Leak Location Research Articles

Experimental and numerical investigation on soil erosion under different pipeline-leaking locations

In recent years, cave-in ground failures have become increasingly common worldwide. The leakage of defective pipelines buried below ground level has been identified as a significant contributor to these failures. This study utilized experimental seepage-erosion tests to characterize ground collapses resulting from pipeline leakage and examined the impacts of leakage locations on soil and water losses. Subsequently, discrete element method-finite difference method (DEM-FDM) coupling analyses were conducted to elucidate the mesoscopic mechanisms of pipeline leakage, including changes in seepage force, effective stress, and soil pressure on the pipeline during leakage. The findings include: (1) The locations of leaking spots on pipelines significantly influenced soil and water losses; (2) Leaking spots closer to the pipe bottom led to more severe ground collapses due to larger seepage forces; (3) Soil pressure exhibited decreasing trends near the leaking spot and increasing trends at other locations due to the soil arching effect. These insights provide valuable guidance for preventing pipeline leakages.

AN NEGATIVE PRESSURE WAVE METHOD OPTIMIZATION FOR GAS PIPELINE LEAK LOCATION

The article is devoted to the optimization of the negative pressure wave method, designed to determine the location of leaks from a gas pipeline.The proposed negative pressure wave method provides a more accurate determination of the leak location, because a group of readable equally spaced sensors is used. To determine the location of a leak, the sign of the appearance of a minimum of a certain functional is used, which contains, as an integrand, the product of two exponential functions that cha racterize the weakening of a negative pressure wave propagating from the location of the leak to the position of the selected left and right sensors.Calculation formulas are given for calculating the optimal distance between a displaced pair of readable sensors, and the form of the optimal functional relationship between the distances from the place of leakage to the position of the sensors is established.

Prediction of hydrogen leakage location and intensity in hydrogen refueling stations based on deep learning

The common risk of high-pressure hydrogen leakage seriously affects the safety of hydrogen refueling stations (HRS). In this study, we have developed a hybrid model (WD-KNN-CNN) based on wavelet denoising (WD) and deep learning to predict the hydrogen leakage location and intensity in HRS. The raw data were calculated using validated Computational Fluid Dynamics (CFD), and the WD-processed raw data were utilized as input data for the hybrid model. The K-nearest Neighbors (KNN) is employed to identify the leak location, while a two-dimensional Convolutional Neural Network (CNN) is utilized to extract temporal and spatial distribution features from the concentration sequence for intensity prediction. Bayesian optimization is applied to determine the optimal hyperparameter combination for the model. The results demonstrate that the model achieves a prediction accuracy of 99.14% for leak location and 97.42% for intensity level. Furthermore, through the reduction of sensor quantity, we tested the model's performance and found that utilizing 14 sensors as input resulted in 91.3% accuracy for leak location and 90.8% for intensity level prediction. This hybrid model provides a method for safety monitoring in HRS and offers guidance for personnel response during leak incidents.

Locating leaks in geomembrane-lined ponds using the electrical leak location method: Case histories

Results from seven years of electrical leak location methods applied to geomembrane-lined ponds to locate leaks are presented. Inspections were conducted on 195 projects designed with single (n = 74), double (n = 120), or triple (n = 1) liners. These projects are located in the U.S.A. (n = 159), Canada (n = 30), and Mexico (n = 11). All inspections were conducted on full ponds, which resulted in the detection and repair of 1230 leaks during the study period across an analyzed area of 322 ha. From 2015 to 2021, the average was 14 leaks/ha, with values ranging from 0 to 689 leaks/ha. The results reveal that larger inspected areas (greater than 2 ha) tend to have fewer leaks. 63% of projects had up to 5 leaks/ha. 15% of projects had more than 20 leaks/ha. The results of this study could influence landfill designers, operators, and environmental agencies in defining new practices for designing and operating geomembrane-lined ponds.

