Leakage Conditions Research Articles

Methods and Initial Findings of the Observational Study of Ostomy Consumers Registry

BACKGROUND: Longitudinal patient-reported data are limited for people living with an ostomy. PURPOSE: The Observational Study of Ostomy Consumers (OSO) registry collects data from individuals living with an ostomy. METHODS: Self-reported data were collected via repeated electronic surveys completed by adults living with an ostomy in the United States (US) and United Kingdom (UK). RESULTS: Baseline data, collected between October 2018 and February 2021, were available for 299 individuals from the US (77.9%) and UK (22.1%). Most participants reported using a 2-piece pouching system (55.5%) rather than a 1-piece system (44.4%), and using a convex barrier (54.4%) or flat barrier (44.3%). The most commonly reported barrier wear times were ≥ 3 to < 4 days for 36.6% of participants in the US and ≥ 1 to < 2 days for 42.4% of participants in the UK. Despite self-reports of leakage and peristomal skin conditions, quality of life remained positive. A majority of participants (92.0%) reported being very satisfied or somewhat satisfied with their ostomy product. CONCLUSION: The Observational Study of Ostomy Consumers registry allows for a continuous data stream that can be mined to help understand the patient experience over time. Understanding patient experiences will help to inform clinical practice and research, health care utilization, and product innovation.

Torque Characteristics Investigation of a Flux-Controllable Permanent Magnet Motor Considering Different Flux-Leakage Operation Conditions

In this article, a flux-controllable permanent magnet (FC-PM) motor has been designed and optimized to achieve the optimal torque component distribution under different flux leakage conditions. The FC-PM motor can realize flexible adjustment and control of air-gap magnetic field by a reasonable flux bridge design, which further broadens the speed range of the motor effectively. The focus of the research is to analyze and optimize the torque characteristics in combination with typical operation conditions. First, a motor topology is proposed and then the mechanism of flux leakage and the distribution of torque characteristics are studied. Then, according to different flux leakage operation conditions, an optimization method considering the torque component distribution is newly proposed. Based on the proposed optimization method, the optimal motor can be obtained. Finally, the performance of the optimal motor is analyzed and compared with the initial motor. The comparison results indicate that, based on the proposed optimization method, the torque output capability is improved effectively, while the desired leakage flux adjustment range is maintained.

Feasibility study of β-ray detection system for small leakage from reactor coolant system

Because existing reactant coolant system (RCS) leakage detection mechanisms are insensitive to small leaks, a real-time, direct detection system with a detection threshold below 0.5 gpm∙hr−1 was studied. A beta-ray detection system using a silicon detector with good energy resolution for beta rays and a low gamma-ray response was proposed. The detection performance in the leakage condition was evaluated through experiments and simulations. The concentration of 16N in the coolant corresponding to a coolant leakage of 0.5 gpm was calculated using the analytic method and ORIGEN-ARP. Based on the concentration of 16N and the measurement of the silicon detector with 90Sr/90Y, the beta-ray count rate was estimated using MCNPX. To evaluate the effect of gamma rays inside the containment building, the signal-to-noise ratio (SNR) was calculated. To evaluate the count rate ratio, the radiation field inside the containment building was simulated using MCNPX, and response evaluation experiments were performed using beta and gamma rays on the silicon detector. The expected beta-ray count rate at 0.5 gpm leakage was 7.26 × 105 counts/sec, and the signal-to-background count rate ratio exceeded 88 for a transport time of 10 s, demonstrating its suitability for operation inside a reactor containment building.

Magnetic Field Leakage Cancellation in Multiple-Input Multiple-Output Wireless Power Transfer Systems

This letter presents a method to cancel magnetic field leakage while maintaining high power transfer efficiency (PTE) in multiple-input multiple-output wireless power transfer systems. To achieve low leakage and high PTE, the problem of PTE maximization under the cancellation condition of the magnetic field leakage is solved, which is a maximization problem of affinely constrained Rayleigh quotients. Subsequently, the optimal amplitudes and phases of the input voltages at the transmitter array and the optimal load impedances at the receivers are shown. This method can further reduce the magnetic field leakage by up to 31 dB compared with the PTE maximization method, while having a PTE drop within 5.5%.

Modeling of a bulk material stress state in a conical hopper hole under vibration action

A model of a fine-grained bulk material stress state in a conical hopper hole under vibration during material unloading is proposed. For the study, it is used a model of a discrete medium based on a balance of a small thickness elementary volume, in which the stress redistribution occurs by opening an outlet at the hopper bottom. A formula for determining the material radial stress in the discharge conical hopper hole is obtained, taking into account all force factors influencing the bulk material behavior. The influence of humidity, shape and geometric parameters of the hopper, and vibration intensity on the change of the bulk material stress state is investigated. As a result, the efficiency of vibration to improve the conditions of the fine-grained bulk material leakage from the hoppers is established.

