Flow Technique Research Articles

An optical flow-based method for condition-based maintenance and operational safety in autonomous cleaning robots

Autonomous professional cleaning robots are ubiquitous today, coexist with humans, and the demand continues to improve hygiene and productivity, especially in large indoor workplaces. Hence, a proper maintenance strategy is essential to monitor the robot’s health, assuring flawless operation and safety. Manual supervision and periodic maintenance are adopted in general, which is challenging to detect failures in advance, resulting in higher maintenance costs and operational hazards. Anomalous vibration due to system degradation or environmental factors is an early symptom of failure or potential threats. Hence, predicting the sources of such abnormal vibration will help prompt maintenance action or set a hazard-free environment. However, such condition-monitoring research studies are not common for indoor cleaning robots. To fill this gap, we proposed an automated Condition Monitoring (CM) system, predicting the abnormal vibration sources that will enhance Condition-based Maintenance (CbM) and Operational Safety. A novel vibration-based CM method is presented for mobile robots, suitable for indoor environments under typical illumination states, using a monocular camera and optical flow technique instead of their usual perception-related applications. The sources of abnormal vibration were classified as terrain, collision, loose assembly, and structural imbalance. We modelled vibration as sparse optical flow, and the change in optical flow vector displacement due to the robot’s vibration is derived from consecutive camera frames used as vibration data. A practical framework is developed adopting the One Dimensional Convolutional Neural Network (1D CNN) model to fit this vibration data and tested predicting the vibration source class with an average accuracy of 93.8%. In addition, a vibration source map (CbM map) is proposed by fusing the predicted class in the workplace environment map for real-time monitoring. The case studies conducted using our in-house-developed cleaning robot show that the proposed CM framework will help the maintenance team for CbM, operational safety, and select proper maintenance strategies.

A method to determine the uncertainties of solid particle erosion measurements utilizing machine learning techniques

The ability to predict solid particle erosion is a vital task for the integrity management of pipelines carrying solid particles in many industries. Usually, the accuracy of predicted values is assessed by comparing them with the measured erosion of some cases where data is available. Thus, defining the uncertainties of the experimental data of erosion is crucial to evaluate the reliability of the predictions of solid particle erosion and consequently, the decisions made based on these predictions. The databases gathered by many investigators that include erosion data of various test cases have been considered for this uncertainty investigation. These cases include erosion rate data collected using the ultrasonic thickness measurement technique for gas-sand flows in 0.0508, 0.0762, and 0.1016-m elbow geometries with various particle sizes (75,150,300,550 μm) and air velocities vary in the range between 11 up to 202 m per seconds. This study aims to develop a framework for defining the uncertainties of erosion experiments. For this purpose, first, the uncertainties of repeated experiments were estimated and then analytical analysis was used to generalize these results to cases where repeated results were unavailable. Based on this idea, a correlation between erosion and the most effective parameters was developed. Then, machine learning algorithms were used to determine a correlation between the erosion rate and chosen parameters including material hardness, pipe size, particle size, and flow velocity. The framework of the current model/method is based on the combined results of analytical analysis and machine learning procedure, and the estimated uncertainties are presented by lower and upper bounds curves of erosion data.

Optimal Configuration of Extreme Fast Charging Stations Integrated with Energy Storage System and Photovoltaic Panels in Distribution Networks

Extreme fast charging (XFC) for electric vehicles (EVs) has emerged recently because of the short charging period. However, the extreme high charging power of EVs at XFC stations may severely impact distribution networks. This paper addresses the estimation of the charging power demand of XFC stations and the design of multiple XFC stations with renewable energy resources in current distribution networks. First, a Monte Carlo (MC) simulation tool was created utilizing the EV arrival time and state-of-charge (SOC) distributions obtained from the dataset of vehicle travel surveys. Various impact factors are considered to obtain a realistic estimation of the charging power demand of XFC stations. Then, a method for determining the optimal energy capacity of the energy storage system (ESS), ESS rated power, and size of photovoltaic (PV) panels for multiple XFC stations in a distribution network is presented, with the goal of achieving an optimal configuration. The optimal power flow technique is applied to this optimization so that the optimal solutions meet not only the charging demand but also the operational constraints related to XFC, ESS, PV panels, and distribution networks. Simulation results of a use case indicate that the presented MC simulation can estimate approximate real-world XFC charging demand, and the optimized ESS and PV units in multiple XFC stations in the distribution network can reduce the annual total cost of XFC stations and improve the performance of the distribution network.

