Accurate Flow Velocity Research Articles

An optimized entropy-based model for estimating river confluence hydrodynamics: Accounting for the effects of velocity dip

The entropy model has been successfully used to calculate the flow velocity profile of rivers according to the measured surface-flow velocity. However, it has limitations in the large river where the obvious secondary flow and flow dip (i.e., the location of maximum flow velocity moves down from the surface) occur. The confluence of rivers is usually characterized by strong secondary circulations, posing great challenges for the accurate flow velocity and discharge estimation. This paper aims to assess the effectiveness of the previous entropy-based approach in velocity estimation of a large confluence and then propose an optimized method. The flow velocity data of the large confluence between the Yangtze River and the Poyang Lake under different discharge ratios were used. The performance of the previous entropy model got worse for the stronger secondary circulation survey. To better estimate the confluence hydrodynamics, a moving average method was introduced with its advantage of smoothing data and integrated into the framework of the entropic method for the better assessment of dip parameter, and hence the better estimation of flow velocity profiles. Furthermore, the maximum velocity was found to locate at one third of the total depth below the water surface. The range of entropic function 0.48 ∼ 0.68 could provide a basis for the determination under various flow conditions in rivers. The value of entropic function and the dip parameter got larger as the discharge ratio decreased. This research has expanded the application for estimating hydrodynamics in large rivers.

Friction measurement of aircraft wing based on optimized FlowNet2.0

Few studies have applied the deep optical flows model to global friction measurements of aircraft wing. This study used an optimized FlowNet2.0 model to measure friction of wing based on fluorescent oil film, which achieved the first integration of deep learning and friction measurement. Two input images of the traditional FlowNet2.0 model were extended to multiple images so that more flow features and details could be with them. It is the specific part of optimization that will also further improve the measurement accuracy of FlowNet2.0. Simulation experimental results show that the optimized FlowNet2.0 model reduces the Mean Absolute Percentage Error (MAPE) error by 8.51% and increases Root Mean Square Error (RMSE) by only 0.0138 when compared to the hybrid optical flow method, which indicate that the optimized FlowNet2.0 model has great potential for friction measurement. Measurements in continuous transonic wind tunnel tests demonstrate that FlowNet2.0 can calculate clearer and more accurate flow velocity than hybrid optical flow, and the solved friction magnitude distribution is consistent with the actual flow, which will be of great practical application in wind tunnel engineering.

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.

Shape and Flow Sensing in Arterial Image Guidance From UV Exposed Optical Fibers Based on Spatio-Temporal Networks.

Minimally invasive revascularization procedures such as percutaneous transluminal angioplasty seek to treat occlusions in peripheral arteries. However their ability to treat long occlusions are hampered by difficulties to monitor the location of intravascular devices such as guidewires using fluoroscopy which requires continuous radiation, and lack the capacity to measure physiological characteristics such as laminar blood flow close to occlusions. Fiber optic technologies provide means of tracking by measuring fibers under strain, however they are limited to known geometrical models and are not used to measure external variations. We present a navigation framework based on optical frequency domain reflectometry (OFDR) using fully-distributed optical sensor gratings enhanced with ultraviolet exposure to track the three-dimensional shape and surrounding blood flow of intra-vascular guidewires. To process the strain information provided by the continuous gratings, a dual-branch model learning spatio-temporal features allows to predict the output measures based on scattered wavelength distributions. The first network determines the 3D shape appearance of the guidewire using the input backscattered wavelength shift data in combination with prior segmentations, while a second network (graph temporal convolution network) produces estimates of vascular flow velocities using ground-truth 4D-flow MRI acquisitions. Experiments performed on synthetic and animal models, as well as in a preliminary human trial shows the capability of the model to generate accurate 3D shape tracking and blood flow velocities differences below 2 cm/s, thus providing realistic physiologic and anatomical properties for intravascular techniques. The study demonstrates the feasibility of using the device clinically, and could be integrated within revascularization workflows for treating occlusions in arteries, since the navigation framework involves minimal manual intervention.