Advanced monitoring and simulation for underground gas storage risk management

It is crucial to ensure the safety and integrity of underground gas storage (UGS) infrastructure for energy reliability in California, and many other places around the world. To address the risk management need in UGS industry, we take advantage of recent advances in downhole fiber optic monitoring and coupled well-reservoir simulation to provide unprecedented understanding of gas flow in wells at UGS sites. We have combined advanced monitoring and simulation of UGS operations into a decision-support system called the Integrated Risk Management and Decision Support System (IRMDSS). The IRMDSS framework includes three components: (i) mechanistic models, (ii) continuous and frequent monitoring data, and (iii) a supervisory interface for performing analyses using the models and monitoring data. The goal of the IRMDSS is to equip UGS operators with real-time monitoring data and simulation tools that can alert them to potential failures, detect early leakage, and support mitigation decision-making to prevent otherwise larger failures. We demonstrate an application of the IRMDSS by analyzing the temperature and pressure response to a hypothetical leak. Through a review of distributed temperature sensing (DTS) data collected at an operating UGS facility we show that DTS can uniquely and precisely identify the depth of the gas-water-contact in the well annulus, and that DTS can provide an early warning signal of upward gas flow as would occur in a well blowout scenario. When combined with modeling analysis, a rough leak rate can be roughly estimated to understand the severity of the leakage conditions and to support the mitigation decision needed. • Mechanistic models simulate system responses to UGS facility operations. • Advanced monitoring technologies provide continuous update status for UGS facilities. • DTS data can provide real-time well leakage detection and identify leaky location. • Combination of models and advanced monitoring benefits UGS risk management.

Tribological properties of rock-bit journal bearings tribo-pair with micro-dimples under contaminated grease lubrication

The failure of bit bearings is one of the main factors that restrict the life of a bit, and studies revealed that sealing and adhesive wear failure are common failure modes of bit bearings. To study the adaptability of the optimal textures to anti-wear and anti-friction performance of an 8 1/2” rock bit journal bearingunder sealing leakage conditions, the rheological properties of grease in the range of 0%–50% of the water-based drilling fluid volume in the compound lithium-based grease (rock bit grease) were tested. For the cylindrical dimples (diameter: 300 μm; depth: 40 μm, area ratio: 5%) and elliptical dimples (major and minor axis of 720 and 360 μm, respectively; depth: 40 μm, area ratio: 10%), the experimental study on the wear resistance of the optimized texture tribo-pairs was carried out as the grease invaded different drilling fluid contents based on the pin-disk pair. Results showed that the drilling fluid volume in the rock bit grease significantly affected the rheological properties of the grease. Furthermore, the cylindrical and elliptical dimples still had good anti-wear and anti-friction effects. Especially when the drilling fluid volume ratios in the grease reached 50%, the elliptical dimples can still reduce the friction coefficient and wear amount of the pair by 19.88% and 56.99%, respectively. With the increase of drilling fluid invasion into grease, the wear morphology of the un-textured tribo-pairs showed that the wear form changed from abrasive to adhesive wear, while that of the preferred textured tribo-pairs indicated slight abrasive wear.

A Novel Pipeline Leak Detection Technique Based on Acoustic Emission Features and Two-Sample Kolmogorov-Smirnov Test.

Pipeline leakage remains a challenge in various industries. Acoustic emission (AE) technology has recently shown great potential for leak diagnosis. Many AE features, such as root mean square (RMS), peak value, standard deviation, mean value, and entropy, have been suggested to detect leaks. However, background noise in AE signals makes these features ineffective. The present paper proposes a pipeline leak detection technique based on acoustic emission event (AEE) features and a Kolmogorov–Smirnov (KS) test. The AEE features, namely, peak amplitude, energy, rise-time, decay time, and counts, are inherent properties of AE signals and therefore more suitable for recognizing leak attributes. Surprisingly, the AEE features have received negligible attention. According to the proposed technique, the AEE features are first extracted from the AE signals. For this purpose, a sliding window was used with an adaptive threshold so that the properties of both burst- and continuous-type emissions can be retained. The AEE features form distribution that change its shape when the pipeline condition changes from normal to leakage. The AEE feature distributions for leak and healthy conditions were discriminated using the two-sample KS test, and a pipeline leak indicator (PLI) was obtained. The experimental results demonstrate that the developed PLI accurately distinguishes the leak and no-leak conditions without any prior leak information and it performs better than the traditional features such as mean, variance, RMS, and kurtosis.

