Accurate Velocity Measurements Research Articles

Improving the precision of solids velocity measurement in gas-solid fluidized beds with a hybrid machine learning model

Accurate measurement of solids velocity is crucial for fluidized bed reactor design, optimization, and scaling in chemical engineering. However, the cross-correlation calculation of dual-channel particle concentration signals has limitations in dense-phase fluidized systems. To address this, we propose a data-driven approach using machine learning algorithms in numerical simulations. Four popular algorithms (linear regression, random forest, support vector machine, and neural network) are evaluated. Individual models show poor generalization with a determinable coefficient below 0.2. To overcome this, a hybrid model combining a neural network and random forest is developed, improving data prediction and generalization. The determinable coefficient increases to 0.90, and the relative error in average particle velocity prediction reduces to less than 0.01. This hybrid machine learning model has the potential to overcome limitations of cross-correlation-based particle velocimetry, expanding its applications in multiphase flow systems. It offers a promising solution for accurate solids velocity measurement in fluidized beds.

Enhanced Measurement of Vortex Beam Rotation Using Polarization-Assisted Particle Swarm Optimization for Phase Retrieval

In detecting the rotation velocity of an object employing the rotational Doppler effect of vortex beams, atmospheric turbulence can easily cause phase distortion and spiral spectrum dispersion, consequently reducing velocity measurement accuracy. This study combines adaptive optical intelligence algorithms with polarization compensation information to propose a novel approach, the Stokes–Particle swarm optimization Gerchberg–Saxton (Stokes-PSO GS) algorithm, which integrates Stokes polarization information assistance and PSO for GS phase retrieval. The algorithm adjusts the phase and amplitude of the pre-compensated phase screen of the GS algorithm utilizing Stokes information of polarized vortex beam (with lL = 5 and lR = −5) before and after distortion. The PSO is then employed to optimize the pre-compensated phase screen and perform compensations. Simulation results at zS-T = 200 m and Cn2 = 1 × 10−14 m−2/3, demonstrate that the Stokes-PSO GS algorithm exhibits strong stability (small angular spectrum purity deviation, σp, Stokes-PSO GS = 0.005675% < σp, GS = 11.62%), superior optical field recovery (well-recovered Stokes optical field, up to 33.76% improvement in angular spectrum purity), and high-velocity measurement accuracy (25.93% improvement) compared to the GS algorithm. This approach enables precise measurement of the rotation velocity of the vortex beam, demonstrating its potential in practical applications.

Comparative Assessment of Different Image Velocimetry Techniques for Measuring River Velocities Using Unmanned Aerial Vehicle Imagery

The accurate measurement of river velocity is essential due to its multifaceted significance. In response to this demand, remote measurement techniques have emerged, including large-scale particle image velocimetry (LSPIV), which can be implemented through cameras or unmanned aerial vehicles (UAVs). This study conducted water surface velocity measurements in the Xihu River, situated in Miaoli County, Taiwan. These measurements were subjected to analysis using five distinct algorithms (PIVlab, Fudaa-LSPIV, OpenPIV, KLT-IV, and STIV) and were compared with surface velocity radar (SVR) results. In the quest for identifying the optimal parameter configuration, it was found that an IA size of 32 pixels × 32 pixels, an image acquisition frequency of 12 frames per second (fps), and a pixel size of 20.5 mm/pixel consistently yielded the lowest values for mean error (ME) and root mean squared error (RMSE) in the performance of Fudaa-LSPIV. Among these algorithms, Fudaa-LSPIV consistently demonstrated the lowest mean error (ME) and root mean squared error (RMSE) values. Additionally, it exhibited the highest coefficient of determination (R2 = 0.8053). Subsequent investigations employing Fudaa-LSPIV delved into the impact of various water surface velocity calculation parameters. These experiments revealed that alterations in the size of the interrogation area (IA), image acquisition frequency, and pixel size significantly influenced water surface velocity. This parameter set was subsequently employed in an experiment exploring the incorporation of artificial particles in image velocimetry analysis. The results indicated that the introduction of artificial particles had a discernible impact on the calculation of surface water velocity. Inclusion of these artificial particles enhanced the capability of Fudaa-LSPIV to detect patterns on the water surface.

