Non-invasive Flow Measurement Research Articles

Unbalanced Optimal Transport For Stochastic Particle Tracking

Non-invasive flow measurement techniques, such as particle tracking velocimetry, resolve 3D velocity fields by pairing tracer particle positions in successive time steps. These trajectories are crucial for evaluating physical quantities like vorticity, shear stress, pressure, and coherent structures. Traditional approaches deterministically reconstruct particle positions and extract particle tracks using tracking algorithms. However, reliable track estimation is challenging due to measurement noise caused by high particle density, particle image overlap, and falsely reconstructed 3D particle positions. To overcome this challenge, probabilistic approaches quantify the epistemic uncertainty in particle positions, typically using a Gaussian probability distribution. However, the standard deterministic tracking algorithms relying on nearest-neighbor search do not directly extend to the probabilistic setting. Moreover, such algorithms do not necessarily find globally consistent solutions robust to reconstruction errors. This paper aims to develop a globally consistent nearest-neighborhood algorithm that robustly extracts stochastic particle tracks from the reconstructed Gaussian particle distributions in all frames. Our tracking algorithm relies on the unbalanced optimal transport theory in the metric space of Gaussian measures. Specifically, we optimize a binary transport plan for efficiently moving the Gaussian distributions of reconstructed particle positions between time frames. We achieve this by computing the partial Wasserstein distance in the metric space of Gaussian measures. Our tracking algorithm is robust to position reconstruction errors since it automatically detects the number of particles that should be matched through hyperparameter optimization. Notably, our tracking algorithm also readily applies to the standard deterministic PTV case. Finally, we validate our method using an in vitro flow experiment using a 3D-printed cerebral aneurysm.

Estimation of local arterial stiffness using non-invasive flow measurements: a study on a stenosed carotid artery

Arterial stiffness is a well-established independent prognostic bio-marker in the human cardiovascular system (CVS). Despite its value as a predictive bio-marker, the inclusion of vascular stiffness into standard clinical procedures or guidelines is limited. This has come about as a result of the abundance of tools created to estimate and evaluate local arterial stiffness in people. This study employs a non-invasive technique to measure local arterial stiffness in the human carotid arteries using proximal flow measurements. Within this work, a viscoelastic lumped-parameter (0D) model of the carotid arteries is developed in complement with the non-invasive flow measurements obtained from the Doppler ultrasound. Further, concepts of sensitivity analysis and parameter estimation are applied to estimate arterial stiffness using the flow measurement taken at the common carotid artery (CCA). After the successful estimation of arterial stiffness in CCA, results show good agreement between model simulations and actual measurement.

Removal of the effect of the high solids holdup ring in the fluidized bed on electromagnetic tomography

Electromagnetic tomography (EMT) system based on tunneling magneto resistance (TMR) sensor, as a non-invasive multiphase flow measurement technique, can effectively detect the solid-phase distribution in the gas–liquid–solid three-phase fluidized bed with magnetic catalysts by reconstructing the permeability distribution image. The radial distribution of solid particles in the gas–liquid–solid three-phase fluidized bed with uneven fluidization is non-uniform, in which a high solids holdup ring exists near the wall of the riser. When the TMR-EMT system is used directly for image reconstruction, the high solids holdup ring will greatly reduce the image quality, further leading to the inability to detect phase information in the riser. To improve the reconstructed image by the TMR-EMT system, a removal method for the high solids holdup ring is proposed. The method firstly estimates the equivalent thickness of the high solids holdup ring based on the boundary measurement data. Then the influence of the high solids holdup ring on the boundary measurement data is subtracted. By the removal method, the internal phase distribution information is prominent. The simulation and experimental results demonstrate that the proposed method can realize the permeability distribution detection by the TMR-EMT system in the presence of the high solids holdup ring.

