Experimental and Finite Element Analysis of Corroded High-Pressure Pipeline Repaired by Laminated Composite

Repairs of corroded high-pressure pipelines are essential for fluids transportation under high pressure.One of the methods used in their repairs is the use of layered composites.The composite used must have the necessary strength.Therefore,the experiments and analytical solutions presented in this paper are performed according to the relevant standards and codes,including ASME PCC-2,ASME B31.8S,ASME B31.4,ISO 24817 and ASME B31.G.In addition,the experimental tests are replicated numerically using the finite element method.Setting the strain gauges at different distances from the defect location,can reduce the nonlinear effects,deformation,and fluctuations due to the high pressure.The direct relationship between the depth of an axial defect and the stress concentration is observed at the inner side edges of the defect.Composite reparation reduces the non-linearities related to the sharp variation of the geometry and a more reliable numerical simulation could be performed.

Modeling of Hydrogen Blending on the Leakage and Diffusion of Urban Buried Hydrogen-Enriched Natural Gas Pipeline

With the introduction of various carbon reduction policies around the world,hydrogen energy,as a kind of clean energy with zero carbon emission,has attracted much attention.The safe and economical transportation of hydrogen is of great significance to the development of hydrogen energy industries.Utilizing natural gas pipelines to transport hydrogen is considered to be an efficient and economical way.However,hydrogen has a higher risk of leakage due to its strong diffusion capacity and lower explosive limit than conventional natural gas.Therefore,it is of great significance to study the leakage and diffusion law of hydrogen-enriched natural gas(HENG)pipelines for the safe transportation of hydrogen energy.In this study,the leakage and diffusion characteristics of urban buried HENG pipelines are investigated numerically,and the dangerous degree of leakage is analyzed based on the time and area when the gas concentration reaches the lower explosive limit.The influences of hydrogen blending ratio(HBR),operating pressure,leakage hole size and direction,as well as soil type on the leakage and diffusion law of HENG are analyzed.Results show that the hydrogen mixing is not the key factor in increasing the degree of risk after gas leakage for urban buried HENG pipelines.When the HBR is 5%,10%,15% and 20%,the corresponding first dangerous time is 1053,1041,1019 and 998 s,respectively.Thiswork is expected to provide a valuable reference for the safe operation and risk prevention of HENG pipelines in the future.

A Novel Parameter-Optimized Recurrent Attention Network for Pipeline Leakage Detection

Accurate detection of pipeline leakage is essential to maintain the safety of pipeline transportation.Recently,deep learning(DL)has emerged as a promising tool for pipeline leakage detection(PLD).However,most existing DL methods have difficulty in achieving good performance in identifying leakage types due to the complex time dynamics of pipeline data.On the other hand,the initial parameter selection in the detection model is generally random,which may lead to unstable recognition performance.For this reason,a hybrid DL framework referred to as parameter-optimized recurrent attention network(PRAN)is presented in this paper to improve the accuracy of PLD.First,a parameter-optimized long short-term memory(LSTM)network is introduced to extract effective and robust features,which exploits a particle swarm optimization(PSO)algorithm with cross-entropy fitness function to search for globally optimal parameters.With this framework,the learning representation capability of the model is improved and the convergence rate is accelerated.Moreover,an anomaly-attention mechanism(AM)is proposed to discover class discriminative information by weighting the hidden states,which contributes to amplifying the normalabnormal distinguishable discrepancy,further improving the accuracy of PLD.After that,the proposed PRAN not only implements the adaptive optimization of network parameters,but also enlarges the contribution of normal-abnormal discrepancy,thereby overcoming the drawbacks of instability and poor generalization.Finally,the experimental results demonstrate the effectiveness and superiority of the proposed PRAN for PLD.

Detecting the backfill pipeline blockage and leakage through an LSTM-based deep learning model

Detecting a pipeline's abnormal status,which is typically a blockage and leakage accident,is important for the continuity and safety of mine backfill.The pipeline system for gravity-transport high-density backfill(GHB)is complex.Specifically designed,efficient,and accurate abnormal pipeline detection methods for GHB are rare.This work presents a long short-term memory-based deep learning(LSTM-DL)model for GHB pipeline blockage and leakage diagnosis.First,an industrial pipeline monitoring system was introduced using pressure and flow sensors.Second,blockage and leakage field experiments were designed to solve the problem of negative sample deficiency.The pipeline's statistical characteristics with different working statuses were analyzed to show their complexity.Third,the architecture of the LSTM-DL model was elaborated on and evaluated.Finally,the LSTM-DL model was compared with state-of-the-art(SOTA)learning algorithms.The results show that the backfilling cycle comprises multiple working phases and is intermittent.Although pressure and flow signals fluctuate stably in a normal cycle,their values are diverse in different cycles.Plugging causes a sudden change in interval signal features;leakage results in long variation duration and a wide fluctuation range.Among the SOTA models,the LSTM-DL model has the highest detection accuracy of98.31%for all states and the lowest misjudgment or false positive rate of 3.21%for blockage and leakage states.The proposed model can accurately recognize various pipeline statuses of complex GHB systems.