Graph Laplace Regularization-based pressure sensor placement strategy for leak localization in the water distribution networks under joint hydraulic and topological feature spaces

Urban water distribution networks (WDNs) have wide range and intricate topology, which include leakage, pipe burst and other abnormal states during production and operation. With the continuous development of the Internet of Things (IoT) technology in recent years, the means of monitoring the WDNs by using wireless sensor network technology has gradually received attention and extensive research. Most of the existing researches select the deployment location of sensors according to the hydraulic state of the WDNs, but the connectivity and topology between the nodes of the WDNs are not fully considered and analyzed. In this study, a new method that can integrate the topological features and hydraulic model information of the WDN is proposed to solve the problem of optimal sensor placement. First, the method preprocesses the covariance matrix of the pressure sensitivity matrix of the water distribution network by a diffusion kernel-based data prefiltering method and obtains the new network topology weights and its Laplacian matrix under the constraints of the network topology through a data-based graphical Laplacian learning method. Then, the sensor placement problem is transformed into a matrix minimum eigenvalue constraint problem by the Graph Laplace Regularization (GLR)-based method, and finally the selection of sensor nodes is accomplished by the method based on Gershgorin Disc Alignment (GDA). The proposed strategy is tested on a passive Hanoi network, an active Net 3 network, and a larger network, PA2, and is compared with some existing methods. The results show that the proposed solution achieves good performance in three different leak localization methods.

Safety Through Visibility: Tracing Hydrogen in Colors with Highly Customizable and Flexibly Applicable Supraparticle Additives

AbstractThe flammability of H2‐air mixtures demands timely detection and precise localization of H2 leakages to ensure safety and targeted maintenance measures in the anticipated hydrogen economy. Herein, H2 indicator supraparticles (SPs) are demonstrated that meet this demand by making H2 visible to the naked eye by a rapid (ir)reversible color change. Their toolbox‐like manufacturing from SiO2 nanoparticles (NPs), Pt NPs, and indicator dye molecules via spray‐drying allows for engineering their structure, texture, and detection performance by systematically tuning their composition. It is demonstrated that decreasing the SiO2 NP size, increasing the Pt NP concentration, and adjusting the amount of dye molecules improve the SPs’ response times. This opens up the option for multidimensional customization of their detection performance. Furthermore, the toolbox of H2 indicator SPs containing the established resazurin system is expanded to other indicator dyes, which makes a color change and reversibility of choice feasible. H2 indicator SPs meet many of the target characteristics for H2 detectors, e.g., low limit of detection, cycle stability, and high selectivity. Moreover, the SPs’ particulate nature allows for their flexible application as an additive, e.g., in coatings or clothing, which enables the detection and localization of H2 leakages in relevant application scenarios.

A remote detection method for leak location of external ports in a tokamak: Theory and preliminary experimental validation

The vacuum vessel of fusion devices, equipped with an array of external ports characterized by intricate flange connections and welding joints, inherently carries a notable risk of leakage. With the impending introduction of deuterium-tritium fusion reactions, restrictions on technician access necessitate a more innovative approach. This paper introduces a groundbreaking leak detection method for fusion device ports, leveraging the differential pumping effects of molecular screens. A robotic arm is utilized to transport a mass spectrometer equipped with a molecular screen into the tokamak for detecting leaks at external ports. Through theoretical calculations, simulations, and proof-of-principle experiments, our method demonstrates the feasibility in detecting and localizing leaks, offering a promising solution for leak detection in tritium-operated fusion devices.