Research on applying scan matching algorithm to improve velocity measurement accuracy of single-line ranging lidar

There has been increased demand for safe and efficient speed measurement of moving objects in engineering and practical applications, particularly in scenarios involving the measurement of the movement speed of objects at close range. Thus, this study designed a single-line scanning pulsed lidar system. In addition, algorithms were employed to enhance the measurement accuracy of the single-line ranging pulsed lidar. In this study, the lidar was installed on a guideway-integrated testing platform to obtain point cloud data, which was processed using the normal distributions transform (NDT), iterative closest points (ICP), and correlative scan matching (CSM) algorithms. The experimental results demonstrated that the NDT algorithm outperformed the ICP and CSM algorithms in terms of velocity measurement accuracy and algorithm robustness. Moreover, the velocity measurement results obtained from the NDT algorithm were closer to the actual track speed, with a maximum velocity deviation of lesser than 0.021 m/s compared to the actual guideway speed.

Real Time Measurement of Multiphase Flow Velocity using Electrical Capacitance Tomography

Accurate and real-time measurement of fluid flow velocity is crucial in various industrial processes, especially when dealing with multiple phase fluids. Traditional flow measurement methods often struggle to accurately quantify the velocity of complex multiphase flows within pipes. This challenge necessitates the exploration of innovative techniques capable of providing reliable measurements. This paper proposes the utilization of Electrical Capacitance Tomography (ECT) as a promising approach for measuring the velocity of multiple phase fluids in pipes. The ECT technique involves the non-intrusive imaging of the electrical capacitance distribution within the pipe. By utilizing an array of electrodes placed around the pipe circumference, the capacitance distribution can be reconstructed, offering insight into the fluid flow patterns. By analyzing the temporal changes in the capacitance distribution, the velocity of different phases within the pipe can be estimated. To achieve accurate velocity measurements, an ECT system needs to account for the complexities introduced by multiphase flows. Various image reconstruction algorithms, such as linear back-projection and iterative algorithms like Gauss-Newton and Levenberg-Marquardt, are employed to reconstruct the capacitance distribution. Additionally, advanced signal processing techniques, such as cross-correlation analysis and time-difference methods, are used to extract velocity information from the reconstructed images. This paper presents an experimental investigation of measuring the velocity of multiple-phase fluids in pipes using the ECT technique. The study aims to address the challenges associated with different flow regimes, fluid properties, and pipe geometries by exploring advancements in electrode design, system calibration, and data processing techniques to enhance the accuracy and robustness of ECT-based velocity measurements.

Pulsed wave Doppler ultrasound: Accuracy, variability, and impact of acquisition parameters on flow measurements.

Pulsed wave Doppler ultrasound is a useful modality for assessing vascular health as it quantifies blood flow characteristics. To facilitate accurate diagnosis, accuracy and consistency of this modality should be assessed through Doppler quality assurance (QA). The purpose of this study was to characterize the accuracy, reproducibility, and inter-scanner variability of ultrasound flow velocity measurements via a flow phantom, with a focus on the effect of systematic acquisition parameters on measured flow velocity accuracy. Using a manufacturer-calibrated flow phantom, pulsed wave measurements were acquired on five clinical systems (iU22, Philips) with three models of transducers, including both linear and curvilinear models. The peak and mean flow velocities were estimated by vendor-supplied spectral analysis tools. To investigate intra- and inter-scanner variability, measurements were repeated using each scanner-transducer pair under a standardized set of conditions. Inter-scanner variability was assessed using ANOVA. Flow velocity accuracy was investigated by mean absolute percentage error. The impacts of receive gain, measurement depth, and beam steering on measured flow velocity accuracy were examined by varying each parameter over its available range and comparing to the ground truth flow velocity. Inter-scanner variability was statistically significant for peak flow measurements made using both linear and curvilinear transducers, though absolute differences in measured velocity were small. Inter-scanner variability was not statistically significant for mean flow velocity. Receive gain, measurement depth, and beam steering were all found to impact the accuracy of measured flow characteristics for linear transducers. Accuracy of the flow measurements made with the curvilinear transducer demonstrated high consistency to changes in receive gain at a constant depth, though were impacted by increasing the measurement depth. Carefully and consistently selected acquisition and set-up parameters are essential in order to establish a reliable and meaningful QA program.