Real-Time Estimation of Support Provision Capability for Poor-Observable Distribution Networks

An indispensable step towards coordinating the actions of distribution and transmission system operators ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dso</small> s- <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tso</small> ) is to estimate the range of flexibility that can be offered to the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tso</small> by <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dso</small> s. Within this context, a data-driven probabilistic approach is proposed to evaluate the capability of a distribution network in providing active and reactive power support in real-time. To this end, in an offline phase, a linear discriminant analysis model, together with a piecewise linear model of the distribution network are trained to delineate the boundary of a region representing the adherence to distribution network operational constraints. In the implementation phase, this region comprises the feasible set of a series of optimization problems, formulated to determine the support provision capability. These optimization problems are of iterative linear programming type, which allows for real-time applicability. The evaluated support capability can be deemed as the available reserve in real-time transmission operation, which enables providing a coordinated response towards unexpected events, and facilitates the participation of distributed resources in the balancing market by granting an up-to-date estimation of available supports. This approach is tested on the IEEE 123-node system and verified through comparison with an AC optimal power flow technique.

Numerical Study of Compressible and Weak Compressible Flows in Channel Based on Artificial Compressibility Method and Fully Artificial Compressibility Method

In this article, a numerical study of compressible and weak compressible Newtonian flows is achieved for a time marching, Galerkin algorithm. A comparison between two numerical techniques for such flows, namely the artificial compressibility method (AC–method) and the fully artificial compressibility method (FAC–method) is performed. In the first artificial compressibility parameter ( is added to the continuity equation, while this parameter is added to both continuity and momentum equations in the second technique. This strategy is implemented to treat the governing equations of Newtonian flow in cylindrical coordinates (axisymmetric). Particularly, this study concerns with the effect of the artificial compressibility parameters on the convergence level of solutions components. To confirm the analysis of these approaches, Poiseuille flow along a circular channel under an isothermal state is used as a simple test problem. The results show that when the AC-method is used there is a significant reduction in the level of time convergence of pressure and axial velocity compared to that with FAC-method. Here, for compressible flow the Tail model of state is employed to relate the pressure to density. In this context, the effect of Tail parameters and Reynolds number on the time convergence of solution components is also investigated in the present study. The results indicate a significant reduction in the time-stepping convergence as increasing in the {B,m}-value. In contrast, more difficulties are faced in the convergence when the level of the Reynolds number is increasing.

OAOF - Obstacle Avoidance using Optical Flow Algorithm for Unmanned Aerial Vehicles

A camera sensor may be a useful tool for a number of robotic and autonomous systems when used with MATLAB Simulink for obstacle avoidance. Using a camera sensor and MATLAB Simulink's optical flow algorithm, the obstacle avoidance system is shown in this work. For the purpose of identifying and following the motion of objects in the picture, the system analyses visual data from the camera. The motion vectors of the objects are computed using the optical flow technique, and the robot and obstacles' relative motion is calculated using those motion vectors. In order to provide safe and easy navigation, the system can recognise and avoid obstacles in real-time. The system is easily adjustable and adaptable to diverse applications thanks to the usage of MATLAB Simulink, which enables the integration of several sensor and control systems. The suggested system's findings demonstrate that using a camera sensor with the optical flow algorithm may be a useful tool for obstacle avoidance in a range of robotic and autonomous systems.

Glueball spectroscopy in lattice QCD using gradient flow

Removing ultraviolet noise from the gauge fields is necessary for glueball spectroscopy in lattice QCD. It is known that the Yang-Mills gradient flow method is an alternative approach instead of link smearing or link fuzzing in various aspects. In this work we study the application of the gradient flow technique to the construction of the extended glueball operators. We examine a simple application of the gradient flow method, which has some problems in glueball mass calculations at large flow time because of its nature of diffusion in space-time. To avoid this problem, the spatial links are evolved by the ``spatial gradient flow'', that is defined to restrict the diffusion to spatial directions only. We test the spatial gradient flow in calculations of glueball two-point functions and Wilson loops as a new smearing method, and then discuss its efficiency in comparison with the original gradient flow method and the conventional method. Furthermore, to demonstrate the feasibility of our proposed method, we determine the masses of the three lowest-lying glueball states, corresponding to the $0^{++}$, $2^{++}$ and $0^{-+}$ glueballs, in the continuum limit in the pure Yang-Mills theory.

