled dictates the droplet dynamics through the bulk of the sneeze duration. These findings reveal crucial differences between coughs and sneezes, which are essential when modelling such flow accurately or designing pressure-driven simulators.
Emerging Leaders 2021
Measurement Science and Technology is pleased to bring together the best early-career researchers in measurement and metrology and publish their exceptional work in an annual collection dedicated to emerging leaders.
An emerging leader is defined as a top researcher in their field who completed their PhD in 2009 or later (10 years excluding career breaks).
Papers
Charitha de Silva Charitha de Silva received his PhD from the Department of Mechanical Engineering at the University of Melbourne in December 2014. He was a postdoctoral researcher in the same department from 2015–2018. From January 2019 onwards, he was appointed a lecturer in the School of Mechanical and Manufacturing Engineering at the University of New South Wales. His research focus spans a variety of fluid flows, including wall-bounded flows, microfluidics, biofluids and aerobiology. He is also pursuing to advance image-based flow measurement techniques at both the macro and micro scales.
Mitchell Spearrin Dr. Mitchell Spearrin is an Associate Professor of Mechanical and Aerospace Engineering at the University of California Los Angeles (UCLA). Prof. Spearrin's research focuses on spectroscopy and optical sensors, including laser absorption and fluorescence, with experimental application to propulsion and energy systems. His interests extend to chemical kinetics studies with relevance to pollutant formation and alternative fuels, as well as broader applications of spectroscopy for environmental monitoring and biomedical devices. Dr. Spearrin completed his PhD at Stanford University in 2015. Prior to his academic career, Dr. Spearrin worked for Pratt & Whitney Rocketdyne as a propulsion system development engineer.
In this work, a method for experimentally determining a local characteristic velocity, 
, is presented for purposes of analyzing rocket combustion progress based on in-situ laser absorption spectroscopy measurements of temperature and gas composition. Measuring 
from spatially-varying thermochemical properties provides an alternative to the classical 
evaluation, which uses global chamber pressure and mass flow rate measurements. Accordingly, the novel method provides more detailed insight to the underlying mechanisms (multi-phase thermochemistry, diffusive mixing, turbulence, etc) governing combustion performance in the spatial domain. The method is demonstrated on an RP-2/O2 liquid-propellant rocket engine (LRE) and a PMMA/O2 hybrid rocket combustion experiment. Localized 
results for the LRE are obtained via in-chamber measurements of temperature, CO, and CO2 and are presented over a range of pressures (
28–83 bar) and mixture ratios (MR = 2.5–5). For the hybrid rocket combustion experiment, one-dimensional tomographic reconstruction techniques are used to spatially-resolve the flow-field thermochemistry and obtain spatially-resolved measurements of temperature, CO, CO2, and H2O. These measurements are compiled to obtain spatially-resolved 
images of the combustion zone. The 
results from both experiments are compared to the theoretical 
expected from chemical equilibrium, providing for a method to assess combustion performance or progress locally within the combustion zone.
Ricardo Mejia-Alvarez Dr Ricardo Mejia-Alvarez is an Assistant Professor in the Department of Mechanical Engineering at Michigan State University. He received his PhD degree in Theoretical and Applied Mechanics from the University of Illinois at Urbana-Champaign. He worked in Los Alamos National Laboratory from 2010 to 2016. Dr. Mejia-Alvarez studies shock-matter interactions, with emphasis on their effect on living tissue and the mechanics of TBI. Some of his accolades include the 2011 Francois Frenkiel Award for Fluid Dynamics from the American Physical Society and a Fulbright Fellowship.
This study shows the basic design and experimental characterization of the advanced blast chamber at Michigan State University. This facility is a large cross-section explosively-driven blast chamber. The cross-section of the facility is 2.03 m × 2.03 m, and the length of its tunnel is 5.5 m. This relatively short length was made possible by introducing a new driver design shaped like a pair of logarithmic spirals with a coincident focus. The experimental characterization of the facility demonstrates that this driver design produces blast fronts with very low curvature, and overpressure durations as short as ∼1.2 ms. Since this was the initial characterization of the facility, the maximum overpressure considered was ∼144 kPa. This facility was conceived to perform studies of blast-induced traumatic brain injury based on full-size models of the human body or large animal models. Its large cross section ensures that area blockage is within permissible values, and its driver design ensures short overpressure durations typical of battle field blast events.
Bo Dong Bo Dong received his PhD in 2017 from Guangdong University of Technology (with Prof. Yun Zhang), before carrying out postdoctoral research at Beihang University (With Prof. Bing Pan) and Guangdong Hong Kong Macao Joint Lab Smart Discrete (with Prof. Shengli Xie, and Prof. Yong Liang). Currently, he is an assistant professor at Guangdong University of Technology. His research interests focus on optical measurement technology and instrument, especially optical coherence tomography, optical coherence elastography, digital image/volume correlation, digital gradient sensing, and chromatic confocal displacement sensor.