An inverse problem for Bingham type fluids

In this paper we study a coefficient identification problem described by an elliptic variational-hemivariational inequality with unilateral constraints. The inequality is the weak formulation of the mathematical model of a stationary incompressible flow of Bingham type in a bounded domain. The unknown coefficient is a generalized viscosity function of the fluid. The boundary conditions represent generalizations of the no leak condition and a multivalued and nonmonotone version of a nonlinear Navier–Fujita frictional slip condition. The result on well posedness of the direct problem is established based on the theory of multivalued pseudomonotone operators. The existence to the inverse problem is proved by a Weierstrass type argument.

Analysis of Stokes system with solution-dependent subdifferential boundary conditions

We study the Stokes problem for the incompressible fluid with mixed nonlinear boundary conditions of subdifferential type. The latter involve a unilateral boundary condition, the Navier slip condition, a nonmonotone version of the nonlinear Navier–Fujita slip condition, and the threshold slip and leak condition of frictional type. The weak form of the problem leads to a new class of variational–hemivariational inequalities on convex sets for the velocity field. Solution existence and the weak compactness of the solution set to the inequality problem are established based on the Schauder fixed point theorem.

A study of the influencing factors of DNAPLs transport in stochastic network fractures

The migration and distribution of dense non-aqueous phase liquid (DNAPLs) in fractured media are affected by many factors, including the physical and chemical properties of DNAPLs, geometric and chemical properties of fractures and leakage conditions. Previous studies were mostly conducted in a single fracture, while the migration of DNAPLs in stochastic network fracture were seldom examined. In this paper, stochastic network fractures are generated using the Monte Carlo method. The coordinates and apertures of fractures are identified and output by pixel scanning. The migration of Perchloroethylene (PCE) in the stochastic network fractures is simulated by PetraSim. The effects of the geometric properties of the fractures and the leakage conditions (including the leakage rate and the leakage location) on the migration of DNAPLs are discussed. The numerical simulation results show that the spatial variability of fractures also affects the migration path and spatial distribution of DNAPLs. With the increase of spatial variability of fracture width in the network fractures, dominant channels appear, the migration rate of DNAPLs accelerates, and the location of DNAPLs centroid and the spatial distribution of saturation change significantly. The leakage rate affects the migration range and spatial distribution of DNAPLs. The higher the leakage rate is, the faster the migration rate of DNAPLs will be. The more saturated the accumulated DNAPLs at the bottom of the model, the greater the spatial distribution of DNAPLs is. In the same network fracture, different leakage locations will also lead to different migration paths and distribution ranges of DNAPLs. The spatial variability of fractures in the gravity direction at different leakage locations is different, leading to different migration paths and spatial distribution of DNAPLs.

Research on oil-gas Pipeline Leakage Detection Method Based on Particle Swarm Optimization Algorithm Optimized Support Vector Machine

In order to solve the problem of low accuracy in oil-gas pipeline leak detection, a pipeline leak detection method based on Particle Swarm Optimization (PSO) algorithm optimized Support Vector Machine (SVM) is introduced. This method uses PSO to solve the penalty factor ‘c’ and kernel function parameter ‘g’, and constructs the pipeline leakage detection model of SVM. We set up an experimental platform to collect negative pressure wave signals under different working conditions. After wavelet domain denoising and data preprocessing, four eigenvalues of Mean, Standard Deviation, Kurtosis and Skewness are extracted from the signals to form the eigenvector samples, which are taken as input of SVM, and four working conditions of normal, leakage, rise and fall are taken as output. Through experimental verification, the comprehensive performance of PSO-SVM algorithm is better than that of traditional SVM, Genetic Algorithm optimized SVM and grid search algorithm optimized SVM. The POS-SVM algorithm can be applied to the leak detection of oil-gas pipeline.

