Velocity Measurements Research Articles

Geomorphological controls on estuary hydrodynamics with implications for diatom blooms in deglaciated coastal areas

Understanding local hydraulic conditions is imperative to coastal harmful algal bloom (HAB) monitoring. The research summarized herein describes how the locations and tidal phases selected for coastal hazard sampling can influence measurement results used to guide management decisions for HABs. Our study was conducted in Frenchman Bay, Maine, known for its complex deglaciated coastline, strong tidal influence, and shellfishing activities that are susceptible to problematic HABs such as those produced by some species (spp.) of the diatom genus Pseudo-nitzschia. In-situ measurements of current velocity, density, and turbulence collected over a semidiurnal tidal cycle and a companion numerical model simulation of the study area provide concurrent evidence of two adjacent counter-rotating sub-mesoscale eddies (2–4 km diameter) that persist in the depth-averaged residual circulation. The eddies are generated in the wake of several islands in an area with abrupt bathymetric gradients, both legacy conditions partly derived from deglaciation ~15 kya. Increased concentrations of Pseudo-nitzschia spp. measured during the semidiurnal survey follow a trend of elevated turbulent dissipation rates near the water surface, indicating that surface sampling alone might not adequately indicate species abundance. Additional measurements of Pseudo-nitzschia spp. from two years of weekly sampling in the region show that algal cell abundance is highest where residual eddies form. These findings provide incentive to examine current practices of HAB monitoring and management by linking coastal geomorphology to hydraulic conditions influencing HAB sampling outcomes, coastal morphometric features to material accumulation hotspots, and millennial time scales to modern hydraulic conditions.

Hemodynamic Effects of Intermittent Pneumatic Compression on Athletes: A Double-Blinded Randomized Crossover Study.

There are multiple postexercise recovery technologies available in the market based on the assumption of blood-flow enhancement. Lower-limb intermittent pneumatic compression (IPC) has been widely used, but the available scientific evidence supporting its effectiveness remains scarce, requiring a deeper investigation into its underlying mechanisms. The aim of this study was to assess the hemodynamic effects caused by the use of IPC at rest. Twenty-two soccer and track and field athletes underwent two 15-minute IPC protocols (moderate- [80mm Hg] and high-pressure [200mm Hg]) in a randomized order. Systolic peak velocity, end-diastolic peak velocity, arterial diameter, and heart rate were measured before, during (at the eighth minute), and 2minutes after each IPC protocol. Significant effects were observed between before and during (eighth minute) the IPC protocol for measures of systolic (P < .001) and end-diastolic peak velocities (P < .001), with the greater effects observed during the high-pressure protocol. Moreover, 2minutes after each IPC protocol, hemodynamic variables returned to values close to baseline. Arterial diameter presented significant differences between pressures during the IPC protocols (P < .05), while heart rate remained unaltered. IPC effectively enhances transitory blood flow of athletes, particularly when applying high-pressure protocols.

Inspection of Numerical and Fractional CMC and Water-based Hybrid Nanofluid with Power Law and Non-Singular Kernel: A Fractal Approach

A number of thermal devices might benefit from the usage of nanofluids in solar power. In this research, the idea of MHD mixed convective hybrid nanofluids including grapheme oxide (GO) and molybdenum disulfide (MoS2) nanoparticles with water (H2O) and Carboxymethyl Cellulose (CMC) as base fluids in a perpendicular channel to analyze the heat transfer of the flowing fluid with the help of recent definition of fractional derivatives namely Fractal fractional derivative. The problem is expressed as PDEs with beginning and boundary conditions, and it is analyzed analytically using the Laplace transform method. The velocity, temperature, and concentration measurements are also shown in series for their appropriate Laplace inverse. Delays in parameters like nanoparticle volume sharing rate have a significant influence on these techniques. Skin friction, Nusselt, and Sherwood numbers are computed for the longitudinal channel's left and right walls, and the necessary numerical results are presented in tabular format. As a result, it is discovered that the rate of heat transfer reduces as the volume percentage of nanoparticles and the Fractal time fractional value rise. Furthermore, when comparing nanofluids, water-based (H2O + GO + MoS2) hybrid nanofluids have a greater influence on governed model than (CMC + GO + MoS2)-based hybrid nanofluids.