WAVELET TRANSFORM THRESHOLD NOISE REDUCTION METHODS IN THE OIL PIPELINE LEAKAGE MONITORING AND POSITIONING SYSTEM

This letter investigates the wavelet transform, as well as the principle and the method of the noise reduction based on wavelet transform, it chooses the threshold noise reduction, and discusses in detail the principles, features and design steps of the threshold method. Rigrsure, heursure, sqtwolog and minimization four kinds of threshold selection method are compared qualitatively, and quantitatively. The wavelet analysis toolbox of MATLAB helps to realize the computer simulation of the signal noise reduction. The graphics and calculated standard deviation of the various threshold noise reductions show that, when dealing with the actual pressure signal of the oil pipeline leakage, sqtwolog threshold selection method can effectively remove the noise. Aiming to the pressure signal of the oil pipeline leakage, the best choice is the wavelet threshold noise reduction with sqtwolog threshold. The leakage point is close to the actual position, with the relative error of less than 1%.

Research on Magnetic Flux Leakage Detection Method for Subsea Pipeline

The intelligent pig based on the (MFL) is frequently used for in-line inspection of transportation pipelines. The article discusses the key technology of an MFL tool that includes the sensors structure, the constitution of tool hardware, software and the analysis method of MFL signal.

Coefficients de-noising with wavelet transform for magnetic flux leakage data obtained from oil pipeline

This paper considers the problem of noise cancellation for the magnetic flux leakage (MFL) data obtained from the inspection of oil pipelines. MFL data is contaminated by various sources of noise, and the noise can considerably reduce the detectability of flaw signals in MFL data. This paper presents a new de-noising approach for removing the system noise contained in the MFL data by using the coefficients de-noising with wavelet transform. Experimental results are presented to demonstrate the advantages of this de-noising approach over the conventional wavelet de-noising method.

Simulation Study on the Typical Influencing Factors for Negative Pressure Wave in Liquid Pipeline Leakage

We conduct simulation study on the typical influencing factors for negative pressure wave in liquid pipeline leakage. We first analyse the liquid pipeline leakage detection based on negative pressure wave method and obtain the essential simulation parameters. Then based on the physical model of pipeline and by introducing leakage boundary condition, we simulate the variation of pressure and flow rate in pipeline after leakage, the influence of leakage scale and leakage position on the pressure and flow rate in the pipeline. The results show that the leakage scale mainly influences the amplitude of negative pressure wave, and that the leakage position inflnenees both the amplitude and the shape of the curves of negative pressure wave.

Using acoustic technique to detect leakage in city gas pipelines

For solving the difficult problem of leakage detection in city gas pipelines, a method using acoustic technique based on instantaneous energy (IE) distribution and correlation analysis was proposed. Firstly, the basic theory of leakage detection and location was introduced. Then the physical relationship between instantaneous energy and structural state variation of a system was analyzed theoretically. With HILBERT-HUANG transformation (HHT), the instantaneous energy distribution feature of an unstable acoustic signal was obtained. According to the relative contribution method of the instantaneous energy, the noise in signal was eliminated effectively. Furthermore, in order to judge the leakage, the typical characteristic of the instantaneous energy of signal in the input and output end was discussed using correlative analysis. A number of experiments were carried out to classify the leakage from normal operations, and the results show that the leakages are successfully detected and the average recognition rate reaches 93.3% among three group samples. It is shown that the method using acoustic technique with IED and correlative analysis is effective and it may be referred in other pipelines.

Numerical and experimental investigation of leakage behavior at null position of high-pressure electro-pneumatic servo valve

The high-pressure electro-pneumatic servo valve(HESV)is a core element of the high-pressure pneumatic servo system.The annular clearance and the rounded corner of the spool-sleeve can cause the leakage at null position,thereby affecting high-precision control and stability of the servo system.This paper investigates the effects of the clearance structure on leakage behavior at null position of the HESV.A numerical approach was employed to evaluate the effects,and then a mathematical model was established to obtain the variation law of leakage flow rate at null position.The results indicate that the leakage flow rate at null position varies linearly with supply pressure and rounded corner radius,and is nonlinear as a quadratic concave function with annular clearance.The leakage flow rate of the annular clearance and the rounded corner varies with the valve opening in an invariable−nonlinear−linear trend.A test rig system of leakage behavior at null position of the HESV was built to confirm the validity of the numerical model,which agrees well with the conducted experimental study.