Measurement of Mountain River Discharge Based on UHF Radar

River discharge is an essential hydrological index of the global water cycle and is important for flood and drought forecast. Ultrahigh frequency (UHF) radar has gained much attention since it can remotely retrieve river discharge from the surface flow velocity measurement in real-time and all-weather condition. However, it becomes challenging for the mountain river case where the water level changes more significant than that of the plain river due to the deep-narrow channel, which breaks the previous empirical relationship between surface flow velocity and discharge. In this letter, an improved index-velocity method is proposed to address this issue. Through modeling the nonuniform variation of the cross-sectional area with water level, a more accurate surface flow velocity–discharge relationship is established. Experimental results show that the proposed method produces higher calculation accuracy than that of the previous methods, which enlarges the applicable scenario of UHF radar in discharge measurement.

Identification of venular capillary remodelling: a possible link to the development of periodontitis?

PurposeThe present study measured changes in arteriolar and venular capillary flow and structure in the gingival tissues during the development of plaque-induced gingival inflammation by combining dynamic optical coherence tomography (OCT), laser perfusion, and capillaroscopic video imaging.MethodsGingival inflammation was induced in 21 healthy volunteers over a 3-week period. Gingival blood flow and capillary morphology were measured by dynamic OCT, laser perfusion imaging, and capillaroscopy, including a baseline assessment of capillary glycocalyx thickness. Venular capillary flow was estimated by analysis of the perfusion images and mean blood velocity/acceleration in the capillaroscopic images. Readings were recorded at baseline and weekly over the 3 weeks of plaque accumulation and 2 weeks after brushing was resumed.ResultsPerfusion imaging demonstrated a significant reduction of gingival blood flow after 1 and 2 weeks of plaque accumulation (P<0.05), but by 3 weeks of plaque accumulation there was a more mixed picture, with reduced flow in some participants and increased flow in others. Participants with reduced flux at 3 weeks also demonstrated venular-type flow as determined by perfusion images and evidence of the development of venular capillaries as assessed by the velocity/acceleration ratio in capillaroscopic images. After brushing resumed, these venular capillaries were broken down and replaced by arteriolar capillaries.ConclusionsAfter 3 weeks of plaque accumulation, there was wide variation in microvascular reactions between the participants. Reduced capillary flow was associated with the development of venular capillaries in some individuals. This is noteworthy, as an early increase in venous capillaries is a key vascular feature of cardiovascular disease, psoriasis, Sjögren syndrome, and rheumatoid arthritis—diseases with a significant association with the development of severe gingival inflammation, which leads to periodontitis. Future investigations of microvascular changes in gingival inflammation might benefit from accurate capillary flow velocity measurements to assess the development of venular capillaries.

Towards capturing a database of respiratory exhalations from flow visualisations

One of the most common modes of infection transmission is through pathogen laden droplets expelled during natural human respiratory exhalations such as speaking, coughing, and sneezing. Infection control guidelines for the prevention of respiratory infection make assumptions about two key parameters: the safe distance between an infected and healthy individual and the size of large and small droplets (Bahl et al., 2020). Studies in the past have utilised flow visualisation techniques to understand the dynamics of respiratory flows but most of them provide only qualitative data on respiratory droplets and do not provide sufficient detail to estimate accurate flow velocities (Bourouiba et al., 2014; Vansciver et al., 2011; Scharfman et al., 2016). One of the reasons this remains a demanding application is the vast range of droplet sizes that are expelled at various velocities. Here, we present an experimental framework using particle tracking to understand the flow dynamics of the expelled droplets. Three different illumination techniques were used to capture high-speed frames of different exhalations (see figure 1). The high density of droplets in case of sneezing lead to overlap of droplet trajectories with volume illumination approach, which was resolved using tailored optics to illuminate only a slice of sneeze flow. Thereafter, the image processing techniques required for precise PTV were refined to examine droplet dynamics of various exhalations (see figure 2). The techniques were applied to multiple cases of respiratory exhalations to understand subject to subject variability.&#x0D; The results for sneezing revealed a mean droplet velocity of 2 m/s to 5.4 m/s across different subjects. Additionally, less than 1% of droplets were expelled at velocities greater than 10 m/s and almost 80% of were expelled at velocities less than 5 m/s. These values were substantially lower than the values usually assumed in studies modelling or replicating sneezes (Xie et al., 2007; Atkinson and Wein, 2008). The results also revealed a high variation in the droplet dynamics, even among the sneezes from the same subject. Flow direction, spread angle, and head movement were also quantified, and the results reveal substantial variation between the subjects. In the case of coughing, maximum droplet velocities observed were in the range of 10−15 m/s however, these high velocities were detected only during the initial 0.05 s.&#x0D; This work addresses the critical gaps in the understanding of the respiratory transmission of infection by providing valuable data on the droplet dynamics of various exhalations, on which the experimental data was very limited in the existing literature. Furthermore, this data will aid in numerical modelling of respiratory flows, particularly for sneezes, as studies to date rely only on airflow data of the exhalations.