Fabrication and characterization of high-sensitivity, wide-range, and flexible MEMS thermal flow velocity sensors

With the rapid development of various fields, including aerospace, industrial measurement and control, and medical monitoring, the need to quantify flow velocity measurements is increasing. It is difficult for traditional flow velocity sensors to fulfill accuracy requirements for velocity measurements due to their small ranges, susceptibility to environmental impacts, and instability. Herein, to optimize sensor performance, a flexible microelectromechanical system (MEMS) thermal flow sensor is proposed that combines the working principles of thermal loss and thermal temperature difference and utilizes a flexible cavity substrate made of a low-thermal-conductivity polyimide/SiO2 (PI/SiO2) composite porous film to broaden the measurement range and improve the sensitivity. The measurement results show that the maximum measurable flow velocity can reach 30 m/s with a resolution of 5.4 mm/s. The average sensitivities of the sensor are 59.49 mV/(m s−1) in the medium-to-low wind velocity range of 0–2 m/s and 467.31 mV/(m s−1) in the wind velocity range of 2–30 m/s. The sensor proposed in this work can enable new applications of flexible flow sensors and wearable devices.

Difference in right and left cardio-ankle vascular index as a useful marker for evaluation of leg ischemia in patients with lower extremity arterial disease.

Cardio-ankle vascular index (CAVI) is a blood pressure-independent measure of heart-ankle pulse wave velocity and is used as an indicator of arterial stiffness. However, there is a paradox that CAVI is inversely associated with leg ischemia in patients with lower extremity arterial disease (LEAD). The aim of this study was to clarify the significance of the absolute value of left and right difference in CAVI (diff-CAVI). The subjects were 165 patients with LEAD who had received medication therapy. Relationships between diff-CAVI and leg ischemia were investigated. Leg ischemia was evaluated by decrease in leg arterial flow using ankle-brachial index (ABI) and by symptoms using the Rutherford classification. There was a significant inverse correlation between diff-CAVI and ABI. The odds ratio for low ABI of the 3rd versus 1st tertile groups of diff-CAVI was 7.03 with a 95% confidence interval of 2.71 ∼ 18.22. In ROC analysis, the cutoff value of diff-CAVI for low ABI was 1.05 with a sensitivity of 61.1% and a specificity of 87.1%. The median of diff-CAVI was significantly higher in subjects with grade 2 of the Rutherford classification than in subjects with its grade 1. diff-CAVI showed an inverse association with ABI and a positive association with symptoms of leg ischemia. Thus, diff-CAVI is thought to be a useful indicator of leg ischemia in LEAD patients.

Investigations of Combustion Characteristics of N-Dodecane/Air Mixtures at High Temperatures Through Laminar Burning Velocity Measurements

ABSTRACT In this work, combustion characteristics of premixed n-dodecane/air mixtures are investigated at elevated mixture temperatures through laminar burning velocity measurements at atmospheric pressure and elevated temperatures using an externally heated diverging channel method. The experiments were conducted up to high-temperature conditions of 610 K at varying equivalence ratios (ϕ = 0.7–1.3) to mimic the conditions of practical application devices. With an increase in the mixture temperature, the laminar burning velocity increases because of the rise in reactant enthalpy. The peak laminar burning velocity is observed at ϕ = 1.1 for all the mixture temperatures, except at 600 K, where the maximum laminar burning velocity is observed for stoichiometric conditions. The minimum temperature exponent exists for a slightly rich mixture condition (ϕ = 1.1). Out of all mechanisms, the laminar burning velocity predictions using the Lawrence Livermore National Laboratory model are consistent with the present data at all mixture temperatures. Normalized sensitivity analysis is analyzed using the Lawrence Livermore National Laboratory mechanism to understand the key reactions affecting the laminar burning velocity of the n-dodecane/air mixture. The chain branching reaction R16: H + O2 <=> O + OH has a major influence on the enhancement of the laminar burning velocity. Reaction pathway analysis is carried out at stoichiometric conditions for mixture temperatures of 400 K and 600 K, where it is observed that the elemental flux of the reaction, converting CO to CO2, gets reduced by 42.91% at the high-temperature condition.