Fluid Leakage in Submerged Offshore Pipeline: An Analysis of Oil Dispersion in Seawater

Leakages in oil pipelines can cause financial losses and several environmental damages, where large-scale offshore oil and gas exploration results in large releases of oil and gas into ocean waters. In the event of oil leakage, an immediate and adequate response is required to reduce environmental damage, such as containment barriers, for example, which depends on the agglomeration of oil particles, velocity and tendency to propagation. Thus, the understanding of the fluid flow behavior around of subsea pipeline at different depths is crucial. On the other hand, the knowledge of interfacial phenomena of immiscible liquids allows the process of adjective migration in submarine pipelines. Consequently, this science enables the prediction of the behavior and the geometric shape of the water-oil interface and provides a phenomenological foundation concerning the theories of perturbation, the stability criteria and mathematical modeling, as well as the flow patterns in the neighborhoods and submerged pipelines. From this perspective, this work aims to study the oil dispersion in sea water caused by leakage in a submerged pipeline. Here, a two-dimensional mathematical model based on the mass and linear momentum conservation equations and the standard k-ε turbulence model, was developed. The dynamic behavior of the oil and water phases is evaluated by pressure fields, surface velocity, volumetric fraction and velocity vectors. Simulation results show the presence of oil flux from the pipe to the marine stream and vice-versa. Further, the increase in oil velocity at the pipe inlet leads to an increase in pressure drop.

Optimization of the Open Degree of Key Valves Based on Relative Entropy and Pipeline Leakage

Based on information entropy theory, the definition of relative entropy, and the relative entropy minimum principle, this study establishes a multi-objective optimization model for a key valve opening of an urban water distribution network (WDN). Each node pressure is taken as the main research object to reduce pipeline leakage. Moreover, genetic algorithm is applied in the proposed model to solve the key valve opening of the actual WDN in a city in southern China. Using the proposed model, the relevant decision variables of a WDN can be optimized to provide a new manner of network dispatching. © 2017, Tianjin University and Springer-Verlag GmbH Germany.

Composite excitation multi-extension direction defect magnetic flux leakage detection technology

In the traditional pipeline magnetic flux leakage(MFL)detection technology,circumferential or axial excitation is mainly used to excite the magnetic field of defects.However,the domestic and foreign pipeline detection devices currently in operation are mainly axial excitation MFL detection tools,in which circumferential cracks can be clearly identified,but the detection sensitivity of axial cracks is not high,thus forming a detection blind zone.Therefore,a composite excitation multi-extension direction defect MFL detection method is proposed,which can realize the simultaneous detection of axial and circumferential defects.On the basis of the electromagnetic theory Maxwell equation and Biot Savart law,a mathematical model of circumferential and axial magnetization is firstly established.Then finite element simulation software is used to establish a model of a new type of magnetic flux leakage detection device,and a simulation analysis of crack detection in multiple extension directions is carried out.Finally,under the conditions of the relationship model between the change rate of leakage magnetic field and external excitation intensity under unsaturated magnetization and the multi-stage coil magnetization model,the sample vehicle towing experiment is carried out.The paper aims to analyze the feasibility and effectiveness of the new magnetic flux leakage detection device for detecting defects in different extension directions.Based on the final experimental results,the new composite excitation multi extension direction leakage magnetic field detector has a good detection effect for defects in the axial and circumferential extension directions.

Wavelet domain adaptive filtering algorithm for removing the seamless pipe noise contained in the magnetic flux leakage data

With the widespread application and fast development of gas and oil pipeline network in China, the pipeline inspection technology has been used more extensively. The magnetic flux leakage （MFL） method has established itself as the most widely used in-line inspection technique for the evaluation of gas and oil pipelines. The MFL data obtained from seamless pipeline inspection is usually contaminated by the seamless pipe noise （SPN）. SPN can in some cases completely mask MFL signals from certain type of defects, and therefore considerably reduces the detectability of the defect signals. In this paper, a new de-noising algorithm called wavelet domain adaptive filtering is proposed for removing the SPN contained in the MFL data. The new algorithm results from combining the wavelet transform with the adaptive filtering technique. Results from application of the proposed algorithm to the MFL data from field tests show that the proposed algorithm has good performance and considerably improves the detectability of the defect signals in the MFL data.