On the Uncertainty of the Image Velocimetry Method Parameters

Image velocimetry is a popular remote sensing method mainly because of the very modest cost of the necessary equipment. However, image velocimetry methods employ parameters that require high expertise to select appropriate values in order to obtain accurate surface flow velocity estimations. This introduces considerations regarding the subjectivity introduced in the definition of the parameter values and its impact on the estimated surface velocity. Alternatively, a statistical approach can be employed instead of directly selecting a value for each image velocimetry parameter. First, probability distribution should be defined for each model parameter, and then Monte Carlo simulations should be employed. In this paper, we demonstrate how this statistical approach can be used to simultaneously produce the confidence intervals of the estimated surface velocity, reduce the uncertainty of some parameters (more specifically, the size of the interrogation area), and reduce the subjectivity. Since image velocimetry algorithms are CPU-intensive, an alternative random number generator that allows obtaining the confidence intervals with a limited number of iterations is suggested. The case study indicated that if the statistical approach is applied diligently, one can achieve the previously mentioned threefold objective.

Deep Learning-Based Super-resolution Ultrasound Speckle Tracking Velocimetry

Deep ultrasound localization microscopy (deep-ULM) allows sub-wavelength resolution imaging with deep learning. However, the injection of contrast agents (CAs) in deep-ULM is debatable because of their potential risk. In this study, we propose a deep learning-based super-resolution ultrasound (DL-SRU), which employs the concept of deep-ULM and a convolutional neural network. The network is trained with synthetic tracer images to localize positions of red blood cells (RBCs) and reconstruct vessel geometry at high resolution, even for CA-free ultrasound (US) images. The proposed algorithm is validated by comparing the full width at half-maximum values of the vascular profiles reconstructed by other techniques, such as the standard ULM and the US average intensity under in silico and in vitro conditions. RBC localization by DL-SRU is also compared with that by other localization approaches to validate its performance under in vivo condition, especially for veins in the human lower extremity. Furthermore, a two-frame particle tracking velocimetry (PTV) algorithm is applied to DL-SRU localization for accurate flow velocity measurement. The velocity profile obtained by applying the PTV is compared with a theoretical value under in vitro condition to verify its compatibility with the flow measurement modality. The velocity vectors of individual RBCs are obtained to determine the applicability to in vivo conditions. DL-SRU can achieve high-resolution vessel morphology and flow dynamics in vasculature, mapping 110 super-resolved images per second on a standard PC, regardless of various imaging conditions. As a result, the DL-SRU technique is much more robust in localization compared with previous deep-ULM. In addition, the performance of DL-SRU is nearly the same as that of deep-ULM in rapid computational processing and high measurement accuracy. Thus, DL-SRU might become an effective and useful instrument in clinical practice.

Flow Velocity Measurement Using a Spatial Averaging Method with Two-Dimensional Flexural Ultrasonic Array Technology.

Accurate average flow velocity determination is essential for flow measurement in many industries, including automotive, chemical, and oil and gas. The ultrasonic transit-time method is common for average flow velocity measurement, but current limitations restrict measurement accuracy, including fluid dynamic effects from unavoidable phenomena such as turbulence, swirls or vortices, and systematic flow meter errors in calibration or configuration. A new spatial averaging method is proposed, based on flexural ultrasonic array transducer technology, to improve measurement accuracy and reduce the uncertainty of the measurement results. A novel two-dimensional flexural ultrasonic array transducer is developed to validate this measurement method, comprising eight individual elements, each forming distinct paths to a single ultrasonic transducer. These paths are distributed in two chordal planes, symmetric and adjacent to a diametral plane. It is demonstrated that the root-mean-square deviation of the average flow velocity, computed using the spatial averaging method with the array transducer is 2.94%, which is lower compared to that of the individual paths ranging from 3.65% to 8.87% with an average of 6.90%. This is advantageous for improving the accuracy and reducing the uncertainty of classical single-path ultrasonic flow meters, and also for conventional multi-path ultrasonic flow meters through the measurement via each flow plane with reduced uncertainty. This research will drive new developments in ultrasonic flow measurement in a wide range of industrial applications.