Estimated Pulse Wave Velocity is Associated with a Higher Risk of Dementia in the Health and Retirement Study.

In this paper, we use the Health and Retirement Study (HRS) to examine the relationship between an estimated measure of pulse wave velocity (ePWV) and cognitive impairment with no dementia and dementia, respectively. We modeled the relationship between ePWV and cognitive status in 2006/2008 using data from 8,492 men and women (mean age 68.6 years) controlling for age, blood pressure, sociodemographic and socioeconomic characteristics (sex, race and ethnicity, education, income, wealth), health behaviors (smoking and physical activity), body mass index (BMI), health status and related medication use (history of cardiovascular disease, diabetes, and stroke), and cerebrovascular disease (CVD)-related biomarkers (C-reactive protein, cystatin-C, hemoglobin A1c, total cholesterol, high-density lipoprotein [HDL] cholesterol). We assess cognitive function with the 27-item Langa-Weir Telephone Interview for Cognitive Status (TICS) scale. ePWV is derived from an equation based on participant age and resting blood pressure. In a model that controlled for the constituent components of ePWV (age, age squared, systolic and diastolic blood pressure), ePWV is associated with increased odds of having cognitive impairment with no dementia (OR=2.761) and dementia (OR=6.344) relative to a group with no cognitive impairment or dementia. After controlling for the constituent components of ePWV, sociodemographic and socioeconomic characteristics, health status and medication use, health behaviors, BMI, and CVD-related biomarkers, ePWV remains significantly associated with dementia (OR=3.969) but not cognitive impairment with no dementia (OR=1.782). These findings suggest that ePWV may be a novel research tool and biomarker of vascular aging that can be used in large, population-representative studies to examine cognitive aging and dementia risk.

Augmented Biphasic Breathing Using Sniff and an Oral Positive Expiratory Pressure Device (Sniff-PEP) in Fontan Patients.

The use of an oral positive expiratory pressure device (oPEP) with sniff breathing (Sniff-PEP) mimics biphasic ventilation. Biphasic ventilation increases pulmonary blood flow and cardiac output in Fontan patients. The aim of this study was to assess the effect of Sniff-PEP on Fontan flow velocities. A single-center, pilot, prospective study was carried out in 15 subjects with Fontan circulation enrolled to use the oPEP device for 1month. Subjects were instructed on Sniff-PEP and to use the device for 10-15min 3-4 times a day. Measurements of flow velocity and cardiac output were measured via echocardiogram and quality of life assessments were performed at baseline and 4-6weeks later. The mean age at enrollment was 19.9 ± 8.7years (age range of 10-37years). 7 patients (47%) had dominant left ventricle and 8 (53%) had an open fenestration. There was a statistically significant increase in flow velocities in the hepatic vein from 27.5 ± 7.6 to 35.1 ± 11.3cm/s (p = 0.003), left pulmonary artery from 51.6 ± 16.6 to 57.6 ± 21.1cm/s (p = 0.01), and right pulmonary artery from 43.1 ± 14.2 to 45.8 ± 17.2cm/s (p = 0.04). With chronic use, the mean fenestration gradient slightly decreased from 4.5 ± 1.6 to 4.1 ± 1.9mmHg but the difference was not statistically significant (p = 0.14). oPEP device therapy increased flow velocity in several areas in the Fontan circulation with acute use. Further studies are needed to assess the effects long term.Clinical Trial Registration: URL: https://clinicaltrials.gov . Unique identifier: NCT03251742.