Robust measurement of angular velocity based on rotational Doppler effect in misaligned illumination

The discovery of the rotational Doppler effect (RDE) provides opportunities to achieve fast, accurate, and contactless measurement of angular velocity. As the RDE signal is sensitive to the illumination light and its scattering characteristic, how to effectively extract the angular velocity of a detected target becomes one of the key difficulties in this measurement technique. Here, we put forward a scattering modified RDE model to fully understand the frequency-domain behavior of an RDE signal in misaligned illumination. A robust strategy based on the peak-to-peak intervals in a wideband spectrum of the RDE signal is demonstrated to acquire accurate angular velocity, without strict requirement to the alignment of illumination and roughness of the detected surface. Furthermore, the feasibility to extract the spin angular velocity from a composite motion of spin and translation is discussed.

Ultra-fast ultrasound blood flow velocimetry for carotid artery with deep learning

Accurate measurement of blood flow velocity is important for the prevention and early diagnosis of atherosclerosis. However, due to the uncertainty of parameter settings, the autocorrelation velocimetry methods based on clutter filtering are prone to incorrectly filter out the near-wall blood flow signal, resulting in poor velocimetric accuracy. In addition, the Doppler coherent compounding acts as a low-pass filter, which also leads to low values of blood flow velocity estimated by the above methods. Motivated by this status quo, here we propose a deep learning estimator that combines clutter filtering and blood flow velocimetry based on the adaptive property of one-dimensional convolutional neural network (1DCNN). The estimator is operated by first extracting the blood flow signal from the original Doppler echo signal through an affine transformation of the 1D convolution, and then converting the extracted signal into the desired blood flow velocity using a linear transformation function. The effectiveness of the proposed method is verified by simulation as well as in vivo carotid artery data. Compared with typical velocimetry methods such as high-pass filtering (HPF) and singular value decomposition (SVD), the results show that the normalized root means square error (NRMSE) obtained by 1DCNN is reduced by 54.99 % and 53.50 % for forward blood flow velocimetry, and 70.99 % and 69.50 % for reverse blood flow velocimetry, respectively. Consistently, the in vivo measurements demonstrate that the goodness-of-fit of the proposed estimator is improved by 8.72 % and 4.74 % for five subjects. Moreover, the estimation time consumed by 1DCNN is greatly reduced, which costs only 2.91 % of the time of HPF and 12.83 % of the time of SVD. In conclusion, the proposed estimator is a better alternative to the current blood flow velocimetry, and is capable of providing more accurate diagnosis information for vascular diseases in clinical applications.

Low-cost field particle image velocimetry for quantifying environmental turbulence

To date, there is a lack of in-situ turbulence data in natural aquatic systems. Adapting particle image velocimetry (PIV) to field use is of interest to provide such data. Past field PIV systems have been successful, but suffer from high costs, lack of portability, and flow disruption. We present a new field PIV system designed to address these limitations and capture turbulence metrics significant to aquatic biota: velocity, turbulent kinetic energy (TKE), vorticity, eddy size, and eddy circulation. Velocity measurements by the novel PIV system were compared to measurements by an acoustic doppler velocimeter (ADV) and an electromagnetic flowmeter for validation, and its results agreed with these other two methods. The PIV’s TKE results were compared to those of an ADV, and they agreed as well. Vorticity, eddy size, and eddy circulation were validated in the field through repeating past measurements. Each of these data sets generated by the novel PIV fell within the range of the validation measurements. The novel system thus provides accurate turbulence and velocity measurements; further, its design is low-cost, uses widely available materials, is minimally disruptive to the flow, and is highly portable and adjustable, offering an accessible method for acquiring meaningful field turbulence data.