Evaluation of lateral flow and ELISA techniques for detecting IgG and IgM antibodies in COVID-19 cases in Türkiye.

Antibody testing can complement molecular assays for detecting COVID-19. We evaluated the concurrence between lateral flow assay and enzyme-linked immunosorbent assay (ELISA) for the detection of antibodies in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The study was conducted at Kocaeli University, Türkiye. We used a lateral flow assay and ELISA to test serum samples from COVID-19 cases, confirmed by polymerase chain reaction assays (study group) and pre-pandemic stored serum samples (control group). We used Deming regression to evaluate the antibody measurements. The study group included 100 COVID-19 cases, and the control group included pre-pandemic samples from 156 individuals. The lateral flow assay detected immunoglobulin M (IgM) and G (IgG) antibodies in 35 and 37 study group samples. ELISA detected IgM nucleocapsid (N) antibodies in 18 samples, and IgG (N) and IgG spike 1 (S1) antibodies in 31 and 29 samples, respectively. None of the techniques detected antibodies in the control samples. Strong correlations were found between lateral flow IgG (N+ receptor-binding domain + S1) and ELISA IgG (S) (r = 0.93, P < 0.01) and ELISA IgG (N) (r = 0.81, P < 0.01). Weaker correlations were seen between ELISA IgG S and IgG N (r = 0.79, P < 0.01) and lateral flow assay and ELISA IgM (N) (r = 0.70, P < 0.01). Lateral flow assay and ELISA techniques gave consistent results for IgG/IgM antibody measurements towards spike and nucleocapsid proteins, suggesting that both methods can be used to detect COVID-19 where access to molecular test kits is difficult.

A novel model of non-linear radiative Williamson nanofluid flow along a vertical wavy cone in the presence of gyrotactic microorganisms

ABSTRACT A novel mathematical technique for an unsteady bioconvective flow of Williamson nanofluids over a vertical wavy cone in the presence of non-linear radiation and viscous dissipation impacts is investigated. The cone surface is described by a sinusoidal function while the components of the extra stress tensor are presented in terms of the dynamic viscosity. The irregular wavy surface is referred to the convective boundary conditions and nanoparticle passively controls conditions. The governing equations are presented in general forms then the condition and suitable transformations are used to map the wavy cone to regular domain. A fully implicit finite differences method (FDM) is applied to solve the converted system and wide ranges of independent variables are assumed. . The major outcomes disclosed that the increase in the Williamson number causes a reduction the nanofluid flow while the skin friction coefficient is rising. Further, there is an enhancement in the values of the Nusselt number up to 75% is obtained as Biot number is altered from 0.1 to 0.4. Also, the convective boundary conditions in presence of the non-linear radiations are recommended to enhance the velocity, temperature and rate of the heat transfer.

Rapid electrochemical biosensor composed of DNA probe/iridium nanoparticle bilayer for Aphanizomenon flos-aquae detection in fresh water

Toxic cyanobacteria pose a serious threat to aquatic ecosystems and require adequate detection and control systems. Aphanizomenon flos-aquae is a harmful cyanobacterium that produces the toxicant saxitoxin. Therefore, it is necessary to detect the presence of A. flos-aquae in lakes and rivers. We proposed a rapid electrochemical biosensor composed of DNA primer/iridium nanoparticles (IrNP) bilyer for the detection of A. flos-aquae in freshwater. The extracted A. flos-aquae gene (rbcL-rbcX) is used as a target, and it was fixed to the electrode using a 5'-thiolated DNA primer (capture probe). Then, Avidin@IrNPs complex for amplification of electrical signals was bound to the target through a 3'-biotinylated DNA primer (detection probe). To rapidly detect the target, an alternating current electrothermal flow technique was introduced in the detection step, which could reduce the detection time to within 20 min. To confirm the biosensor fabrication, atomic force microscopy was used to investigate the surface morphology. To evaluate the biosensor performance, cyclic voltammetry and electrochemical impedance spectroscopy were used. The target gene was detected at a concentration of 9.99 pg/mL in tap water, and the detection range was 0.1 ng/mL to 103 ng/mL with high selectivity. Based on the combined system, we employed A. flos-aquae in tap water. This rapid cyanobacteria detection system is a powerful tool for CyanoHABs in the field.