Measurement of a radial flow profile with eddy current flow meters and deep neural networks *

Eddy current flow meters (ECFMs) measure flows of conductive fluids. Recent interest in ECFMs has increased due to applications in advanced nuclear reactors. ECFMs are well suited for such applications, as they can provide non-invasive measurements of flow in fluids that are often difficult to measure. Traditionally, ECFMs are operated using an alternating current at a single frequency, limiting ECFMs to measure average fluid velocities, blockages, or voids. We expand the capabilities of ECFMs by measuring the fluid radial velocity profile of liquid mercury. To accomplish this, we made several ECFM sensitivity measurements at a range of frequencies. Different frequencies vary the electromagnetic skin depth of the device. By adjusting frequencies, we probed the fluid velocity at various radial locations and constructed a flow-velocity profile. The relationship between the ECFM measurements and velocity profile is nonlinear and requires solving an inverse problem. Using electromagnetic finite-element simulations to train a deep neural network (DNN), we created a model that provides a stable general relationship between the sensitivity measurements of an ECFM and the fluid velocity profile. Using ECFM measurements of liquid mercury, our DNN model calculates a flow profile that agrees well with computational fluid dynamics (CFD) simulations. This technique has potential to improve flow monitoring for optimization, safe operation of conductive fluid loops, and/or validating complex CFD models.

Comparing non-invasive diagnostic methods for arteriovenous fistula stenosis: a prospective study.

International guidelines recommend screening for arteriovenous fistula (AVF) stenosis using various non-invasive methods. We evaluate different non-invasive AVF flow measurements for detecting AVF stenosis. Twenty-three haemodialysis patients with suspected AVF stenosis are enrolled based on abnormal physical signs or high venous pressure during dialysis. Ultrasound dilution, urea dilution, Doppler ultrasonography, and fistulography are performed on all patients. The accuracy of three non-invasive methods is compared. Fistulography reveals AVF stenosis in 18 patients, 12 of whom have severe stenosis (greater than 50% stenosis in diameter). Concerning the location of the stenosis lesions, eight are at the inflow site, six at the outflow site, and four on both sites. Receiver operating characteristic curve analysis shows that Doppler ultrasonography has a high discriminative ability and the averaged areas under the curves are 0.933 (95% confidence interval [CI]; 0.81 to 0.99) for stenosis and 0.929 (95% CI 0.82-0.99) for severe stenosis. The sensitivity of each method for the prediction of access stenosis using ultrasound dilution, urea dilution, and Doppler ultrasonography is 73%, 73%, and 80%, respectively. The respective specificity of each method is 40%, 80%, and 100%, respectively. Physical examination (PE) shows an 80% sensitivity and 80% specificity in the detection of AVF stenosis. The combination of Doppler ultrasound with PE produces the highest sensitivity (93%) for detecting AVF stenosis. Doppler ultrasound combined with physical examination is more accurate than other non-invasive methods for detecting AVF stenosis.

Simulation Uncertainty for a Virtual Ultrasonic Flow Meter

Ultrasonic clamp-on meters have become an established technology for non-invasive flow measurements. Under disturbed flow conditions, their measurement values must be adjusted with corresponding fluid mechanical calibration factors. Due to the variety of flow disturbances and installation positions, the experimental determination of these factors often needs to be complemented by computational fluid dynamics (CFD) simulations. From a metrological perspective, substituting experiments with simulation results raises the question of how confidence in a so-called virtual measurement can be ensured. While there are well-established methods to estimate errors in CFD predictions in general, strategies to meet metrological requirements for CFD-based virtual meters have yet to be developed. In this paper, a framework for assessing the overall uncertainty of a virtual flow meter is proposed. In analogy to the evaluation of measurement uncertainty, the approach is based on the utilization of an expanded simulation uncertainty representing the entirety of the computational domain. The study was conducted using the example of an ultrasonic clamp-on meter downstream of a double bend out-of-plane. Nevertheless, the proposed method applies to other flow disturbances and different types of virtual meters. The comparison between laboratory experiments and simulation results with different turbulence modeling approaches demonstrates a clear superiority of hybrid RANS-LES models over the industry standard RANS. With an expanded simulation uncertainty of 1.44 × 10−2, the virtual measurement obtained with a hybrid model allows for a continuous determination of calibration factors applicable to the relevant mounting positions of a real meter at a satisfactory level of confidence.