A method combining phase-contrast technique and spectral-domain optical coherence tomography (OCT) has been recently proposed for visualizing curing behaviors inside polymers (2020 Appl. Phys. Lett. 116 054103). Here, based on the method, a non-contact and highly-sensitive optical sensor is further developed to monitor the photocuring process of light-cured polymers. Compared with the existing method, the proposed optical sensor features two distinct advantages: (a) the sensor uses a point-detection OCT, rather than a line-field OCT, to capture the interference signal, which significantly improves its practicability and the measuring speed; (b) the sensor can simultaneously monitor the shrinkage strains and refractive index variations during a photocuring process, featuring enhanced functionality in practical applications. For validation, a cure monitoring experiment was carried out, proving that the sensor can accurately monitor the shrinkage strain and refractive index variation during the polymer photocuring process. The polymer coating fabrication process and the polymer adhesive process were also monitored, showing the effectiveness and practicability of the sensor.
Frieder Lucklum Frieder Lucklum received his Dipl.-Ing. (MSc) degree in microsystem technology from the Otto-von-Guericke University Magdeburg, Germany, in 2005 and his PhD degree in the field of acoustic microsensors from the Johannes Kepler University Linz, Austria, in 2010. In January 2020, he was appointed Professor MSO at DTU Electrical Engineering, Technical University of Denmark (DTU) and became head of the Centre for Acoustic-Mechanical Microsystems (CAMM). He is a senior member of IEEE and Tutorial Co-Chair for the IEEE Sensors conference 2020 and 2021. His research areas include acoustic microsystems, additive manufacturing, phononic crystals and devices, and electroacoustic systems.
A phononic-fluidic cavity sensor is a new type of acoustic fluid sensor to measure volumetric liquid properties. In our work, it consists of solid-air 3D phononic crystal (PnC) layers confining a fluidic cavity resonator to generate a strong, well separated cavity resonance within the phononic band gap. This allows for the measurement of changes in speed of sound of a liquid analyte with very high, linear sensitivity. In this work, we present theoretical and experimental results for very sensitive determination of sodium chloride and glucose concentrations in aqueous solutions. The 3D-printed measurement cell consists of a rectangular liquid chamber surrounded by an optimized PnC with a simple cubic ball and beam design acting as a metamaterial combining Bragg and local resonance scattering to create optimal boundary conditions for the liquid cavity resonator. Analytical transmission line modeling is used to illustrate the working principle of the sensor. Numerical finite element models describe the phononic band structure and transmission behavior, as well as the frequency response of the sensor element at different mass fractions of the sample solutions as validation for our experiments. A high sensitivity of concentration and subsequently speed of sound is demonstrated over a very large concentration range of 0%–30%.
Filippo Coletti Filippo Coletti is Professor of Experimental Fluid Dynamics at ETH Zurich. Previously he was McKnight Land-Grant Associate Professor of Aerospace Engineering & Mechanics at the University of Minnesota, which he joined in 2014. He performed his doctoral studies at the von Karman Institute for Fluid Dynamics and at the University of Stuttgart, where he obtained his PhD in 2010, and was postdoctoral fellow at Stanford University between 2011 and 2013. His research interests are focused on particle-laden flows, which he studies with a wide spectrum of experimental approaches and with applications to both biomedical and environmental problems.
Polymer solutions are often used to produce drag-reduced fluid flows, in which the drag reduction is achieved due to the solutions' non-Newtonian shear-thinning and viscoelastic properties. However, experiments using polymer solutions are typically challenging due to the tendency of the polymer to degrade when subjected to intense shearing. The degradation reduces the amount of drag reduction as the experiment progresses, which limits the experiment duration and the accuracy of the results. Here we introduce a method to avoid the degradation of the polymer solution by driving the flow with a paddlewheel instead of a conventional pump. The solution is shown to undergo very little degradation during the paddlewheel's operation. The method is then applied to perform novel measurements of a drag-reduced turbulent boundary layer at two different Reynolds numbers, both with and without a suspended particle phase. The effects of carrier fluid rheology and Reynolds number on the particle concentration and velocity profiles are explored, as well as the effect on total drag of the flow. For a given fluid type and Reynolds number, the drag is found to be nearly constant with the global particle volume fraction, suggesting that the particles have a limited ability to modulate the drag. Remarkably, the particle velocity fluctuations are greater in the non-Newtonian cases, possibly due to enhanced collisions in the near-wall region.