Experimental investigation of the variation of the local gas velocities in a cold flow pebble bed reactor (PBR) using a hot wire anemometry technique

Obtaining accurate results and new benchmark data for the local velocity of the gas flowing within the pebble bed reactor (PBR) is a key step for understanding and benchmarking simulations of the thermal hydraulics in the PBR core as it significantly affects the design and safety operation of these reactors. Therefore, this work focused on studying the local gas velocities inside a pebble bed using a sophisticated hot wire anemometry (HWA) technique, which was supported with a novel probe-protector case that protected the probe, allowing the measurements to be conducted at various locations in a pebble bed with pebble diameter of 5cm and an aspect ratio of 6. The measurements were conducted at various superficial inlet gas velocities (0.3≤Ug≤2.4m/s), covering both transitional and turbulent flow conditions (993.78≤Re≤7950.24) at three axial levels and four radial locations (r/R=0, 0.33, 0.67, 0.9). The results highlighted the effect of the wall on the variation of the local gas velocities inside the pebble bed, as higher gas velocities were recorded at the near-wall region, where the void fractions are higher, providing a path of the least resistance to the flow of the gas, compared to the center of the bed. Furthermore, a second order polynomial correlation with an R2=93.96% and an AARE equal to 1.47% was developed for the prediction of the local gas velocity inside the bed, within the experimental range of the study. The accurate gas velocity measurements obtained in this study can serve as benchmark data for the validation of CFD simulations coupled with heat transfer calculations.

Non-nulling protocols for fast, accurate, 3-D velocity measurements in stacks

ABSTRACT The authors present protocols for making fast, accurate, 3D velocity measurements in the stacks of coal-fired power plants. The measurements are traceable to internationally-recognized standards; therefore, they provide a rigorous basis for measuring and/or regulating the emissions from stacks. The authors used novel, five-hole, hemispherical, differential-pressure probes optimized for non-nulling (no-probe rotation) measurements. The probes resist plugging from ash and water droplets. Integrating the differential pressures for only 5 seconds determined the axial velocity V a with an expanded relative uncertainty U r(V a) ≤ 2% of the axial velocity at the probe’s location, the flow’s pitch (α) and yaw (β) angles with expanded uncertainties U(α) = U(β) = 1 °, and the static pressure p s with U r(p s) = 0.1% of the static pressure. This accuracy was achieved 1) by calibrating each probe in a wind tunnel at 130, strategically-chosen values of (V a, α, β) spanning the conditions found in the majority of stacks (|α| ≤ 20 °; |β| ≤ 40 °; 4.5 m/s ≤ V a ≤27 m/s), and 2) by using a long-forgotten definition of the pseudo-dynamic pressure that scales with the dynamic pressure. The resulting calibration functions span the probe-diameter Reynolds number range from 7,600 to 45,000. Implications: The continuous emissions monitoring systems (CEMS) that measure the flue gas flow rate in coal-fired power plant smokestacks are calibrated (at least) annually by a velocity profiling method. The stack axial velocity profile is measured by traversing S-type pitot probes (or one of the other EPA-sanctioned pitot probes) across two orthogonal, diametric chords in the stack cross-section. The average area-weighted axial velocity calculated from the pitot traverse quantifies the accuracy of the CEMS flow monitor. Therefore, the flow measurement accuracy of coal-fired power plants greenhouse gas (GHG) emissions depends on the accuracy of pitot probe velocity measurements. Coal-fired power plants overwhelmingly calibrate CEMS flow monitors using S-type pitot probes. Almost always, stack testers measure the velocity without rotating or nulling the probe (i.e., the non-nulling method). These 1D non-nulling velocity measurements take significantly less time than the corresponding 2D nulling measurements (or 3D nulling measurements for other probe types). However, the accuracy of the 1D non-nulling velocity measurements made using S-type probes depends on the pitch and yaw angles of the flow. Measured axial velocities are accurate at pitch and yaw angles near zero, but the accuracy degrades at larger pitch and yaw angles. The authors developed a 5-hole hemispherical pitot probe that accurately measures the velocity vector in coal-fired smokestacks without needing to rotate or null the probe. This non-nulling, 3D probe is designed with large diameter pressure ports to prevent water droplets (or particulates) from obstructing its pressure ports when applied in stack flow measurement applications. This manuscript presents a wind tunnel calibration procedure to determine the non-nulling calibration curves for 1) dynamic pressure; 2) pitch angle; 3) yaw angle; and 4) static pressure. These calibration curves are used to determine axial velocities from 6 m/s to 27 m/s, yaw angles between ±40°, and pitch angles between ±20°. The uncertainties at the 95% confidence limit for axial velocity, yaw angle, and pitch angle are 2% (or less), 1°, and 1°, respectively. Therefore, in contrast to existing EPA-sanctioned probes, the non-nulling hemispherical probe provides fast, low uncertainty velocity measurements independent of the pitch and yaw angles of the stack flow.