ABB Robotic Laser Doppler Anemometry Measurements With RobotStudio® With An Open Wind Channel And An Ellipsoid

Laser Doppler anemometry is a state-of-the-art non-invasive experimental flow measurement technique. Traditionally used linear traversing units can only change the position of the measurement probe, but cannot change the orientation of the probe. A six degrees of freedom robot can simplify and fasten up the pose planning and measurement process real-offline. A developed LDA software plug-in for RobotStudio is used to real-offline plan and perform flow measurements in an open wind channel (flow rate = 300 m³/h) with an ellipsoid (diameter y=z = 140 mm and x = 40 mm). In 25 mm, 20 mm, 15 mm, 10 mm, 5 mm and 2 mm orthogonal distance of the ellipsoid surface the tangential velocity component is determined. Accuracy and repeatability of the robot-LDA system is found and compared to the traditional linear traversing system. Results show a positive velocity gradient towards the axis of the wind channel with a velocity range of 4.4 – 20.3 m/s. At 25 mm distance the tangential velocity is in the range of 4.4 – 12.5 m/s, at 20 mm 7.6 – 17.5 m/s, at 15 mm 12.1 – 19.7 m/s, at 10 mm 16.5 – 20.1 m/s, at 5 mm 19.0 – 20.2 m/s and at 2 mm 18.3 – 20.3 m/s. In positive y and negative z-direction, a reduction of the tangential velocity component is found in all measurements. At a distance of 25 mm, the measurement surface is slightly outside to the wind channel inlet (diameter = 80 mm). The closer the measurement surface is at the wind channel axis, the higher is the wind channel axial component. Robot-LDA average accuracy of 13 µm and repeatability of 10 µm is in the range of the linear traversing unit where the accuracy is 20 µm and the repeatability is 6 µm. The robot-LDA measurement system is an alternative to the existing linear-LDA with the advantages of real-offline robot path planning for arbitrary surfaces requiring six degrees of freedom probe placement.

Modeling and Experimental Study of the Effect of Pore Water Velocity on the Spectral Induced Polarization Signature in Porous Media

AbstractInduced polarization (IP) is increasingly applied for hydrological, environmental and agricultural purposes. Interpretation of IP data is based on understanding the relationship between the IP signature and the porous media property of interest. Mechanistic models on the IP phenomenon rely on the Poisson‐Nernst‐Plank equations, where diffusion and electromigration fluxes are the driving forces of charge transport and are directly related to IP. However, to our knowledge, the impact of advection flux on IP was not investigated experimentally and was not considered in any IP model. In this work, we measured the spectral IP (SIP) signature of porous media under varying flow conditions, in addition to developing and solving a model for SIP signature of porous media, which takes flow into consideration. The experimental and the model results demonstrate that as bulk velocity increases, polarization and relaxation time decrease. Using a numerical model, we established that fluid flow near the particle deforms the electrical double layer (EDL) structure, accounting for the observed reduction in polarization. We found a qualitative agreement between the model and the measurements. Still, the model overestimates the impact of flow rate on SIP signature, which we explain in terms of the flow boundary conditions. Overall, our results demonstrate the sensitivity of the SIP signature to fluid flow, highlighting the need to consider fluid velocity in the interpretation of the SIP signature of porous media, and opening an exciting new direction for noninvasive measurements of fluid flow at the EDL scale.

Kalman filtering approach to particle track filtering and track uncertainty quantification for 3D PTV measurement