Strong Scattering Targets Separation Based on Fractional Fourier Transformation in Pulse-to-Pulse Coherent Acoustical Doppler Current Profilers

The influence of the strong scattering targets (SSTs) on the flow cell velocities, i.e., the fish-bias effect, is a common phenomenon existing widely in real-time measurements of ocean and river current profiles when using pulse-to-pulse coherent acoustical Doppler current profilers (ADCPs), because the SSTs with different velocities and excessively high echo strengths are inevitable in the ocean and river background flow mediums. However, there are no effective means to eliminate the SST-caused biases and recover the current profiles accurately. The common solutions to solve the SST problem either neglect the influence simply, or abandon the affected data directly, which of course causes wrong current profiles or even invalidation of the on-site measurement. Therefore, this paper proposes an SST separation method to eliminate the SST-caused biases in ADCPs. By employing a fractional Fourier transform (FrFT)-based method, the SST echoes are separated from the background ones, so that the accurate estimation of both velocities of the flow cells and the SSTs are obtained through respective signals. First, the proposed SST separation method based on FrFT is theoretically analyzed and validated through a simulation model of flow measurement scenarios, which is constructed with a large number of distributed point targets and several SSTs. Then, an ADCP core algorithm is implemented with traditional correlation theory after the SST separation. Next, the performance of the proposed method is evaluated with different-strength SST signals being superposed to the background echoes. At last, the computational complexity of the FrFT algorithm is evaluated to show that the SST separation algorithm does not affect the real-time measurements of the current velocities significantly. The simulation results show that, even when the averaging power of an SST is 60 dB stronger than that of the averaging background echoes, accurate flow velocities of the affected flow cells can be recovered. Moreover, up to five SSTs around one flow cell with a total power 60 dB higher than that of the background echoes can be separated effectively by the separation algorithm with an affordable computational cost, with which both the current profile and the velocities of the SSTs are accurately obtained.

Manhattan Distance-Based Adaptive 3D Transform-Domain Collaborative Filtering for Laser Speckle Imaging of Blood Flow.

Laser speckle contrast imaging (LSCI) is a full-field, noncontact imaging technology for mapping blood flow with high spatio-temporal resolution, in which the speckle contrast can be estimated either in spatial domain or temporal domain. Temporal LSCI (tLSCI) provides higher spatial resolution than spatial domain does. However, when the number of sampling frames is limited, it is difficult to obtain accurate blood flow velocity owing to the significant statistical noise. The widely used spatially averaged tLSCI (savg-tLSCI) usually requires a large number of sampling frames to obtain acceptable denoising performance. Here, based on the nonlocal filtering strategy of block-matching and three-dimensional transform-domain collaborative filtering (BM3D), Manhattan distance-based adaptive BM3D (MD-ABM3D) is proposed to effectively manage the complicated inhomogeneous noise in tLSCI image and improve the signal-to-noise ratio. Manhattan distance improves the accuracy of the block matching in strong noise, and the adaptive algorithm adapts to the inhomogeneous noise and estimates suitable parameters for improved denoising. MD-ABM3D improves 4.91 dB in peak signal-to-noise ratio relative to savg-tLSCI. It achieves stability for denoising tLSCI image with different temporal windows. The image-quality evaluation of MD-ABM3D for tLSCI (t = 20 frames) equals that of savg-tLSCI (t = 60 frames). It achieves high signal-to-noise ratio with a reduced number of sampling frames. A reduced number of sampling frames are more practical for biomedical applications. It also offers higher temporal resolution and less disturbance from the motion of the moving object.