Research on weighted carrier phase three difference precision velocity measurement method based on BDS / GPS integrated system

Aiming at the problem of velocity measurement accuracy in single system carrier phase differential velocity measurement and the possible cycle slip in the process, a carrier phase three difference method is proposed to further improve the velocity measurement accuracy. The use of carrier phase triple difference positioning has been very mature, but there is not much research in velocity measurement. Therefore, this paper uses carrier phase triple difference for velocity measurement. At the same time, in order to improve the cycle slip problem in the process, an integral Doppler double difference combined with carrier phase triple difference is proposed, and then a method to improve the cycle slip based on weight is obtained. Both static and dynamic tests show that the carrier phase triple difference method can improve the speed measurement accuracy, and the weighted carrier phase triple difference method can improve the cycle slip.

Ultrafast Ultrasound Vector Doppler for Small Vasculature Imaging.

Ultrafast Doppler has been accepted as a novel modality for small vasculature imaging with high sensitivity, high spatiotemporal resolution and high penetration. However, conventional Doppler estimator adopted in studies of ultrafast ultrasound imaging is only sensitive to the velocity component along the beam direction and has angle-dependent limitation. Vector Doppler has been developed with the goal of angle-independent velocity estimation, but is typically employed for relatively large vessels. In this study, combining multiangle vector Doppler strategy and ultrafast sequencing, ultrafast ultrasound vector Doppler (ultrafast UVD) is developed for small vasculature hemodynamic imaging. The validity of the technique is demonstrated through experiments on a rotational phantom, rat brain, human brain and human spinal cord. A rat brain experiments show that compared with the ultrasound localization microscopy (ULM) velocimetry, which is widely accepted as an accurate flow velocimetry technique, the average relative error (ARE) of the velocity magnitude estimated by ultrafast UVD is approximately 16.2%, with an RMSE of the velocity direction of 26.7°. It is demonstrated that ultrafast UVD is a promising tool for accurate blood flow velocity measurement, especially for the organs including brain and spinal cord with vasculature typically exhibiting tendential alignment of vascular trees.

Autocorrelation analysis-based OCT velocimetry for axial blood flow velocity imaging of the cerebral capillary network.