Three-dimensional Particle Tracking Velocimetry (3D-PTV) is a non-invasive flow measurement technique that computes the velocity field by reconstructing 3D particle positions of individual tracer particles and by subsequently tracking those positions. The particle velocity measurement accuracy depends on the faithful reconstruction of 3D particle positions. The complex measurement chain in 3D-PTV involves several steps, from calibration to 3D position reconstruction and particle position tracking, each having its own source of error. Additionally, higher seeding density increases the uncertainty in particle reconstruction and tracking, which in turn, increases the noise in the estimated tracks. A noisy track decreases the measurement accuracy and amplifies any noise in the PTV-derived quantities of interest, which includes acceleration, pressure and vorticity. Thus, track filtering techniques are critical in a 3D-PTV measurement. Track fitting using polynomial functions, filtering methods adopted from signal processing and object tracking are among the well-established techniques used to achieve smooth position, velocity estimates from reconstructed particle trajectories. The Kalman filter is one such filtering technique that is widely used in various applications. The strength of the Kalman filter lies in its ability to perform noise reduction that is informed by existing physical models and the uncertainty estimates of recorded measurements. However, the measurement uncertainty input to the Kalman filter needs to be known at priori, which in many cases may not be available or could be difficult to estimate. In the literature on Kalman filters and their variants applied to 2D-PIV/PTV, the position uncertainty data fed to the filter is either user-defined or estimated based on global noise levels in the PTV measurements. But instantaneous position and velocity uncertainty quantification for individual particle positions/tracks has been challenging in the 3D PTV community. Recent work by Bhattacharya and Vlachos (2020) provides an estimate of the uncertainty in the reconstructed particle positions for a 3D PTV measurement. This position uncertainty estimate dynamically updates the filter gain for each track and enables the evaluation of the performance of the Kalman filter in 3D PTV track filtering.

Ultrasound PIV Uncertainty Quantification

Ultrasound Particle image velocimetry (UPIV) is a non-invasive flow measurement technique where acousticopaque flow tracers are injected into a working fluid and ensonified to create ultrasound images. These images are processed using PIV cross-correlation based algorithms to measure the velocity field (Kim et al., 2004). UPIV is useful for opaque flows, primarily where complex flows exist, accordingly, it is used in many industrial and clinical research applications such as studying intracardiac flow (Crase et al., 2007). Furthermore, the measurement provides suitable temporal and spatial resolutions for improved diagnostic metrics. Mentioned applications and the sensitive diagnostic industrial and clinical decisions made based on these measurements intensifies the importance of characterizing the UPIV measurement accuracy and associated uncertainty. However, quantifying UPIV measurement uncertainty is non-trivial due to the complexity of possible uncertainty sources, their combination, and propagation through the measurement chain.
 The formation of a particle image by ultrasound significantly differs from optical imaging, introducing unique aspects to image quality that must be considered. Particle images are formed across several ultrasound scan lines, yielding an elliptical particle image shape. Furthermore, the particle’s reflected pressure wave is converted to a digital signal that undergoes signal modulation, and this process forms a non-Gaussian point spread function (PSF) along the scan line direction. Additionally, clusters of tracers produce a single, bright image intensity and speckle image pattern. Compared to conventional PIV images, UPIV incurs significantly higher image noise due to lack of filtration for the ultrasound reflection of the non-tracer obstacles.

Estimating Central Pulse Pressure From Blood Flow by Identifying the Main Physical Determinants of Pulse Pressure Amplification.

Several studies suggest that central (aortic) blood pressure (cBP) is a better marker of cardiovascular disease risk than peripheral blood pressure (pBP). The morphology of the pBP wave, usually assessed non-invasively in the arm, differs significantly from the cBP wave, whose direct measurement is highly invasive. In particular, pulse pressure, PP (the amplitude of the pressure wave), increases from central to peripheral arteries, leading to the so-called pulse pressure amplification (ΔPP). The main purpose of this study was to develop a methodology for estimating central PP (cPP) from non-invasive measurements of aortic flow and peripheral PP. Our novel approach is based on a comprehensive understanding of the main cardiovascular properties that determine ΔPP along the aortic-brachial arterial path, namely brachial flow wave morphology in late systole, and vessel radius and distance along this arterial path. This understanding was achieved by using a blood flow model which allows for workable analytical solutions in the frequency domain that can be decoupled and simplified for each arterial segment. Results show the ability of our methodology to (i) capture changes in cPP and ΔPP produced by variations in cardiovascular properties and (ii) estimate cPP with mean differences smaller than 3.3 ± 2.8 mmHg on in silico data for different age groups (25–75 years old) and 5.1 ± 6.9 mmHg on in vivo data for normotensive and hypertensive subjects. Our approach could improve cardiovascular function assessment in clinical cohorts for which aortic flow wave data is available.