Optical Tracking Velocimetry (OTV): Leveraging Optical Flow and Trajectory-Based Filtering for Surface Streamflow Observations

Nonintrusive image-based methods have the potential to advance hydrological streamflow observations by providing spatially distributed data at high temporal resolution. Due to their simplicity, correlation-based approaches have until recent been preferred to alternative image-based approaches, such as optical flow, for camera-based surface flow velocity estimate. In this work, we introduce a novel optical flow scheme, optical tracking velocimetry (OTV), that entails automated feature detection, tracking through the differential sparse Lucas-Kanade algorithm, and then a posteriori filtering to retain only realistic trajectories that pertain to the transit of actual objects in the field of view. The method requires minimal input on the flow direction and camera orientation. Tested on two image data sets collected in diverse natural conditions, the approach proved suitable for rapid and accurate surface flow velocity estimations. Five different feature detectors were compared and the features from accelerated segment test (FAST) resulted in the best balance between the number of features identified and successfully tracked as well as computational efficiency. OTV was relatively insensitive to reduced image resolution but was impacted by acquisition frequencies lower than 7–8 Hz. Compared to traditional correlation-based techniques, OTV was less affected by noise and surface seeding. In addition, the scheme is foreseen to be applicable to real-time gauge-cam implementations.

Processing methods for photoacoustic Doppler flowmetry with a clinical ultrasound scanner.

Photoacoustic flowmetry (PAF) based on time-domain cross correlation of photoacoustic signals is a promising technique for deep tissue measurement of blood flow velocity. Signal processing has previously been developed for single element transducers. Here, the processing methods for acoustic resolution PAF using a clinical ultrasound transducer array are developed and validated using a 64-element transducer array with a -6 dB detection band of 11 to 17MHz. Measurements were performed on a flow phantom consisting of a tube (580 μm inner diameter) perfused with human blood flowing at physiological speeds ranging from 3 to 25 mm / s. The processing pipeline comprised: image reconstruction, filtering, displacement detection, and masking. High-pass filtering and background subtraction were found to be key preprocessing steps to enable accurate flow velocity estimates, which were calculated using a cross-correlation based method. In addition, the regions of interest in the calculated velocity maps were defined using a masking approach based on the amplitude of the cross-correlation functions. These developments enabled blood flow measurements using a transducer array, bringing PAF one step closer to clinical applicability.

Measuring flow velocity on frozen and non-frozen slopes of black soil through leading edge method

Flow velocity is a major parameter related to hillslope hydrodynamics erosion. This study aims to measure flow velocity over frozen and non-frozen slopes through leading edge method before being calibrated with accurate flow velocity to determine the correct coefficient for convenience of flow velocity measurement. Laboratory experiments were conducted on frozen and non-frozen soil slopes with flumes involving four slope gradients of 5°, 10°, 15°, and 20°and three flow rates of 1, 2, and 4L/min with a flume of 6m long and 0.1m wide. The measurements were made with a stopwatch to record the time duration that the water flow ran over the rill segments of 2, 4 and 6 m long. Accurate flow velocity was measured with electrolyte trace method, under pulse boundary condition. The leading edge and accurate flow velocities were used to determine the correction coefficient to convert the former to the latter. Results showed that the correction coefficient on frozen soil slope was 0.81 with a coefficient of determination (R2) of 0.99. The correction coefficient on non-frozen soil slope was 0.79 with R2 of 0.98. A coefficient of 0.8 was applicable to both soil surface conditions. The accurate velocities on the four frozen black soil slopes were approximately 30%, 54%, 71%, and 91% higher than those on non-frozen soil slopes. By contrast, the leading edge flow velocities on the frozen soil slopes were 23%, 54%, 67%, and 84% higher than those on non-frozen soil slopes. The flow velocities on frozen soil slopes increased with flow rate at all four slopes, but they increased from 5 to 15° before getting stabilized. Therefore, rill flow velocity can be effectively measured with leading edge method by multiplying the leading edge velocity with a correction coefficient of 0.80. This study provides a strategy to measure rill flow velocity for studies on soil erosion mechanisms.