The accurate measurement of blood flow velocity in the capillary network is challenging due to the small size of the vessels and the slow flow of red blood cells (RBCs) within the vessel. Here, we introduce an autocorrelation analysis-based optical coherence tomography (OCT) method that takes less acquisition time to measure the axial blood flow velocity in the capillary network. The axial blood flow velocity was obtained from the phase change in the decorrelation period of the first-order field autocorrelation function (g1) of the OCT field data, which was acquired with M-mode acquisition (repeated A-scans). The rotation center of g1 in the complex plane was first re-centralized to the origin, then the phase change due to the movement of RBCs was extracted in the g1 decorrelation period which is usually 0.2-0.5 ms. In phantom experiments, the results suggest that the proposed method could accurately measure the axial speed with a wide range of 0.5-15 mm/s. We further tested the method on living animals. Compared with the phase-resolved Doppler optical coherence tomography (pr-DOCT), the proposed method can obtain robust axial velocity measurements with more than five times shorter acquisition time.

Determination of the Gurney velocity for the unconventional aluminum‐water explosive**

AbstractThe present work is dedicated to the determination of the Gurney velocity of the unconventional aluminum‐water explosive. The paper presents the experimental arrangement, the diagnostic equipment used, and the main results that have been obtained. The experiments report the accurate velocity measurement of projectiles accelerated by aluminum and copper underwater exploding wires. Based on the results obtained, a Gurney velocity of 1.88 km/s has been obtained for the unconventional aluminum‐water explosive. The result is discussed and compared with typical values for standard explosives.

A variational mode decomposition projectile signal processing algorithm of infrared sky screen velocity measurement system and detection mathematical model of detection screen

To improve the measurement accuracy of projectile initial velocity, this paper proposes a variational mode decomposition (VMD) projectile signal processing algorithm, and researches the detection mathematical model of detection screen. We establish the detection mathematical model of detection screen by combining projectile’s radiation, projectile’s size and the signal characteristics of projectile; analyze the contribution of projectile radiance to infrared photoelectric detector. Based on the time and space relationship that the projectile passing through the detection screen, we use the noise equivalent flux density to characterize the detection sensitivity of infrared sky screen to evaluate its detection performance and set up its time-space domain detection models; utilize the characteristic of the output signal of infrared sky screen to form a variational mode decomposition projectile signal processing algorithm, and use the cross correlation function to extract the time. By comparing the infrared sky screen and the traditional sky screen velocity measurement system with the standard net target velocity measurement system, it is evident that due to external interference, some velocity error values obtained by the traditional sky screen velocity measurement system are significantly different from those obtained by the infrared sky screen velocity measurement system. Without external interference, the average velocity error between the traditional sky screen velocity measurement system and the net target velocity measurement system is 0.47 m/s. Compared with traditional sky screen velocity measurement systems, the average velocity error of the infrared sky screen velocity measurement system and the net target velocity measurement system is slightly smaller, which is 0.32 m/s.

Four-Dimensional (4D) Millimeter Wave-Based Sensing and Its Potential Applications in Digital Construction: A Review

Digital construction relies on effective sensing to enhance the safety, productivity, and quality of its activities. However, current sensing devices (e.g., camera, LiDAR, infrared sensors) have significant limitations in different aspects. In light of the substantial advantages offered by emerging 4D mmw technology, it is believed that this technology can overcome these limitations and serve as an excellent complement to current construction sensing methods due to its robust imaging capabilities, spatial sensing abilities, velocity measurement accuracy, penetrability features, and weather resistance properties. To support this argument, a scientometric review of 4D mmw-based sensing is conducted in this study. A total of 213 articles published after the initial invention of 4D mmw technology in 2019 were retrieved from the Scopus database, and six kinds of metadata were extracted from them, including the title, abstract, keywords, author(s), publisher, and year. Since some papers lack keywords, the GPT-4 model was used to extract them from the titles and abstracts of these publications. The preprocessed metadata were then integrated using Python and fed into the Citespace 6.2.R3 for further statistical, clustering, and co-occurrence analyses. The result revealed that the primary applications of 4D mmw are autonomous driving, human activity recognition, and robotics. Subsequently, the potential applications of this technology in the construction industry are explored, including construction site monitoring, environment understanding, and worker health monitoring. Finally, the challenges of adopting this emerging technology in the construction industry are also discussed.