Predicting Rail Corrugation Based on Convolutional Neural Networks Using Vehicle’s Acceleration Measurements

This paper presents a deep learning approach for predicting rail corrugation based on on-board rolling-stock vertical acceleration and forward velocity measurements using One-Dimensional Convolutional Neural Networks (CNN-1D). The model’s performance is examined in a 1:10 scale railway system at two different forward velocities. During both the training and test stages, the CNN-1D produced results with mean absolute percentage errors of less than 5% for both forward velocities, confirming its ability to reproduce the corrugation profile based on real-time acceleration and forward velocity measurements. Moreover, by using a Gradient-weighted Class Activation Mapping (Grad-CAM) technique, it is shown that the CNN-1D can distinguish various regions, including the transition from damaged to undamaged regions and one-sided or two-sided corrugated regions, while predicting corrugation. In summary, the results of this study reveal the potential of data-driven techniques such as CNN-1D in predicting rails’ corrugation using online data from the dynamics of the rolling-stock, which can lead to more reliable and efficient maintenance and repair of railways.

AI-driven predictions of geophysical river flows with vegetation

In river research, forecasting flow velocity accurately in vegetated channels is a significant challenge. The forecasting performance of various independent and hybrid machine learning (ML) models are thus quantified for the first time in this work. Utilizing flow velocity measurements in both natural and laboratory flume experiments, we assess the efficacy of four distinct standalone machine learning techniques—Kstar, M5P, reduced error pruning tree (REPT) and random forest (RF) models. In addition, we also test for eight types of hybrid ML algorithms trained with an Additive Regression (AR) and Bagging (BA) (AR-Kstar, AR-M5P, AR-REPT, AR-RF, BA-Kstar, BA-M5P, BA-REPT and BA-RF). Findings from a comparison of their predictive capabilities, along with a sensitivity analysis of the influencing factors, indicated: (1) Vegetation height emerged as the most sensitive parameter for determining the flow velocity; (2) all ML models displayed outperforming empirical equations; (3) nearly all ML algorithms worked optimal when the model was built using all of the input parameters. Overall, the findings showed that hybrid ML algorithms outperform regular ML algorithms and empirical equations at forecasting flow velocity. AR-M5P (R2 = 0.954, R = 0.977, NSE = 0.954, MAE = 0.042, MSE = 0.003, and PBias = 1.466) turned out to be the optimal model for forecasting of flow velocity in vegetated-rivers.

Wavelet Cross-Correlation Signal Processing for Two-Phase Flow Control System in Oil Well Production

An algorithm based on continuous measurement of multiphase flows of oil well production has been designed to improve the efficiency of the technical control of oil production processes in the field. Separation-free, non-contact measurement of multiphase flows of oil well products allows increasing the efficiency of managing oil production processes in the field. Monitoring the current density using radioisotope measuring transducers (RMTs) allows obtaining information about the structure of the flow in the form of the distribution of gas inclusions and the speed of movement of liquid and gas in a two-phase flow. Fluid velocity measurement is based on digital processing of RMT signals, applying a continuous or discrete undecimated wavelet transform to them, and assessing the cross-correlation of wavelet coefficients in individual subspaces of the wavelet decomposition. The cross-correlation coefficients of two RMT signals located at a base distance, calculated in the subspaces of the wavelet decomposition, characterize the speed of movement of gas bubbles of different sizes in a vertical pipe. The measurement assumes that the velocity of the liquid phase of the oil flow in a vertical pipe mainly corresponds to the velocity of small bubbles. This speed should be determined by the maximum cross-correlation of wavelet coefficients in the corresponding decomposition subspace. Computer modeling made it possible to evaluate the characteristics of the algorithm for controlling the speed of liquid movement in the gas–liquid flow of oil well products and determine the mass flow rate of the liquid and the relative value of the gas content. The implementation of the algorithm in a multi-channel version of the device allows monitoring an entire cluster of wells in the field.