IoT Based LPG Gas Level Detection & Gas Leakage Accident Prevention with Alert System

Liquefied Petroleum Gas (LPG) is widely used for cooking fuel in developing countries for economic reasons, for energy-rich fuel source that contains the higher calorific value, for clean fuel with low carbon emission and for a portable that is available in even the faraway of areas. Therefore the proposed gas leakage detection and monitoring system for the gasoline content present in household LPG cylinder is developed. Usually, the capacity of LPG in cylinder is not determined in an exact manner and a cylinder when the gas is about to empty will be a difficult situation for the one who uses LPG gas for cooking continuously. By using IoT, the information of the near to the empty level of LPG gas in the cylinder will send to the user and the gas refill method by using telephonic ordering can be conducted. The purpose of this research is the detection of gas leakage and monitoring the LPG gas cylinder weight regularly to know the remaining value of gas in the cylinder. When the gas leakage is sensed, the warning signal and alarm sound will be active and also switch on exhaust fan automatically to decrease the gas concentration. The weight of LPG is measured using the load sensor (SEN-10245) and the output of the sensor is connected with Arduino MKR Wifi 1010 microcontroller. The user can know the validity of LPG usage daily because the amount of LPG gas will publish as events and watch them come through in real-time using the Wia IoT cloud platform. Consequently, the user is alerted by giving SMS notification to their mobile phone when the LPG level is critically low (below 20%) by use of the integration function of the Wia IoT platform call Twilio, using its web service APIs. Then by detecting the gas leakage with MQ6 gas sensor, this research work indicates for gas leakage condition and also helps to prevent the LPG gas burst accidents in the home.

Analysis of the LNAPL Migration Process in the Vadose Zone under Two Different Media Conditions.

This study focused on the processes of free infiltration, precipitation displacement, and natural attenuation of the LNAPL under the condition of near-surface leakage. Sandbox experiments were performed to explore the migration characteristics of LNAPL in the vadose zone with two media structures and the influences of the soil interface on the migration of LNAPL. The results indicate that the vertical migration velocity of the LNAPL infiltration front in medium and coarse sand was 1 order of magnitude higher than that in fine sand and that the LNAPL accumulated at the coarse–fine interface, which acted as the capillary barrier. Displacement of precipitation for LNAPL had little relationship with rainfall intensity and was obviously affected by medium particle size, where coarse sand (40.78%) > medium sand (20.5%) > fine sand (10%). The natural attenuation rate of the LNAPL in the vadose zone was related to the water content of the media; the natural attenuation rate of fine sand was higher. This study simulated the process of the LNAPL leakage from the near surface into the layered heterogeneous stratum, improved the understanding of the migration of the LNAPL under different stratum conditions, and can provide support for the treatment of LNAPL leakage events in the actual site.

Deeppipe: Theory-guided LSTM method for monitoring pressure after multi-product pipeline shutdown

The pressure changes dramatically during the shutdown process of the multi-product pipeline. When the pipeline pressure comes to decrease, it is often mistaken as pipeline leakage or other abnormal condition which increases the burden of the operator on-site. At present, the method of pipeline shutdown pressure analysis is mainly based on numerical simulation which can not monitor shutdown pressure in real-time. In this work, the time-series approximate ability of long short-term memory (LSTM) is taken advantage of to construct a shutdown pressure prediction model. To overcome the drawback of this deep learning algorithm that is trained only by ample data, the scientific principle and theory are integrated into LSTM. Subsequently, the theory-guided long short-term memory (TG-LSTM) is proposed for pipeline shutdown pressure prediction. The proposed model is trained with available data and simultaneously guided by the theory (physical principle and engineering theory) of the underlying problem. In the training process, the data mismatch, as well as monotonicity constraints, and boundary constraints are coupled into loss function. After acquiring the parameters of the neural network, a TG-LSTM model is established which not only fits the data, but also follows the physical principle and the engineering theory. The proposed model is verified by three real-world multi-product pipelines. The results indicate that TG-LSTM achieves better accuracy than other prediction models, with MAPE being 0.246%, 0.186%, and 0.143%, respectively. Finally, the sensitivity analysis of different hyper-parameter is conducted to illustrate the robustness of TG-LSTM in pipeline shutdown pressure prediction.

Vitrectomy and Vitreoretinal Surgery in Persistent Macular Edema

Macular edema can occur as a result of many ocular diseases and systemic diseases. Edema is associated with vascular leaking conditions. Diabetic retinopathy, retinal vein occlusion, posterior segment inflammatory diseases, and intraocular surgery are the most common causes. There are many treatment options in the treatment of edema. Despite medical and laser treatment, there may be persistent cases. Some of these cases can benefit from vitrectomy and vitreoretinal surgery. This review will focus on the place and applications of vitrectomy and vitreoretinal surgery in persistent macular edema.