Mechanisms controlling the transport and evaporation of human exhaled respiratory droplets containing the severe acute respiratory syndromecoronavirus: a review.

Transmission of the coronavirus disease 2019 is still ongoing despite mass vaccination, lockdowns, and other drastic measures to control the pandemic. This is due partly to our lack of understanding on the multiphase flow mechanics that control droplet transport and viral transmission dynamics. Various models of droplet evaporation have been reported, yet there is still limited knowledge about the influence of physicochemical parameters on the transport of respiratory droplets carrying the severe acute respiratory syndromecoronavirus 2. Here we review the effects of initial droplet size, environmental conditions, virus mutation, and non-volatile components on droplet evaporation and dispersion, and on virus stability. We present experimental and computational methods to analyze droplet transport, and factors controlling transport and evaporation. Methods include thermal manikins, flow techniques, aerosol-generating techniques, nucleic acid-based assays, antibody-based assays, polymerase chain reaction, loop-mediated isothermal amplification, field-effect transistor-based assay, and discrete and gas-phase modeling. Controlling factors include environmental conditions, turbulence, ventilation, ambient temperature, relative humidity, droplet size distribution, non-volatile components, evaporation and mutation. Current results show that medium-sized droplets, e.g., 50µm, are sensitive to relative humidity. Medium-sized droplets experience delayed evaporation at high relative humidity, and increase airborne lifetime and travel distance. By contrast, at low relative humidity, medium-sized droplets quickly shrink to droplet nuclei and follow the cough jet. Virus inactivation within a few hours generally occurs at temperatures above 40°C, and the presence of viral particles in aerosols impedes droplet evaporation.

Identification of the CCL2 PI3K/Akt axis involved in autophagy and apoptosis after spinal cord injury.

Spinal cord injury (SCI) is a devastating neurological disease with no cure that usually results in irreversible loss of sensory and voluntary motor functions below the injury site. We conducted an in-depth bioinformatics analysis combining the gene expression omnibus spinal cord injury database and the autophagy database and found that the expression of the autophagy gene CCL2 was significantly upregulated and the PI3K/Akt/mTOR signaling pathway was activated after SCI. The results of the bioinformatics analysis were verified by constructing animal and cellular models of SCI. We then used small interfering RNA to inhibit the expression of CCL2 and PI3K to inhibit and activate the PI3K/Akt/mTOR signaling pathway; western blot, immunofluorescence, monodansylcadaverine, and cell flow techniques were used to detect the expression of key proteins involved in downstream autophagy and apoptosis. We found that when PI3K inhibitors were activated, apoptosis decreased, the levels of autophagy-positive proteins LC3-I/LC3-II and Bcl-1 increased, the levels of autophagy-negative protein P62 decreased, the levels of pro-apoptotic proteins Bax and caspase-3 decreased, the levels of the apoptosis-inhibiting protein Bcl-2 increased. In contrast, when a PI3K activator was used, autophagy was inhibited, and apoptosis was increased. This study revealed the effect of CCL2 on autophagy and apoptosis after SCI through the PI3K/Akt/mTOR signaling pathway. By blocking the expression of the autophagy-related gene CCL2, the autophagic protective response can be activated, and apoptosis can be inhibited, which may be a promising strategy for the treatment of SCI.