Uncertainty quantification for velocity measurement with 2D2C particle image velocimetry

Abstract In this paper, a theoretical model of dimensionless instantaneous and average velocity measurement uncertainty quantification with 2D2C particle image velocimetry (PIV) is established under the framework of generally adopted international uncertainty quantification standards. The effectiveness of the model is verified using uniform flow field testing data. Combined with semi-quantitative analysis of the theoretical model, uncertainty control suggestions for PIV measurements are given. The major sources affecting the uncertainty of instantaneous velocity measurements are the reference velocity, particle instantaneous pixel displacement, and their correlation term. For average velocity measurement uncertainty quantification, the uncertainty of particle average pixel displacement is effectively controlled by taking a large number of particle images. Thus, three single-component terms — the calibration factor, particle average pixel displacement and reference velocity — and two correlation terms — the particle average pixel displacement–calibration factor and the particle average pixel displacement–reference velocity — all make an important contribution to the average velocity measurement uncertainty. To reduce the uncertainty of PIV velocity measurement, one can reduce the reference velocity measurement uncertainty, optimize the PIV algorithm and improve the calibration factor by applying a high spatial resolution imaging system in experiments. In addition, reducing the reference velocity measurement uncertainty and improving the spatial resolution are key feasible methods.

Quantitative Digital Subtraction Angiography Measurement of Arterial Velocity at Low Radiation Dose Rates.

Quantitative digital subtraction angiography (qDSA) has been proposed to quantify blood velocity for monitoring treatment progress during blood flow altering interventions. The method requires high frame rate imaging [~ 30 frame per second (fps)] to capture temporal dynamics. This work investigates performance of qDSA in low radiation dose acquisitions to facilitate clinical translation. Velocity quantification accuracy was evaluated at five radiation dose rates in vitro and in vivo. Angiographic technique ranged from 30 fps digital subtraction angiography ( at the interventional reference point) down to a 30 fps protocol at 23% higher radiation dose per frame than fluoroscopy ( ). The in vitro setup consisted of a 3D-printed model of a swine hepatic arterial tree connected to a pulsatile displacement pump. Five different flow rates (3.5-8.8mL/s) were investigated in vitro. Angiography-based fluid velocity measurements were compared across dose rates using ANOVA and Bland-Altman analysis. The experiment was then repeated in a swine study (n = 4). Radiation dose rate reductions for the lowest dose protocol were 99% and 96% for the phantom and swine study, respectively. No significant difference was found between angiography-based velocity measurements at different dose rates in vitro or in vivo. Bland-Altman analysis found little bias for all lower-dose protocols (range: [- 0.1, 0.1]cm/s), with the widest limits of agreement ([- 3.3, 3.5]cm/s) occurring at the lowest dose protocol. This study demonstrates the feasibility of quantitative blood velocity measurements from angiographic images acquired at reduced radiation dose rates.

Discovery of the Remarkably Red L/T Transition Object VHS J183135.58-551355.9

We present the discovery of VHS J183135.58−551355.9 (hereafter VHS J1831−5513), an L/T transition dwarf identified as a result of its unusually red near-infrared colors (J − K S = 3.633 ± 0.277 mag; J − W2 = 6.249 ± 0.245 mag) from the VISTA Hemisphere Survey and CatWISE2020 surveys. We obtain low-resolution near-infrared spectroscopy of VHS J1831−5513 using the Magellan Folded port InfraRed Echellette spectrograph to confirm its extremely red nature and assess features sensitive to surface gravity (i.e., youth). Its near-infrared spectrum shows multiple CH4 absorption features, indicating an exceptionally low effective temperature for its spectral type. Based on proper-motion measurements from CatWISE2020 and a photometric distance derived from its K s -band magnitude, we find that VHS J1831−5513 is a likely (∼85% probability) kinematic member of the β Pictoris moving group. Future radial velocity and trigonometric parallax measurements will clarify such membership. Follow-up mid-infrared or higher-resolution near-infrared spectroscopy of this object will allow for further investigation as to the cause(s) of its redness, such as youth, clouds, and viewing geometry.