A new class of elliptic quasi-variational-hemivariational inequalities for fluid flow with mixed boundary conditions

In this paper we study a class of quasi-variational-hemivariational inequalities in reflexive Banach spaces. The inequalities contain a convex potential, a locally Lipschitz superpotential, and a implicit obstacle set of constraints. Solution existence and compactness of the solution set to the inequality problem are established based on the Kakutani–Ky Fan fixed point theorem. The applicability of the results is illustrated by the steady-state Oseen model of a generalized Newtonian incompressible fluid with mixed boundary conditions. The latter involve a unilateral boundary condition, the Navier slip condition, a nonmonotone version of the nonlinear Navier-Fujita slip condition, and a generalization of the threshold slip and leak condition of frictional type.

Experimental study on water pipeline leak using In-Pipe acoustic signal analysis and artificial neural network prediction

Water pipeline leakage is a common and significant global problem. In-pipe inspection based on hydrophone is one of the most direct, accurate, and reliable solutions for leak detection and recognition. In this study, a scheme of in-pipe detector was designed to pick up and identify acoustic signal due to leak. To investigate the characteristic of acoustic signal, an experimental platform was built to simulate the leaks and obtain acoustic signals under different leak conditions in an industrial scale water pipeline. Because a decreased pressure as leak has an unstable fluctuation in time domain, the frequency composition of the signal was analyzed in frequency domain, and then the change of frequency amplitude can be referenced to recognize the leaks. Moreover, the effects of leak size, pipeline pressure, and water flow rate on the characteristic of acoustic signal were investigated. The results show that the signal’s intensity under leak conditions are significantly higher than that of no leak case, and it will increase as the increased leak size; the signal intensity under no leak case will increase with the growth of pipeline pressure; the flow velocity has little effect on the signal intensity. To increase the recognition accuracy, an artificial neural network model was developed for the leak prediction, and 18 cases through additional tests were selected to validate the accuracy of model. Comparing experimental and prediction results, maximum relative error is within 10.0%. It indicates that the prediction model has a reasonable accuracy for the leak recognition.

An innovative machine learning based framework for water distribution network leakage detection and localization

Leakages in the underground water distribution networks (WDNs) waste over 1 billion gallon of water annually in the US and cause significant socio-economic loss to our communities. However, detecting and localization leakage in a WDN remains a challenging technical problem despite of significant progresses in this domain. The progresses in machine learning (ML) provides new ways to identify the leakage by data-driven methods. However, in-service WDNs are short of labeled data under leaking conditions, which makes it infeasible to use common ML models. This study proposed a novel machine learning (ML)-based framework for WDN leak detection and localization. This new framework, named clustering-then-localization semi-supervised learning (CtL-SSL), uses the topological relationship of WDN and its leakage characteristics for WDN partition and sensors placement, and subsequently utilizes the monitoring data for leakage detection and leakage localization. The CtL-SSL framework is applied to two testbed WDNs and achieves 95% leakage detection accuracy and around 83% final leakage localization accuracy by use of unbalanced data with less than 10% leaking data. The developed CtL-SSL framework advances the leak detection strategy by alleviating the data requirements, guiding optimal sensor placement, and locating leakage via WDN leakage zone partition. It features excellent scalability, extensibility, and upgradeability for applications to various types of WDNs. It will provide valuable a tool in sustainable management of the WDNs.

Hydraulic performance benchmarking for effective management of water distribution networks: An innovative composite index-based approach

Hydraulic performance assessment and benchmarking of water distribution networks (WDNs) impose a major challenge to water utilities worldwide. Presently, benchmarking strategies for WDNs are not fully developed, especially for analyzing intermittent systems commonly encountered in non-developed nations. To overcome these limitations, this paper proposes an index-based benchmarking strategy for WDNs, comparing their actual hydraulic performance and expected serviceability. A robust Hydraulic Performance Index (HPI) is developed as a global metric to account for the combined impact of multiple hydraulic outputs, concerning their benchmark values. The applicability of this index is verified on a numerical benchmark network, and its usefulness is demonstrated on a real-world intermittent WDN located in Kolkata (India) by coupling the HPI-based framework with hydraulic models using the EPANET-MATLAB programmer's toolkit. A scenario-based analysis is conducted using extended-period simulation to obtain the HPI for diverse service levels and leakage conditions of the WDN models. The HPI is designed to effectively capture the localized pressure reduction during peak flow, prioritize hydraulic outputs based on regional constraints, and penalize systems with unsustainably high hydraulic output. The developed strategy is also effective in performance benchmarking of WDNs of different nations with diverse serviceability and threshold parameters on a common platform. Finally, the practical efficacy and generalizability of the HPI-based results in the context of case-specific performance management of WDNs, along with limitations, recommendations and future perspectives are elucidated upon.