Volumetric particle tracking velocimetry (PTV) uncertainty quantification

We introduce the first comprehensive approach to determine the uncertainty in volumetric Particle Tracking Velocimetry (PTV) measurements. Volumetric PTV is a state-of-the-art non-invasive flow measurement technique, which measures the velocity field by recording successive snapshots of the tracer particle motion using a multi-camera set-up. The measurement chain involves reconstructing the three-dimensional particle positions by a triangulation process using the calibrated camera mapping functions. The non-linear combination of the elemental error sources during the iterative self-calibration correction and particle reconstruction steps increases the complexity of the task. Here, we first estimate the uncertainty in the particle image location, which we model as a combination of the particle position estimation uncertainty and the reprojection error uncertainty. The latter is obtained by a gaussian fit to the histogram of disparity estimates within a sub-volume. Next, we determine the uncertainty in the camera calibration coefficients. As a final step the previous two uncertainties are combined using an uncertainty propagation through the volumetric reconstruction process. The uncertainty in the velocity vector is directly obtained as a function of the reconstructed particle position uncertainty. The framework is tested with synthetic vortex ring images. The results show good agreement between the predicted and the expected RMS uncertainty values. The prediction is consistent for seeding densities tested in the range of 0.01 to 0.1 particles per pixel. Finally, the methodology is also successfully validated for an experimental test case of laminar pipe flow velocity profile measurement where the predicted uncertainty is within 17% of the RMS error value.

Personalized mathematical model of human arm arteries with inflow boundary condition

Mathematical modeling is a powerful tool to study the overall behavior of the cardiovascular system (CVS). To build a personalized or predictive mathematical model of the human CVS, it is essential to validate the model outputs with some empirical results (measurements). In this paper, we developed a 0D model of the human arm arteries for inflow boundary condition in complement with the data obtained from a healthy person. Further, the model outputs are validated/compared with the non-invasive flow measurements taken at ulnar artery using Doppler ultrasound (Toshiba’s Aplio$$^{\text {TM}}$$ 500). We observed a good agreement between model simulations and flow measurements taken at ulnar artery of a healthy person $$(r=0.8264)$$.

Analogue black hole spectroscopy; or, how to listen to dumb holes

Spectroscopy is a fundamental tool in science which consists in studying the response of a system as a function of frequency. Among its many applications in Physics, Biology, Chemistry and other fields, the possibility of identifying objects and structures through their emission spectra is remarkable and incredibly useful. In this paper we apply the spectroscopy idea to a numerically simulated hydrodynamical flow, with the goal of developing a new, non-invasive flow measurement technique. Our focus lies on an irrotational draining vortex, which can be seen, under specific conditions, as the analogue of a rotating black hole (historically named a dumb hole). This paper is a development of a recent experiment that suggests that irrotational vortices and rotating black holes share a common relaxation process, known as the ringdown phase. We apply techniques borrowed from black hole physics to identify vortex flows from their characteristic spectrum emitted during this ringdown phase. We believe that this technique is a new facet of the fluid-gravity analogy and constitutes a promising way to investigate experimentally vortex flows in fluids and superfluids alike.

Continuous thermodilution to assess absolute flow and microvascular resistance: validation in humans using [15O]H2O positron emission tomography.