Discovery of the Remarkably Red L/T Transition Object VHS J183135.58-551355.9

We present the discovery of VHS J183135.58−551355.9 (hereafter VHS J1831−5513), an L/T transition dwarf identified as a result of its unusually red near-infrared colors (J − K S = 3.633 ± 0.277 mag; J − W2 = 6.249 ± 0.245 mag) from the VISTA Hemisphere Survey and CatWISE2020 surveys. We obtain low-resolution near-infrared spectroscopy of VHS J1831−5513 using the Magellan Folded port InfraRed Echellette spectrograph to confirm its extremely red nature and assess features sensitive to surface gravity (i.e., youth). Its near-infrared spectrum shows multiple CH4 absorption features, indicating an exceptionally low effective temperature for its spectral type. Based on proper-motion measurements from CatWISE2020 and a photometric distance derived from its K s -band magnitude, we find that VHS J1831−5513 is a likely (∼85% probability) kinematic member of the β Pictoris moving group. Future radial velocity and trigonometric parallax measurements will clarify such membership. Follow-up mid-infrared or higher-resolution near-infrared spectroscopy of this object will allow for further investigation as to the cause(s) of its redness, such as youth, clouds, and viewing geometry.

Research on multi-velocity measurement method of solid particles based on interdigital electrostatic sensor

Abstract In gas-solid two-phase flow, the velocities of solid particles will show different distributions as the flow pattern changes, and it is a challenging task to achieve the measurement of different velocity parameters in the flow field. To obtain the velocity information of solid particles in the flow field, a novel method to achieve multi-velocity measurement of solid particles is proposed by using the spatial filtering characteristics of interdigital electrostatic sensor with variational mode decomposition (VMD). Firstly, a three-dimensional model of the interdigital electrostatic sensor is established using COMSOL simulation software. Based on its spatial dynamic sensitivity, a theoretical analysis of its spatial filtering characteristics is conducted to obtain a mathematical relationship for velocity measurement. Subsequently, the VMD decomposition method is applied to an experiment involving charged wires, with the experimental results verifying the effectiveness of VMD in achieving multi-scale decomposition of electrostatic signals. Finally, the proposed measurement method is validated on a gravity feeding device and a pneumatic conveying system. The experimental results demonstrate that the method exhibits good feasibility.

The velocity extraction and feature analysis of glacier surface motion in the Gongar region based on multi-source remote sensing data

The movement of glaciers plays a crucial role in environmental and geological processes, significantly influencing the formation and dynamics of ice bodies. This study leverages feature tracking technology to analyze optical and Synthetic Aperture Radar (SAR) remote sensing imagery, specifically GF-1 optical images and GF-3, Sentinel-1 SAR images, captured during the 2020 to 2021 ablation season in Gongar. The aim was to quantify glacier surface velocities and to evaluate the comparative effectiveness of different remote sensing modalities in capturing these dynamics. Our findings indicate a strong consistency in the spatial distribution of glacier surface velocities derived from diverse remote sensing data sources, with high-precision optical imagery (GF-1) yielding the most accurate velocity measurements, followed by Sentinel-1 SAR data. Notably, large glaciers in Gongar exhibited rapid movements, with an average velocity of 0.16 m/d, primarily at elevations between 4,500 and 6,500 m. The fastest velocities were recorded at approximately 4,500 m elevation. Glaciers with inclines ranging from 10° to 60° displayed the highest velocities within the 20°–30° slope range. It was observed that glaciers on the southeast slope moved faster, exhibiting the highest average surface velocity, in contrast to those on the west slope, which moved more slowly. The surface velocity of the ice tongue region of Krayaylak Glacier that the largest glacier in Pamir, was observed to be lower than 0.6 m/d, indicating a slow movement speed. The study also reveals that the effectiveness of different remote sensing data in detecting glacier velocity in Gongar, with high-resolution data more accurately capturing surface velocities in melting areas or those with slower movement. This study underscores the importance of multi-source remote sensing data in understanding glacier dynamics and contributes valuable insights into the mechanisms driving glacier movements.