Efficient dissipation-based nonlinear solver for multiphase flow in discrete fractured media

Solving nonlinear equation systems resulting from the Fully Implicit Method (FIM) remains a challenge for numerical simulation of multiphase flow in subsurface fractured media. The Courant numbers can vary orders of magnitude across discrete fracture-matrix (DFM) models because of the high contrasts in the permeability and length-scale between matrix and fracture. The standard Newton solver is usually unable to converge for big timestep sizes.Limited research has been conducted on nonlinear solver techniques for multiphase flow and transport in fractured media. We make an extension of a new dissipation-based continuation (DBC) method to DFM models. Our goal is to prevent timestep cuts and maintain efficient time-stepping for FIM. The DBC algorithm builds a homotopy of the discretized conservation equations through the addition of numerical dissipation terms. We introduce a continuation parameter for controlling the dissipation and ensuring that accuracy of the computed solution will not be reduced. Under the nonlinear framework of DBC, general dissipation operators and adaptive strategies are developed to provide the optimal dissipation matrix for the multiphase hyperbolic system.We assess the new nonlinear solver using multiple numerical examples. Results reveal that the damped-Newton solver suffers from serious restrictions on timestep sizes and wasted iterations. In contrast, the DBC solver provides excellent computational performance. The dissipation operators are able to successfully resolve main convergence difficulties. We also investigate the impact of star-delta transformation which removes the small cells at fracture intersections. Moreover, we demonstrate that an aggressive time-stepping does not affect the solution accuracy.

An An Optimal Placement of STATCOM Controller on 14-Bus IEEE Standard Test Transmission Network Using Particle Swarm Optimization

The bulk transport of electrical energy from a generating station to an electrical substation is facilitated by transmission network. Transmission network suffered from unavoidable power loss and there is need to minimize the loss so as to improve the overall efficiency of the network. By strategically placing Static Synchronous Compensator (STATCOM) controller into appropriate location, power loss can be reduced. Factually, one of the most crucial Flexible Alternating Current Transmissions (FACTS) device that control electrical power and increase system power transfer capability is considered as STATCOM. STATCOM is typically installed on the transmission network to reduce power losses and improve the voltage profile of the system. To significantly reduce power losses and improve the system voltage profile, which will ensure the security and dependability of the power system, this research looks into the optimal placement of STATCOM controller on transmission system using Particle Swarm Optimization (PSO) technique. A steady state mathematical model of STATCOM-based Power Injection Mode (PIM) through voltage source representation was derived. The mathematical model was implemented into Newton-Raphson (NR) load flow and PSO technique was used to optimize the exact location of STATCOM in objective to minimize power losses, improve voltage magnitude profile and provide adequate voltage stability to the power system. The voltage magnitude, active and reactive power loss were achieved for load flow solution on 14-bus IEEE test system using Power System Analysis Toolbox (PSAT) in MATLAB Simulink. Results revealed that, all the terminal voltages are within the voltage limits of 1.040 p.u and 1.035 p.u., respectively. The total active and reactive power losses were able to reduce by 6.90 % and 9.13 % respectively. The STATCOM based PSO optimal placement gave a stronger compensatory impact because it helped to reduce power losses and improved the overall network STATCOM-based profile.

Solution kinetics analysis of vesicle trafficking of zymogen granule.

Zymogen granule (ZG) is a micrometer size enzymatic vesicle in the exocrine pancreas. It regulates secretion of digestive enzymes. It is also associated with pancreatic disease and disorder. It has several myosin classes molecular motors such as 1c, 5c, 6 and 7b. These myosin motors are responsible for transportation of ZG. The hydrolysis of adenosine triphosphate (ATP) initiates the directional motion of these myosin motors. To understand what the key part of the kinetics step for the ZG's trafficking, we measured each step of ATPase of purified ZGs from the rat pancreas. In the present study, we employed the stop flow techniques in order to investigate step(s) of ATPase cycle, e.g. ATP binding, ADP dissociation, phosphate release, and actin binding rate. The data were observed as ensemble work of myosins. The results provide the kinetics of these molecular motors complex present in the ZG. For ATP binding, mant-dATP was used, and result was fit with a single exponential with about 200 (/s) rate. For ADP dissociation, mant-dADP was used with double mixing methods. The fast and slow rates of ADP dissociation are 120 /s and 28 /s, respectively. For actin binding rate, Pyrene-actin was used. The fitting curves were found to be double exponential rates. The fast and slow rates are 64 /s and 12 /s, respectively. For the phosphate release, Phosphate binding protein was used and was observed to be double exponential, and the fast and slow rates are 250 /s and 8/s, respectively. Comparing only Myosin 5c, slow rate part of ADP dissociation and phosphate dissociation shows a little more significance, while ATP binding rate is not different.