Amir Khodabandeh Amir Khodabandeh received his PhD degree (with distinction) in Geodesy and Global Navigation Satellite Systems (GNSS) from Curtin University, Perth, Australia, in 2015. He is currently a lecturer (Assistant Professor) at the Department of Infrastructure Engineering, the University of Melbourne, Australia. He is also Chair of a study group of the International Association of Geodesy (IAG): Multi-GNSS Theory and Algorithms, and a member of the IAG Working Group: Reliability of low-cost & Android GNSS in navigation and geosciences. His research interests include estimation theory, interferometric positioning, and GNSS quality control.
The global navigation satellite systems (GNSS) carrier phase measurements form the basis of high-precision satellite positioning. These measurements are often accompanied by their code counterparts to enable one to compute single-epoch ambiguity-resolved positioning solutions. To avoid unwanted code modelling errors, such as code multipath, one may opt for a phase-only solution and take recourse to carrier phase measurements of two successive epochs. In this paper we study the ambiguity resolution performance of a dual-epoch phase-only model, upon which the unknown positioning parameters are assumed to be completely unlinked in time. With the aid of closed-form analytical results, it is investigated how ambiguity resolution performs when dealing with high-rate phase data. It is thereby shown that multi-GNSS integration makes near real-time centimetre-level phase-only positioning possible. Our analytical analysis is supported by means of numerical results.
Pai-Yen Chen Pai-Yen Chen is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Chicago. He received his PhD degree from the University of Texas at Austin in 2013. He was awarded National Science Foundation CAREER Award, IEEE Sensors Council Young Professional Award, SPIE Rising Researcher Award, ACES Early Career Award, Young Scientist Awards from PIERS and URSI societies, IEEE Raj Mittra Travel Grant, and Donald Harrington Dissertation Fellowship, among others. His research interests focus on applied electromagnetics, sensors and integrated systems, radio-frequency telemetry, and wireless measurement techniques.
We herein introduce a wireless dielectric sensing platform that enables long-range interrogation of compact and fully-passive chipless sensors in complex environments filled with clutters and self-jamming. This telemetry technique requires postprocessing the harmonic received signal strength indicator (RSSI) in the frequency-hopping spread spectrum (FHSS), for which a harmonic transponder sensor (or harmonic sensor) has a narrow-band antenna sensor operating at frequency f0 and a wideband antenna covering the spectrum centered at 2f0. As a representative example, we have demonstrated that the information of the microfluids-reconfigured antenna sensor can be successfully encoded in the peak frequency of the harmonic RSSI pattern. Importantly, different from traditional passive backscatter sensors and radio-frequency identification tags, which are vulnerable to the background electromagnetic noises, this FHSS-assisted harmonic-based telemetry method allows robust far-field interrogation of sensors in realistic fully-scattered channels such as indoor environments. We envision that the lightweight battery-free harmonic sensor and system may be beneficial for widespread applications in internet-of-things, industry 4.0, smart cities, and healthcare applications such as rapid contactless point-of-care tests and telemedicine.
Duan Li Duan Li received a PhD degree in measurement technology and instrumentation from the School of Instrumentation and Optoelectronic Engineering, Beihang University (BUAA), and was elected into the National Postdoctoral Program for Innovative Talents in 2017. From 2017 to 2019, he a postdoctoral fellow at the School of Electronic and Information Engineering, Beihang University. He currently holds an assistant professor position with the School of Instrumentation and Optoelectronic Engineering, Beihang University. His current research interests include LiDAR signal processing, system design and application.
To improve the on-line laser-ranging performance of light detection and ranging (LiDAR), the run time and resource consumption of the commonly used Gauss–Newton method need to be considered under the condition of high-frequency laser echo scattered from multiple targets. Limited by insufficient hardware processing units and the requirement of increased measurement speed, a multi-target laser ranging based on matrix sparsification and a division-free Gauss–Jordan solver implemented on a field programmable gate array is proposed. A Jacobian matrix sparsification method is used to reduce the number of multiplication operations in the coefficient matrix and constant vector calculations, thereby reducing hardware resources and time costs. In order to increase the hardware implementation efficiency, a division-free Gauss–Jordan elimination method is used to quickly solve linear equations. The Vivado-based simulation results show that the amount of hardware resources, such as look-up tables, flip-flops and digital signal processor (DSP) are reduced by 34.5%, 35.7% and 27.3%, respectively, and the time cost is reduced by 3 μs (750 clock cycle @250 MHz) in the case of five iterations, satisfying the real-time requirements for a measurement rate of 45.45 kHz at most. Experimental results show that the proposed method maintains comparable performance of point extraction ability, ranging precision and accuracy, which are 100.0%, 1.78 cm and 0.62 cm@35.64 dB for two-target laser ranging and 98.03% 1.89 cm and 0.60 cm@35.91 dB for three-target laser ranging. For the field of multi-target real-time ranging full-waveform LiDAR, it is a promising method due to its low time cost and resource consumption while ensuring ranging performance.