A Pipeline Leak Localization Method Based on the EEMD-HT Algorithm and the Leakage-Grading Resolution Strategy

A Pipeline Leak Localization Method Based on the EEMD-HT Algorithm and the Leakage-Grading Resolution Strategy

The mesenteric entry site as a potential weak point in gastrointestinal anastomoses – findings from an ex-vivo biomechanical analysis

PurposeGastrointestinal disorders frequently necessitate surgery involving intestinal resection and anastomosis formation, potentially leading to severe complications like anastomotic leakage (AL) which is associated with increased morbidity, mortality, and adverse oncologic outcomes. While extensive research has explored the biology of anastomotic healing, there is limited understanding of the biomechanical properties of gastrointestinal anastomoses, which was aimed to be unraveled in this study.MethodsAn ex-vivo model was developed for the biomechanical analysis of 32 handsewn porcine end-to-end anastomoses, using interrupted and continuous suture techniques subjected to different flow models. While multiple cameras captured different angles of the anastomosis, comprehensive data recording of pressure, time, and temperature was performed simultaneously. Special focus was laid on monitoring time, location and pressure of anastomotic leakage (LP) and bursting pressures (BP) depending on suture techniques and flow models.ResultsSignificant differences in LP, BP, and time intervals were observed based on the flow model but not on the suture techniques applied. Interestingly, anastomoses at the insertion site of the mesentery exhibited significantly higher rates of leakage and bursting compared to other sections of the anastomosis.ConclusionThe developed ex-vivo model facilitated comparable, reproducible, and user-independent biomechanical analyses. Assessing biomechanical properties of anastomoses offers an advantage in identifying technical weak points to refine surgical techniques, potentially reducing complications like AL. The results indicate that mesenteric insertion serves as a potential weak spot for AL, warranting further investigations and refinements in surgical techniques to optimize outcomes in this critical area of anastomotic procedures.

Automated extraction of tunnel leakage location and area from 3D laser scanning point clouds

Water leakage is one of the common tunnel disease problems. Fast and accurate detection is crucial for timely management of leakage disease. This paper introduces a method for automatically extracting the location and area of tunnel leakage from 3D laser scanning point clouds. The method involves three key steps: segmenting the point cloud data along the tunnel axis based on the width of the tube sheet, correcting the intensity of the point cloud using a linear intensity correction model, and binarizing the point cloud data to extract the leakage area and calculate its area. To evaluate the performance of the proposed methodology, water leakage detection software is prepared based on the method, and tests are carried out in the urban subway tunnels in the leakage disease-producing sections. The results show a detection accuracy of 92 % and an improvement in detection efficiency of 7–8 times. The method proposed can be used to quickly and accurately extract the location and area of water leakage from point cloud data.

Development of an AI-based image/ultrasonic convergence camera system for accurate gas leak detection in petrochemical plants

Outdoor pipeline leaks are difficult to accurately measure using existing concentration measurement systems installed in petrochemical plants owing to external air currents. Besides, leak detection is only possible for a specific gas. The purpose of this study was to develop an image/ultrasonic convergence camera system that incorporates artificial intelligence (AI) to improve pipe leak detection and establish a real-time monitoring system. Our system includes an advanced ultrasonic camera coupled with a deep learning-based object-detection algorithm trained on pipe image data from petrochemical plants. The collected data improved the accuracy of detected gas leak localization through deep learning. Our detection model achieves an mAP50 (Mean average precision calculated at an intersection over union (IoU) threshold of 0.50)score of 0.45 on our data and is able to detect the majority of leak points within a system. The petrochemical plant environment was simulated by visiting petrochemical plants and reviewing drawings, and an outdoor experimental demonstration site was established. Scenarios such as flange connection failure were set under medium-/low-pressure conditions, and the developed product was experimented under gas leak conditions that simulated leakage accidents. These experiments enabled the removal of potentially confounding surrounding noise sources, which led to the false detection of actual gas leaks using the AI piping detection technique.