Continuous thermodilution is a novel technique to quantify absolute coronary flow and microvascular resistance (MVR). Notably, intracoronary infusion of saline elicits maximal hyperaemia, obviating the need for adenosine. The primary aim of this study was to validate continuous thermodilution in humans by comparing invasive measurements to [15O]H2O positron emission tomography (PET). As a secondary goal, absolute flow and MVR were compared between invasive measurements obtained with and without adenosine. Twenty-five patients underwent coronary computed tomography angiography (CCTA), [15O]H2O PET, and invasive assessment. Absolute coronary flow and MVR were measured in the left anterior descending and left circumflex artery using a dedicated infusion catheter and a temperature/pressure sensor-tipped guidewire. Invasive measurements were performed with and without adenosine. In order to compare invasive flow measurements with PET perfusion, subtending myocardial mass of the investigated vessels was derived from CCTA using the Voronoi algorithm. Invasive and non-invasive measurements of adenosine-induced hyperaemic flow and MVR showed strong correlation (r = 0.91; P < 0.001 for flow and r = 0.85; P < 0.001 for MVR) and good agreement [intraclass correlation coefficient (ICC) = 0.90; P < 0.001 for flow and ICC = 0.79; P < 0.001 for MVR]. Absolute flow and MVR also correlated well between measurements with and without adenosine (r = 0.97; P < 0.001 for flow and r = 0.98; P < 0.001 for MVR) and showed good agreement (ICC = 0.96; P < 0.001 for flow and ICC = 0.98; P < 0.001 for MVR). Continuous thermodilution is an accurate method to measure absolute coronary flow and MVR, which is evidenced by strong agreement with [15O]H2O PET derived flow and resistance. Absolute flow and MVR correlate highly between invasive measurements obtained with and without adenosine, which confirms that intracoronary infusion of room temperature saline elicits steady-state maximal hyperaemia.

Estimating left ventricular elastance from noninvasive aortic flow and brachial pressure measurements

Background and AimLeft Ventricular (LV) End-systolic elastance (Ees) serves as a major determinant of cardiac systolic contractility. Traditional Methods: to evaluate the ventricular mechanics directly from measurements require intraventricular pressure and volume recordings during an acute preload alteration. The noninvasive derivation of Ees remains thus challenging. Here, we present a novel method to estimate the LV pressure-volume loop and ela-stance in humans from given aortic flow waveforms, LV ejection fraction and brachial pressure using a 1-D model of the cardiovascular system (1).MethodsInitially, the measured aortic flow waveform is used as the input to the default 1-D model and the output brachial systolic and diastolic pressure are compared with the ‘real’ values. Subsequently, arterial compliance and peripheral resistance are modified accordingly until peripheral pressure is accurately predicted. In a second step, this person-specific model is used to estimate the elastance parameters that produce the ‘real’ aortic flow waveform. Additional knowledge of the LV ejection fraction can allow us to derive the entire pressure-volume loop, including end-diastolic elastance. The method was tested on a database of 50 different in silico hemodynamic cases generated after varying cardiac and arterial model parameters.ResultsImplementation of the method yielded good agreement (r = 0.99) and accuracy (n-RMSE = 4%) between ‘real’ and estimated values of Ees (Figure 1). Furthermore, a sensitivity analysis revealed that errors due to poor arterial adjustment and measurements are small (≤8% for Ees).ConclusionThe insilico validation of the proposed method gave promising results and we are currently planning its testing against in vivo data.Figure 1A. Scatter plot of the method-derived Ees (Ees(est)) vs the 'real' values (Ees(real)) for the 50 simulated cases. B. the respective Bland-Altman plot

Non-invasive estimation of domestic hot water usage with temperature and vibration sensors

Electric water heaters are responsible for a large portion of electricity consumption and water usage in the domestic sector. Smart water heaters alleviate the strain on the electricity supply grid and reduce water consumption through behavioural change, but the installation of in-line flow meters is inconvenient and expensive. A non-invasive water flow meter is proposed as an alternative. Non-invasive flow measurement is more common for high flow rates in the industrial sector than for domestic applications. Various non-invasive water measurement methods are investigated in the context of domestic hot water, and a combination of thermal- and vibration-sensing is proposed. The proposed solution uses inexpensive, easily installable, non-invasive sensors and a novel algorithm to provide the same flow measurement accuracy as existing in-line meters. The algorithm detects the beginning and end of water consumption events with an accuracy of 95.6%. Quantitative flow rate estimation was possible for flow rates greater than 5 L min−1 with an accuracy of 89%, while volumetric usage estimation had an accuracy of more than 93%. The algorithm limitations were applied to field data, revealing that water consumption could be detected with an error of less than 12% within the limitations of the proposed algorithm. The paper presents a successful proof of concept for a non-invasive alternative to domestic hot water flow rate measurement.