Henry Ng Henry Ng completed his PhD in Mechanical Engineering at the University of Melbourne under the supervision of Professors Ivan Marusic, Jason Monty and Min Chong. He has since held several postdoctoral positions working with Professors Gustavo Gioia and Pinaki Chakraborty at the Okinawa Institute of Science and Technology, Dr John Coull at the University of Cambridge and Professor Robert Poole and co-workers at the University of Liverpool. His research interests primarily focus on experimental fluid mechanics including wall-bounded turbulent flows, gas turbine aerodynamics, the rheology of complex fluids and the design and construction of wind tunnels and flow rigs.
The capillary breakup extensional rheometer is commonly used to determine material properties of complex fluids. This is achieved by tracking the diameter evolution of a liquid bridge undergoing capillary thinning and breakup in a uniaxial extensional flow. Typically, the filament diameter evolution is tracked at the mid-plane between the two end-plates using a laser micrometer. We show using high-speed imaging that while this arrangement is satisfactory in flows where the filament is long (relative to its initial diameter), slender and approximately cylindrical, errors can be significant when the filaments are short (and with a non-negligible curvature) such as encountered when using the so-called slow-retraction-method and 'Dripping-onto-Substrate' rheometry. We will further highlight the need for high-speed imaging in CaBER experiments by considering errors induced when the laser micrometer is misaligned with the location of filament breakup. This latter source of error will be particularly relevant for capillary breakup experiments where the location of filament breakup is not typically known a priori, such as the case for many so-called 'yield-stress' fluids.
Jiangtao Sun Jiangtao Sun received B.Eng. and M.Eng degrees from Xidian University, Shaanxi, China, in 2008 and 2010, respectively, and the PhD degree from The University of Manchester in 2014. In September 2014, he joined the Brunel Innovation Centre as a research fellow and worked there for two years. Thereafter from October 2016 to April 2017, he was an associate professor in Biomedical Engineering with Sun Yat-Sen University, Guangzhou, China. He is now an associate professor in Instrumentation Science with Beihang University, Beijing, China. His current research interests include electrical capacitance/resistance tomography for industrial and biomedical applications.
Driven chains, also called current injection blocks, are one of the main sub-blocks of electrical impedance tomography (EIT) systems. In most applications, the noise performance of a driven chain is a critical figure of merit, which limits the signal-to-noise ratio of an EIT system. Accurate noise analysis of driven chains with improved Howland circuits is still missing in the EIT literature. In this paper, an accurate noise model of a driven chain with an improved Howland current source for an EIT system is presented. Both simulated and lab-built driven-chain circuits are employed to validate our model. It is shown that the simulation and test results closely match. This work provides both an accurate noise model and a noise budget method for EIT driven chains with an improved Howland current source.
Beiwen Li Dr Beiwen Li is an Assistant Professor of Mechanical Engineering and the director of Analytical 3D Optical Sensing Laboratory at Iowa State University. He received his PhD degree in Mechanical Engineering from Purdue University in 2017. His research focuses on superfast kilohertz 3D optical sensing, precision 3D optical metrology, 3D point cloud data analysis and in-situ monitoring for additive manufacturing. Several of his research works have been highlighted on the cover page of prestigious journals including Optics Express, Applied Optics and G??otechnique Letters. He is the recipient of 2020 SPIE Defense & Commercial Sensing Rising Researcher Award.
Fringe projection profilometry (FPP) is a three-dimensional (3D) shape measurement method that involves projecting fringe patterns onto the object. A phase map retrieved from these fringe images is used for reconstructing the 3D surface of the object. Fourier transform and phase-shifting are two of the widely used fringe analysis techniques for performing 3D shape measurement using FPP. Fourier transform profilometry (FTP) has the advantage of performing high-speed measurement due to its single-shot nature. However, the reconstructed 3D surface has artifacts and high noise, specifically on the edges. On the other hand, phase-shifting profilometry (PSP) has the advantage of higher accuracy (relatively less noise level) but compromises on measurement speed. In this research, we propose a deep learning method to enhance the quality of the FTP phase maps using an efficient deep learning model called phase map enhancement net (PMENet). PMENet takes an FTP phase map as input and predicts a high-quality phase map in a supervised manner by using the phase maps obtained from 18-step PSP as ground truth. The training dataset was generated using a virtual FPP system. Validations were conducted on both the simulated data (generated by virtual FPP system) and the real-world data. The experimental results demonstrate that the trained neural network model can successfully improve the quality of the 3D geometric reconstruction with FTP and reduced the mean and root-mean-square error by 66% and 43%, respectively.