Multipoint leak localization for pipelines via acoustic wave amplitude ratio

Pipeline leak localization is significant for maintaining the regular operation of pipelines. This paper proposes a multipoint leak localization method for pipelines via the acoustic wave amplitude ratio, which addresses the issue of the traditional time delay estimation method (TDE) not being able to locate multiple leaks simultaneously. Firstly, a multipoint leak localization method for pipelines is established without time delay estimation and acoustic velocity. This method merely needs to determine the amplitude of the leakage acoustic wave propagating to both ends of the pipeline to achieve leakage localization. Secondly, the improved empirical wavelet transform (IEWT) method denoises the leakage acoustic wave, which can obtain an accurate acoustic amplitude to reduce the localization error. Finally, the trouble that the mutation factor and crossover probability cannot be adaptively selected in the differential evolution (DE) algorithm is improved, and the improved differential evolution (IDE) algorithm is used to solve the objective function to obtain the position of each leakage point. Field experiments show that the multipoint leak localization method proposed in this paper has perfect accuracy, and the minimum localization error is 11 m.

Prototipe Deteksi Letak Kebocoran Pipa dengan Optimalisasi Kinerja Penerimaan Paket LoRa menggunakan Pengkodean Parameter Fisik

The purpose of this research is to determine the effect of the physical coding of LoRa communications on monitoring water pipelines. Optimizing the performance of packet receivers in the LoRa communication system using coding on the physical parameters SF (spreading factor), BW (bandwidth), and CR (coding rate). The detection system consists of 3 sensor nodes, 3 intermediate nodes, and 1 receiver node. Data from these sensors is sent to a cloud database. The SX1278 LoRa communication module works using a 433 MHz frequency. During the transmission process on the LoRa communication system, optimization is carried out for receiving data packets using the parameter coding method of physical spread factors, bandwidth, and coding rate. As a result of the research, it is shown that the greater the value of the third parameter (SF, BW, and CR), such as improvement in packet reception performance, improvement in bit security, and increasing packet resistance to various disturbances in transmission, but the time required for sending data be longer. The optimal parameters for detecting pipe leak locations include SF 10, BW 500 KHz, and CR 4/8. The LoRa SX1278 scenario is optimal with a distance of 400 meters, where packet and byte reception are obtained 100%.

Hydrogen leakage source positioning method in deep belief network based on fully confined space Gaussian distribution model

Hydrogen leakage has become the biggest bottleneck restricting the development of hydrogen energy. Accurately locating the leakage is conducive to improving the hydrogen energy safety technology system. In this paper, a deep belief network pre-training and fine-tuning (DBN-PF) leakage source positioning coordinate method based on the fully confined space hydrogen Gaussian distribution model is proposed. Firstly, a fully confined space hydrogen Gaussian distribution model is established, and then a pre-training dataset is generated. The Gaussian-Gaussian Restricted Boltzmann Machine (GGRBM) in Deep Belief Network (DBN) is unsupervised pre-trained layer by layer, and the back propagation (BP) neural network in DBN is supervised pre-trained. Secondly, the hydrogen leakage experiment in the fully confined space was carried out, in which 8 hydrogen concentration sensors (HCSs) were placed on the top of the fully confined space, and the leakage experiment was carried out in sequence at 25 leakage locations. Finally, the HCS experimental data of one leakage location is extracted to fine-tune DBN. The experimental data at the other leakage locations were used to verify the positioning results. The results show that the positioning results average error of the proposed method is 20.62 mm. The average error accounts for 2.97% of the diagonal length of the closed model XY plane. Compared with BP neural network trained directly with the same fine-tuning dataset, the positioning error is reduced by at least 82.37%. Compared with the BP neural network with the same pre-training and fine tuning, the positioning error is reduced by at least 39.31%. The positioning technology developed in this study can achieve good hydrogen leak source location accuracy in a fully confined space only with the help of HCSs and a small amount of data.