Cardiac 4D phase-contrast CMR at 9.4\xa0T using self-gated ultra-short echo time (UTE) imaging

BackgroundTime resolved 4D phase contrast (PC) cardiovascular magnetic resonance (CMR) in mice is challenging due to long scan times, small animal ECG-gating and the rapid blood flow and cardiac motion of small rodents. To overcome several of these technical challenges we implemented a retrospectively self-gated 4D PC radial ultra-short echo-time (UTE) acquisition scheme and assessed its performance in healthy mice by comparing the results with those obtained with an ECG-triggered 4D PC fast low angle shot (FLASH) sequence.MethodsCardiac 4D PC CMR images were acquired at 9.4 T in healthy mice using the proposed self-gated radial center-out UTE acquisition scheme (TE/TR of 0.5 ms/3.1 ms) and a standard Cartesian 4D PC imaging sequence (TE/TR of 2.1 ms/5.0 ms) with a four-point Hadamard flow encoding scheme. To validate the proposed UTE flow imaging technique, experiments on a flow phantom with variable pump rates were performed.ResultsThe anatomical images and flow velocity maps of the proposed 4D PC UTE technique showed reduced artifacts and an improved SNR (left ventricular cavity (LV): 8.9 ± 2.5, myocardium (MC): 15.7 ± 1.9) compared to those obtained using a typical Cartesian FLASH sequence (LV: 5.6 ± 1.2, MC: 10.1 ± 1.4) that was used as a reference. With both sequences comparable flow velocities were obtained in the flow phantom as well as in the ascending aorta (UTE: 132.8 ± 18.3 cm/s, FLASH: 134.7 ± 13.4 cm/s) and pulmonary artery (UTE: 78.5 ± 15.4 cm/s, FLASH: 86.6 ± 6.2 cm/s) of the animals. Self-gated navigator signals derived from information of the oversampled k-space center were successfully extracted for all animals with a higher gating efficiency of time spent on acquiring gated data versus total measurement time (UTE: 61.8 ± 11.5%, FLASH: 48.5 ± 4.9%).ConclusionsThe proposed self-gated 4D PC UTE sequence enables robust and accurate flow velocity mapping of the mouse heart in vivo at high magnetic fields. At the same time SNR, gating efficiency, flow artifacts and image quality all improved compared to the images obtained using the well-established, ECG-triggered, 4D PC FLASH sequence.

Flow field sensing with bio-inspired artificial hair cell arrays

The hair cell is a biological sensor that uses microscopic hair-like structures to detect delicate motions of surrounding fluid. Inspired by this principle, an artificial hair cell (AHC) sensory method based on bio-membrane transducers is developed for airflow sensing. One-dimensional arrays built from modular AHC units measure local velocity at different points in a flow profile. Each of the AHC units uses thinly extruded glass fibers as mechanical receptors of air velocity. Hair vibrations are converted to current via hydrogel-supported (lipid bilayers) by virtue of their mechanosensitive properties. The AHC outputs are combined into one channel, requiring a demultiplexing operation to recover individual hair cell information. This is achieved by tuning each AHC hair length to a unique frequency response and recovering individual sensor information based on the frequency content of the signal. The method is entitled tuned frequency response encoding (TFRE). When several AHC units are excited simultaneously by an airflow, the resulting signal is a superposition of each sensor's individual response. The excitation at each sensor is reconstructed from the frequencies that appear in the combined output. This technique was inspired by how organisms use hair cells with tuned responses to mechanically process flow stimuli. It takes advantage of a novel AHC's high signal-to-noise ratio (compared to other membrane-based AHCs) and linear output response to flow velocity. Initial tests with linear arrays of three AHCs show success in estimating the shape of the velocity profile from an air source that varies in position and intensity. However, temporal variations in some cases in membrane size affect sensitivity properties and make accurate flow velocity estimation difficult. Nevertheless, under stable conditions, the measured velocity profiles match closely with theoretical predictions. The implementation of the array sensing method demonstrates the sensory capability of bilayer-based AHC arrays, but highlights the difficulties of achieving consistent performance with biomolecular materials.

Uncertainty in the modelling of spatial and temporal patterns of shallow groundwater flow paths: The role of geological and hydrological site information