On the influence of gas–liquid two-phase flow on Lorentz force velocimetry

Lorentz force velocimetry (LFV) is a contactless and non-invasive flow measurement technique for electrical conducting fluids. When the fluid passes a magnetic field, a Lorentz force will be generated. The force is proportional to the electrical conductivity, to the velocity of the fluid, and to the square of the magnetic flux density. It is possible to apply this technique over a range of conductivities varying from high values as for liquid metals to lower values as typically for electrolytes (e.g. salt water). The main challenge is measuring the force, which is six orders of magnitude smaller for electrolytes in comparison to liquid metals. It was shown that LFV is insensitive to the shape of different velocity profiles and especially to strongly asymmetric profiles as long as the volume flow is constant. However, the influence of a second gaseous phase on the force signal remains an open question. This is of fundamental interest especially for chemical and food processing industries. To examine this influence in detail, electrolyte flow with different volume flow fractions was investigated for the current study. By using frits, it is possible to generate a homogeneous distribution of the second (gaseous) phase in the electrolyte. Small electrolyte velocities or flow rates (v < 1.5 m s−1, = 225 l min−1) enable the formation of slug flow and stratified wavy flow, and higher velocities (v > 1.8 m s−1, = 270 l min−1) the formation of bubbly two-phase flow in the used test section. By this means, it is possible to study a wide range of flow patterns in a horizontal duct. The experimental results are in good agreement with the analytical estimations for fully dispersed flow. However, for a nonhomogeneous distribution of air the force signal deviates much stronger. An explanation can be the superposition of Lorentz forces and magnetic forces, caused by differences of the magnetic susceptibilities of water and air, which will be discussed in greater detail.

First-trimester prediction of preterm labor using disintegrin and metalloprotease 12, uterine artery Doppler, and maternal characteristics

Background A disintegrin and metalloprotease 12 (ADAM12) is a complex placenta-derived protein with proteolytic activity that may serve as a marker of placental dysfunction for the identification of preterm birth. This has benefits of an early intervention and increased antenatal surveillance. In addition, uterine artery Doppler studies are noninvasive measurements of placental vascular flow with increased resistance reflecting impaired trophoblastic invasion. Objectives The objective of this study is to estimate the efficiency of first-trimester maternal serum ADAM12, uterine artery Doppler, and maternal characteristics in the early prediction of preterm birth. Patients and methods This cohort study was conducted on 90 pregnant women in their first trimesters with selection criteria. All patients were investigated for ADAM12 protein level, uterine artery Doppler, and maternal characteristics. They were followed up for screening of preterm birth (before 37 weeks). Results Uterine artery Doppler pulsatility index and maternal serum ADAM12 level either individually or combined are not statistically significant predictors of spontaneous preterm birth. The combination of maternal ADAM12 and BMI with cutoff levels of 162 pg/ml and 32 kg/m2, respectively, was a highly significant effective predictor of preterm birth. Advancing maternal age, increasing parity, increased BMI, and hypertension were maternal characteristics that correlated significantly with preterm birth. Conclusion Uterine artery Doppler pulsatility index and maternal serum ADAM12 level are not statistically significant predictors of spontaneous preterm birth, but ‘combined’ ADAM12 and BMI can do it significantly.

Hydrodynamics Study in a fluidized bed reactor Using Gamma Ray Computed Tomography (CT) Technique

The main goal of this study is to further understand the fluidized bed hydrodynamics by determining the effects of superficial gas velocity and bed density on the hydrodynamics of gas-solid fluidized beds by using a 14-cm-diameter Plexiglas cylindrical fluidized bed column, air at room temperature was used as the fluidizing gas, two different Geld art type-B particles were used glass bead and copper particle with material densities of 2500, 5300 Kg/m3 respectively with the same size particle 210 µm. Three different superficial gas velocities were investigated Ug=25, 30, and 35 cm/s. Non-invasive flow measurement technique gamma ray computed tomography (CT) has been used to measure the time-averaged cross-sectional and radial profiles of solids and gas holdups distribution. The results show that local time-averaged gas holdup is a function of bed material density, that as material density decreases, gas holdup increases, gas holdup increases with increasing superficial gas velocities