Monitoring and risk assessment of Taoyuan ponds using an unmanned surface vehicle with multibeam echo sounder, ground-penetrating radar, and electrical resistivity tomography

Ponds are a unique agricultural irrigation method with a long history in the Taoyuan area. However, most of them lack accurate spatial information, such as water depth, water surface area, and effective water storage capacity. In this study, the water depth of pond was measured using an unmanned surface vehicle, which has the advantage of being able to operate in shallow waters and in areas that are inaccessible to manned boats. The results show that it can rapidly provide 3D point cloud and an underwater 50 cm gridded digital elevation model with an accuracy of 6.7 cm, which can be used to identify structural defects at the pond bed and the embankment and to accurately estimate the water surface area and effective water storage capacity. The nests with diameters of 30 to 70 cm dug by male tilapia to attract female tilapia for spawning can also be found. In addition, the ground-penetrating radar and electrical resistivity tomography were carried out to successfully investigate the electricity-related properties of the internal structure of the embankment and the leakage locations of pond. In conjunction with the maintenance experience of the Taoyuan Management Office, a risk reference index was proposed for the safety management.

Experimental research and numerical study of leakage influence on the internal two-phase flow of subsea jumper

The influences of jumper leakage on the M-type jumper's internal two-phase flow are investigated. The CFD simulation results are validated against the experimental data, demonstrating a high level of agreement. The effects of leak dimensions, different leak locations, gas-liquid ratio, and mixing speed are discussed in terms of the gas-liquid phase distribution, pressure gradient, and jumper vibrations. It is found that the flow patterns can be useful indicators for jumper leak detection, especially when the leak dimension is larger, the slug flow may be formed, producing more serious damage to the jumper. The flow pattern varies significantly with different leak locations. In addition, the flow pattern is affected by the volume percentage of water and faster mixing speeds. The pressure variations can be useful indicators. It is larger as the leak dimension increases and varies significantly in front of the leak location of the jumper. The vibrations can be useful indicators. The larger the leak dimension is, the more pronounced the vibration response is. If vibration sensors are used for leak detection, the downstream vibration signals of the jumper can be a key indicator. Faster mixing rates and water content will cause the jumper to vibrate more intensely.

Tissue Sealant Impact on Skull Base Reconstruction Outcomes: A Systematic Review and Meta-Analysis.

Despite significant advances in understanding of skull base reconstruction principles, the role of tissue sealants in modifying postoperative cerebrospinal fluid (CSF) leak outcomes remains controversial. We evaluate postoperative CSF leak incidence associated with tissue sealant use in skull base defect repair during endoscopic skull base surgery (ESBS). Web of Science, PubMed/MEDLINE, Scopus, and Cochrane Library. Systematic review and meta-analysis of risk differences (RD). A search strategy identified original studies reporting CSF leakage following ESBS with disaggregation by tissue sealant use and/or type. 27 non-randomized studies (n = 2,403) were included for qualitative and meta-analysis. Reconstruction with a tissue sealant did not significantly reduce postoperative CSF leak risk compared with reconstruction without sealant (RD[95% CI] = 0.02[-0.01, 0.05]). Sub-analyses of dural sealant (-0.02[-0.11, 0.07]) and fibrin glue (0.00[-0.07, 0.07]) compared with no sealant were similarly unremarkable. Postoperative CSF leakage was not significantly modulated in further sub-analyses of DuraSeal (0.02[-0.02, 0.05]), Adherus (-0.03[-0.08, 0.03]), or Bioglue (-0.06[-0.23, 0.12]) versus no dural sealant use, or Tisseel/Tissucol versus fibrin glue nonuse (0.00[-0.05, 0.05]). No significant association was seen comparing dural sealant use versus fibrin glue use on pairwise (0.01[-0.03, 0.05]) or network meta-analysis (-0.01[-0.05, 0.04]). Limitations in source literature prevented sub-analyses stratified by leak characteristics, defect size and location, and accompanying reconstruction materials. Tissue sealant use did not appear to impact postoperative CSF leak incidence when compared with nonuse. Higher quality studies are warranted to thoroughly elucidate the clinical value of adjunct sealant use in endoscopic skull base reconstruction. N/A Laryngoscope, 134:3425-3436, 2024.