Graph Neural Networks for Cross-Camera Data Association

Cross-camera image data association is essential for many multi-camera computer vision tasks, such as multi-camera pedestrian detection, multi-camera multi-target tracking, 3D pose estimation, etc. This association task is typically modeled as a bipartite graph matching problem and often solved by applying minimum-cost flow techniques, which may be computationally demanding for large data. Furthermore, cameras are usually treated by pairs, obtaining local solutions, rather than finding a global solution at once for all multiple cameras. Other key issue is that of the affinity function: the widespread usage of non-learnable pre-defined distances, such as the Euclidean and Cosine ones. This paper proposes an effective approach for cross-camera data-association focused on a global solution, instead of processing cameras by pairs. To avoid the usage of fixed distances and thresholds, we leverage the connectivity of Graph Neural Networks, previously unused in this scope, using a Message Passing Network to jointly learn features and similarity functions. We validate the proposal for pedestrian cross-camera association, showing results over the EPFL multi-camera pedestrian dataset. Our approach considerably outperforms the literature data association techniques, without requiring to be trained in the same scenario in which it is tested. Our code is available at <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><uri>https://www-vpu.eps.uam.es/publications/gnn</uri></monospace>

Comparative analysis of IoT-based controlled environment and uncontrolled environment plant growth monitoring system for hydroponic indoor vertical farm

Agriculture is a very important economic sector that contributes to a nation's overall economic development. According to the UN Food and Agriculture Organization, with a rising population, the cereals demand will reach 3 billion tons by 2050. Also, the mission of Sustainable Development Goals (SDGs) is to provide zero hunger and sustainable agriculture by 2030. With a simultaneous decline in cultivable land and water scarcity, food production has to increase in order to achieve the above mission. Vertical farming is a current state of art agriculture technology to increase crop yield per unit area. This work focuses on designing and constructing an IoT-enabled smart vertical farming system with a controlled environment for plant growth. This system uses the hydroponic Deep Flow Technique (DFT), various sensors, and an auto pH and Total Dissolved Solids (TDS) balancing system. This paper provides a comparative analysis of IoT-based controlled environment vertical farming setup with the uncontrolled setup for Romaine lettuce in terms of plant growth parameters like plant height, maximum leaf length, maximum leaf width, and fresh and dry weight of the plant. The observed fresh weight of the aerial part for automated and unautomated setup is found to be 58.66 g and 48.81 g respectively. Also, the chemical analysis showed the plants possess the required optimum range of micro and macronutrients for both setups. The macronutrient results obtained for the controlled/automated (A setup), and uncontrolled/unautomated setup (U setup for Phosphorus (P), and Potassium (K) are (PA, PU) (5.91 g/Kg, 6.06 g/Kg), and (KA, KU) (67.03 g/Kg, 74.01 g/Kg) respectively.

Energy optimization of multi-carrier energy systems to achieve a low carbon community

One of the most prominent concerns of human societies, especially in advanced countries, is how to manage and consume energy in a manner that not only energy demand would be supplied but also a minimum amount of pollution enters the environment. In this case, production and consumption resources of energy and carbon dioxides emission sources have to be identified. Optimizing the consumption schedule can reduce fossil fuel consumption and carbon dioxide emission. This project aims to design and plan a multi-energy carrier microgrid that facilitates reaching a low carbon society. In this manner, utilizing modeling techniques of carbon dioxide flow in energy systems offers a good estimation for the amount of carbon distributed in energy systems. This first step is one of the most fundamental aspects of implementing optimization studies with the purpose of carbon reduction. A proper optimization procedure for multiple energy carrier systems at the distribution system level is recommended by proposing suitable and comprehensive decision variables. The optimization studies, which are based on carbon flow in the distribution network, can assist the decision-makers of the energy field in evaluating the effectiveness of various solutions.