Research on Timing Compensation of Shadow Photograph System with Three-sequence Laser Sparking

The accuracy of the laser sparking time interval measurement plays a key role in the measurement results when the projectile velocity is measured by shadow photograph system with three-sequence laser sparking. By comparing with the original single-chip measurement results, it was found that there was a certain deviation in the measurement results of the laser sparking time. Then the principle of the laser sparking time measurement circuit is introduced in detail, and the reasons for this error phenomenon are summarized. And analyze the original laser sparking time measurement data column and the data column accurately measured with an oscilloscope, use the Pearson correlation coefficient to describe the correlation between the two data columns, and obtain the sample correlation coefficient, the first laser sparking interval coefficient r1=0.50, the second laser sparking interval coefficient r2=0.52. According to the three-level division rule, when 0.4≤|r|<0.7, the two variables are significantly correlated. In order to compensate the original data, it is necessary to find a suitable Δ value to minimize the residual sum of squares of the measured column data after compensation. The mathematical model is developed and solved for the nonlinear system of equations, and the optimal value of Δ for the original data is obtained using a global search for the optimal value: Δ1= 0.238μs, Δ2= 0.201μs. Comparing the compensated data with the original data, The standard deviation of the measured columns of the first flash interval decreased from 0.117μs to 0.022μs after compensation, and the second decreased from 0.102μs to 0.014μs. This shows that the two columns error of compensated data are significantly reduced, which is important for the improvement of the velocity measurement accuracy of the target channel.

Fast and accurate flow measurement through dual-camera light field particle image velocimetry and ordered-subset algorithm

Light field particle image velocimetry (LF-PIV) can measure the three-dimensional (3D) flow field via a single perspective and hence is very attractive for applications with limited optical access. However, the flow velocity measurement via single-camera LF-PIV shows poor accuracy in the depth direction due to the particle reconstruction elongation effect. This study proposes a solution based on a dual-camera LF-PIV system along with an ordered-subset simultaneous algebraic reconstruction technique (OS-SART). The proposed system improves the spatial resolution in the depth direction and reduces the reconstruction elongation. The OS-SART also reduces the computational time brought by the dual-camera LF-PIV. Numerical reconstructions of the particle fields and Gaussian ring vortex field are first performed to evaluate the reconstruction accuracy and efficiency of the proposed system. Experiments on a circular jet flow are conducted to further validate the velocity measurement accuracy. Results indicate that the particle reconstruction elongation is reduced more than 10 times compared to the single-camera LF-PIV and the reconstruction efficiency is improved at least twice compared to the conventional SART. The accuracy is improved significantly for the ring vortex and 3D jet flow fields compared to the single-camera system. It is therefore demonstrated that the proposed system is capable of measuring the 3D flow field fast and accurately.

Enhanced Readout from Spatial Interference Fringes in a Point-Source Cold Atom Inertial Sensor.

When the initial size of an atom cloud in a cold atom interferometer is negligible compared to its size after free expansion, the interferometer is approximated to a point-source interferometer and is sensitive to rotational movements by introducing an additional phase shear in the interference sequence. This sensitivity on rotation enables a vertical atom-fountain interferometer to measure angular velocity in addition to gravitational acceleration, which it is conventionally used to measure. The accuracy and precision of the angular velocity measurement depends on proper extraction of frequency and phase from spatial interference patterns detected via the imaging of the atom cloud, which is usually affected by various systematic biases and noise. To improve the measurement, a pre-fitting process based on principal component analysis is applied to the recorded raw images. The contrast of interference patterns are enhanced by 7-12 dB when the processing is present, which leads to an enhancement in the precision of angular velocity measurements from 6.3 μrad/s to 3.3 μrad/s. This technique is applicable in various instruments that involve precise extraction of frequency and phase from a spatial interference pattern.