A novel method for measuring center-axis velocity of unmanned aerial vehicles through synthetic motion blur images

Accurate velocity measurement of unmanned aerial vehicles (UAVs) is essential in various applications. Traditional vision-based methods rely heavily on visual features, which are often inadequate in low-light or feature-sparse environments. This study presents a novel approach to measure the axial velocity of UAVs using motion blur images captured by a UAV-mounted monocular camera. We introduce a motion blur model that synthesizes imaging from neighboring frames to enhance motion blur visibility. The synthesized blur frames are transformed into spectrograms using the Fast Fourier Transform (FFT) technique. We then apply a binarization process and the Radon transform to extract light-dark stripe spacing, which represents the motion blur length. This length is used to establish a model correlating motion blur with axial velocity, allowing precise velocity calculation. Field tests in a hydropower station penstock demonstrated an average velocity error of 0.048 m/s compared to ultra-wideband (UWB) measurements. The root-mean-square error was 0.025, with an average computational time of 42.3 ms and CPU load of 17%. These results confirm the stability and accuracy of our velocity estimation algorithm in challenging environments.

Research on multi-velocity measurement method of solid particles based on interdigital electrostatic sensor

Abstract In gas-solid two-phase flow, the velocities of solid particles will show different distributions as the flow pattern changes, and it is a challenging task to achieve the measurement of different velocity parameters in the flow field. To obtain the velocity information of solid particles in the flow field, a novel method to achieve multi-velocity measurement of solid particles is proposed by using the spatial filtering characteristics of interdigital electrostatic sensor with variational mode decomposition (VMD). Firstly, a three-dimensional model of the interdigital electrostatic sensor is established using COMSOL simulation software. Based on its spatial dynamic sensitivity, a theoretical analysis of its spatial filtering characteristics is conducted to obtain a mathematical relationship for velocity measurement. Subsequently, the VMD decomposition method is applied to an experiment involving charged wires, with the experimental results verifying the effectiveness of VMD in achieving multi-scale decomposition of electrostatic signals. Finally, the proposed measurement method is validated on a gravity feeding device and a pneumatic conveying system. The experimental results demonstrate that the method exhibits good feasibility.

The velocity extraction and feature analysis of glacier surface motion in the Gongar region based on multi-source remote sensing data

The movement of glaciers plays a crucial role in environmental and geological processes, significantly influencing the formation and dynamics of ice bodies. This study leverages feature tracking technology to analyze optical and Synthetic Aperture Radar (SAR) remote sensing imagery, specifically GF-1 optical images and GF-3, Sentinel-1 SAR images, captured during the 2020 to 2021 ablation season in Gongar. The aim was to quantify glacier surface velocities and to evaluate the comparative effectiveness of different remote sensing modalities in capturing these dynamics. Our findings indicate a strong consistency in the spatial distribution of glacier surface velocities derived from diverse remote sensing data sources, with high-precision optical imagery (GF-1) yielding the most accurate velocity measurements, followed by Sentinel-1 SAR data. Notably, large glaciers in Gongar exhibited rapid movements, with an average velocity of 0.16 m/d, primarily at elevations between 4,500 and 6,500 m. The fastest velocities were recorded at approximately 4,500 m elevation. Glaciers with inclines ranging from 10° to 60° displayed the highest velocities within the 20°–30° slope range. It was observed that glaciers on the southeast slope moved faster, exhibiting the highest average surface velocity, in contrast to those on the west slope, which moved more slowly. The surface velocity of the ice tongue region of Krayaylak Glacier that the largest glacier in Pamir, was observed to be lower than 0.6 m/d, indicating a slow movement speed. The study also reveals that the effectiveness of different remote sensing data in detecting glacier velocity in Gongar, with high-resolution data more accurately capturing surface velocities in melting areas or those with slower movement. This study underscores the importance of multi-source remote sensing data in understanding glacier dynamics and contributes valuable insights into the mechanisms driving glacier movements.