Seepage failure prediction of breakwater using an unresolved ISPH-DEM coupling method enriched with Terzaghi’s critical hydraulic gradient

This study develops a new numerical simulation model for rubble mound failure prediction caused by piping destruction under seepage flows. The piping has been pointed out as a significant cause of breakwater failure during tsunamis. Once boiling and heaving occur on the mound surface, the piping suddenly propagates in the opposite direction of seepage flow. For the seepage failure prediction, a coupled fluid-soil-structure simulator is developed by combining the ISPH for fluid and the DEM for rubble mounds and caisson blocks. The ISPH, a Lagrangian particle method for incompressible fluids, can simulate seepage and violent flows such as tsunamis. The DEM has been applied for discrete particle and rigid body simulations that include discontinuous deformation, as in the rubble mounds failure and large displacement of the caisson block. ISPH-DEM coupling simulations have already been proposed as a technique for multi-phase flows. Still, the technique cannot reproduce the sudden onset of piping from a stable mound. Two simple assumptions are applied to reduce the numerical cost for the fluid-soil-structure simulators of a breakwater structure composed of a rubble mound and the caisson block. Firstly, each rubble is modeled as an idealized spherical DEM particle with the mean diameter of the rubble. The ISPH particle size is assumed to be the same size as the DEM particle. Under these assumptions, the unresolved coupling model between rubble mound particles and fluid, which obtains the interaction through empirical drag force, should be applied. At the same time, the interaction between the fluid and the caisson block is fully resolved with the spatial resolution with the ISPH and DEM particle size. Our new contribution in this paper is how to model the interaction as an unresolved coupling between seepage flow simulated by ISPH and rubble mound particle modeled with DEM. Our original seepage failure experiment is simulated using the proposed ISPH-DEM coupling simulator. We identified the conventional drag force models as the unresolved coupling model are insufficient to initiate the boiling and piping observed in the experiment. It may be due in one part to excessive averaging of flow velocities caused by unresolved coupling. Therefore, Terzaghi’s critical hydraulic gradient is introduced to initiate the boiling and heaving. Unstable DEM particles, judged by Terzaghi’s critical hydraulic gradient, gradually lose their mass to represent unresolved suspended fine rubble mound particles. Our models qualitatively reproduce the sand boiling and backward erosion in the opposite direction of the seepage flow, as shown in the experiment.

Quantification of paravalvular leaks associated with TAVI implants using 4D MRI in an aortic root phantom made possible by the use of 3D printing.

Transcatheter aortic valve implantation (TAVI) has become an alternative to surgical replacement of the aortic valve elderly patients. However, TAVI patients may suffer from paravalvular leaks (PVL). Detecting and grading is usually done by echocardiography, but is limited by resolution, 2D visualization and operator dependency. 4D flow magnetic resonance imaging (MRI) is a promising alternative, which did not reach clinical application in TAVI patients. The aim of this study was applying 3D printing technologies in order to evaluate flow patterns and hemodynamics of PVLs following TAVI, exploiting 4D flow MRI and standard ultrasound. An MR-compatible, anatomically left ventricle, aortic root, and ascending aorta model was fabricated by combining 3D-printed parts and various soft silicone materials to match physiological characteristics. An Abbott Portico™ valve was used in continuous antegrade flow (12-22 l/min), retrograde flow with varying transvalvular pressures (60-110 mmHg), and physiological pulsatile hemodynamics (aortic pressure: 120/80 mmHg, cardiac output: 5 l/min) Time-resolved MR measurements were performed above and below the TAVI stent and compared with color Doppler ultrasound measurements in exactly the same setup. The continuous antegrade flow measurements from MRI largely agreed with the flowmeter measurements, and a maximum error of only 7% was observed. In the retrograde configuration, visualization of the paravalvular leaks was possible from the MR measurements, but flow was overestimated by up to 33%. The 4D MRI measurement in the pulsatile setup revealed a single main PVL, which was also confirmed by the color Doppler measurements, and velocities were similar (2.0 m/s vs. 1.7 m/s). 4D MRI techniques were used to qualitatively assess flow in a patient-specific, MR-compatible and flexible model, which only became possible through the use of 3D printing techniques. Flow patterns in the ascending aorta, identification and quantification of PVLs was possible and the location and extent of PVLs were confirmed by ultrasound measurements. The 4D MRI flow technique allowed evaluation of flow patterns in the ascending aorta and the left ventricle below the TAVI stent with good results in identifying PVLs, demonstrating its capabilities over ultrasound by providing the ability to visualize the paravalvular jets in three dimensions at however, additional expenditure of time and money.