Understanding the hydrological and hydrogeochemical responses of hillslopes and other small scale groundwater systems requires mapping the velocity and direction of groundwater flow relative to the controlling subsurface material features. Since point observations of subsurface materials and groundwater head are often the basis for modelling these complex, dynamic, three-dimensional systems, considerable uncertainties are inevitable, but are rarely assessed. This study explored whether piezometric head data measured at high spatial and temporal resolution over six years at a hillslope research site provided sufficient information to determine the flow paths that transfer nitrate leached from the soil zone through the shallow saturated zone into a nearby wetland and stream. Transient groundwater flow paths were modelled using MODFLOW and MODPATH, with spatial patterns of hydraulic conductivity in the three material layers at the site being estimated by regularised pilot point calibration using PEST, constrained by slug test estimates of saturated hydraulic conductivity at several locations. Subsequent Null Space Monte Carlo uncertainty analysis showed that this data was not sufficient to definitively determine the spatial pattern of hydraulic conductivity at the site, although modelled water table dynamics matched the measured heads with acceptable accuracy in space and time. Particle tracking analysis predicted that the saturated flow direction was similar throughout the year as the water table rose and fell, but was not aligned with either the ground surface or subsurface material contours; indeed the subsurface material layers, having relatively similar hydraulic properties, appeared to have little effect on saturated water flow at the site. Flow path uncertainty analysis showed that, while accurate flow path direction or velocity could not be determined on the basis of the available head and slug test data alone, the origin of well water samples relative to the material layers and site contour could still be broadly deduced. This study highlights both the challenge of collecting suitably informative field data with which to characterise subsurface hydrology, and the power of modern calibration and uncertainty modelling techniques to assess flow path uncertainty in hillslopes and other small scale systems.

Abstract 20360: MRI Assessment of Diastolic and Systolic Intraventricular Pressure Gradients in Heart Failure

Background: Non-invasive assessment of left ventricular (LV) diastolic function is an important goal to better understand physiologic abnormalities in heart failure. The spatiotemporal pattern of LV blood flow velocities during early filling can be used to estimate intraventricular pressure gradients driving early filling. MRI can provide accurate flow velocity information in the LV, but this method has not been exploited to assess diastolic function in heart failure (HF). Aim: To assess differences in intraventricular pressure gradients in heart failure and preserved ejection fraction (HFPEF) and heart failure with reduced ejection fraction (HFREF), compared to subjects without HF. Methods: We studied 23 subjects without HF, 13 subjects with HFREF and 14 subjects with HFPEF. Intraventricular flow during diastole was measured using 2D in-plane phase-contrast MRI. We solved the Eulier equations to compute intraventricular pressure gradients during LV early filling (figure) and during ejection. Results: Whereas the initial velocity of wave propagation (was not significantly different between the groups, the terminal diastolic propagation velocity during early diastolic filling was significantly slower (P=0.004) in HFREF (5.2 m/s; 95%CI=3.9-6.8) than in patients with HFPEF (9.5; 95%CI=6.6-13.6) or subjects without HF (8.9; 95%CI=7.3-10.7). In contrast, HFPEF was associated with a greater amplitude of the reversal of the base-to-apex intraventricular pressure gradient during early filling (β=-0.34; P=0.04), which was driven by the inertial component. Conclusions: HFPEF and HFREF are associated with distinct patterns of intraventricular pressure gradient abnormalities during early diastolic filling. Our findings support fundamental differences in the nature of diastolic dysfunction in these 2 conditions.

Effect of Elemental Shape and Modeling of Mixed Hybrid FEM on Numerical Solution

Mixed Hybrid Finite element method, MHF, is known to be an efficient and powerful analyzing technique with satisfactory accuracy when differentiated variables should be evaluated, such as flow velocity based on velocity potential theory or stress calculated from displacement. On the other hand, it is known that the calculation accuracy and efficiency are affected by elemental shape and modeling. The aim of this study is to confirm the efficiency and accuracy of MHF by comparing it with FEM, and to discuss optimum elemental shapes and modeling. MHF, unlike FEM, evaluates the velocity potential and the flux on the element boundaries not at the element nodes; consequently, the material or mass balance of each element due to flow-in and flow-out through the boundaries can be strictly estimated. The flow velocity within an element, however, should be interpolated by those values on the boundaries, and a distinctive shape function, known as the Raviart-Thomas shape function, becomes necessary. The authors established the MHF formulation for arbitrary shaped elements with 3 and 4 boundaries, and numerically investigated its efficiency and accuracy with various shapes of elements. Through this study, it was confirmed that MHF can evaluate accurate flow velocity with a fewer number of elements than those of FEM. In addition, the effect of shape and modeling of elements on the numerical results were discussed to obtain better accuracy even if the total number of the elements is kept constant.