Performance time and leak pressure of hand-sewn and skin staple intestinal anastomoses and enterotomies in cadaveric cats.

To compare time to construct completion and leak testing between hand-sewn and skin staple anastomoses and enterotomies in cats. Ex vivo, randomized study. Fresh feline cadavers (n = 20). Jejunal segments (8 cm) were harvested and tested on the same day as euthanasia. From each cadaver, one segment was randomly assigned to control (C), hand-sewn enterotomy (HSE), and skin staple enterotomy (SSE) groups, and two segments were randomly assigned to hand-sewn anastomosis (HSA) and skin staple anastomosis (SSA) groups. Construct completion time, initial leak pressure (ILP), and maximum intraluminal pressure were compared. Leakage location was reported. Mean time (s) ± SD was longer (p < .001) for HSA (317.0 ± 50.9) than SSA (160.8 ± 13.1) and for HSE (172.0 ± 36.5) than SSE (20.3 ± 5.0). ILP (mean ± SD) for C (600.0 mmHg ±0.0) was higher (p < .001) than all constructs. ILP (mean ± SD) for SSA (124.2 mmHg ±83.7) was not different (p = .49) than HSA (86.1 ± 51.9), but HSE (200.3 ± 114.7) was higher (p < .001) than SSE (32.2 ± 39.7). Immediate leakage from the center of enterotomy closure was observed in 7/20 SSE. HSA construct completion took twice as long as SSA with no difference in intraluminal pressures. Although HSE construct completion took 8x as long as SSE, HSE had higher intraluminal pressures. In cats, SSA may be an alternative to HSA for intestinal anastomosis, but SSE is not recommended as an alternative to HSE for intestinal enterotomy closure.

Modeling and investigation of the hydrological situation

Thousands of earthen dams have been built in Russia to create accumulating reservoirs, protect low-lying areas from floods, hydropower and others. Sometimes, due to the presence of underground leaks, erosion of the dam occurs and a breakthrough with catastrophic consequences. Therefore, the task of timely identification of places of underground leaks of earthen dams is urgent. Objective – to show the results of a flight experiment and the practical use of a microwave radiometer mounted on an unmanned aerial vehicle (UAV) in order to monitor the condition of an earthen dam in the Voronezh region and the possibility of searching for places of underground leaks. The paper presents the results of an experimental study of the condition of earthen dams. Also, the analysis of the obtained soil moisture maps allows you to determine the locations of possible underground leaks. The results of a flight experiment using a bipolarization microwave radiometer placed on board an unmanned aerial vehicle for remote determination of a portrait of soil moisture can be used to search for places of possible underground leaks. Also, this method of remote measurement of soil moisture can be used for the needs of agriculture, in particular for precision farming systems. In addition, the paper assesses the prospects of using microwave radiometric moisture meters for operational examination of the state of extended hydrological objects.

Leak detection in an operational underground water distribution network using active acoustics

Leaks present in the water distribution networks (WDNs) lead to an enormous loss of a valuable resource, not only affecting the economic efficiency of water utilities but also posing significant potential safety hazards, and an increased burden on infrastructure maintenance. In the past, several studies have focused on passive acoustic-based leak detection by primarily sensing and analyzing the acoustic signals emitted from the leak source. In this study, we installed a low-frequency transducer in the water column of an operational WDN near Los Angeles, California, and excited the system using steady-state sinusoidal and sine burst signals. The acoustic pressure signals inside the pipe network were measured at multiple locations using state-of-the-art hydrophone-enabled devices retrofitted to fire hydrants. The experiments were conducted in the presence of a simulated leak in the network. The leak acts as an impedance discontinuity for the acoustic wave propagation, and therefore, the excited signals undergo partial reflection at the leak location. Based on signal processing techniques, we attempt to detect and localize the leaks in the WDN. The goal of using the active acoustics is to detect small leaks and increase the range of sensing.