Convolutional Neural Network-based Leakage Detection of Crude Oil Transmission Pipes

Due to the rapid development in the petroleum industry,the leakage detection of crude oil transmission pipes has become an increasingly crucial issue.At present,oil plants at home and abroad mostly use manual inspection method for detection.This traditional method is not only inefficient but also labor-intensive.The present paper proposes a novel convolutional neural network(CNN)architecture for automatic leakage level assessment of crude oil transmission pipes.An experimental setup is developed,where a visible camera and a thermal imaging camera are used to collect image data and analyze various leakage conditions.Specifically,images are collected from various pipes with no leaking and different leaking states.Apart from images from existing pipelines,images are collected from the experimental setup with different types of joints to simulate leakage conditions in the real world.The main contributions of the present paper are,developing a convolutional neural network to classify the information in red-green-blue(RGB)and thermal images,development of the experimental setup,conducting leakage experiments,and analyzing the data using the developed approach.By successfully combining the two types of images,the proposed method is able to achieve a higher classification accuracy,compared to other methods that use RGB images or thermal images alone.Especially,compared with the method that uses thermal images only,the accuracy increases from about 91%to over 96%.

Numerical analysis and experimental research on detection of welding defects in pipelines based on magnetic flux leakage

Regular inspection of long-distance oil and gas pipelines plays an important role in ensuring the safe transportation of oil and gas,and inspection on welding defects is an important part of the inspection process.Magnetic flux leakage(MFL)is an electromagnetic non-destructive testing technique which has been commonly utilized to detect welding defects in pipelines.In the present study,Maxwell electro-magnetic simulation software was used to carry out numerical study on the welding defects in pipelines,including incomplete penetration and undercut.TheФ406 pipeline with a wall thickness of 7 mm was selected as the study case to establish the numerical model.Setting the life-off value at 1 mm,the distribution of magnetic leakage field was investigated for pipeline without defect,pipeline with incomplete penetration defect and pipeline with undercut defect respectively,the characteristic values describing the depth and width of defects were found.Furthermore,quantified equations which can be used to describe the defect depth were proposed.Finally,experimental research was carried out to validate the effectiveness of the numerical model,and the experimental results showed good consistence with the numerical calculation results.The research results indicate that,it is technically feasible and reliable to diagnose the incomplete penetration and undercut welding defects in pipelines using MFL.

Development of systems for detection,early warning,and control of pipeline leakage in drinking water distribution:A case study

Water leakage in drinking water distribution systems is a serious problem for many cities and a huge challenge for water utilities.An integrated system for the detection,early warning,and control of pipeline leakage has been developed and successfully used to manage the pipeline networks in selected areas of Beijing.A method based on the geographic information system has been proposed to quickly and automatically optimize the layout of the instruments which detect leaks.Methods are also proposed to estimate the probability of each pipe segment leaking （on the basis of historic leakage data）,and to assist in locating the leakage points （based on leakage signals）.The district metering area （DMA） strategy is used.Guidelines and a flowchart for establishing a DMA to manage the large-scale looped networks in Beijing are proposed.These different functions have been implemented into a central software system to simplify the day-to-day use of the system.In 2007 the system detected 102 non-obvious leakages （i.e.,14.2% of the total detected in Beijing） in the selected areas,which was estimated to save a total volume of 2,385,000 m 3 of water.These results indicate the feasibility,efficiency and wider applicability of this system.

CFD Investigation of Diffusion Law and Harmful Boundary of Buried Natural Gas Pipeline in the Mountainous Environment

The leakage gas from a buried natural gas pipelines has the great potential to cause economic losses and environmental pollution owing to the complexity of the mountainous environment.In this study,computational fluid dynamics(CFD)method was applied to investigate the diffusion law and hazard range of buried natural gas pipeline leakage in mountainous environment.Based on cloud chart,concentration at the monitoring site and hazard range of lower explosion limit(LEL)and upper explosion limit(UEL),the influences of leakage hole direction and shape,soil property,burial depth,obstacle type on the diffusion law and hazard range are analyzed.Results show that the leakage gas is not radially diffused until it reaches the ground,and the velocity of gas diffusion to the ground and the hazard range decrease as the angle between the leaking direction and the buoyancy direction increases.Triangular and square leak holes have a faster diffusion rate and a wider hazard range than circular.The diffusion rate of leakage gas in soil rises as soil granularity and porosity increase.The time of leakage gas diffusion to the ground increases significantly with the increase of burial depth,and the hazard range reduces as burial depth increases.Boulder-type obstacles will alter the diffusion path of the leakage gas and accelerate the expansion of the hazard distance,while trench-type obstacles will cause the natural gas to accumulate in the trench and form a high concentration region slowing the expansion of the surface gas concentration.

Development of a Low-Cost Prototype System for Pipeline Operational and Vandalism Spillage Detection and Validation Framework

Crude oil spillage is a major challenge in Nigeria. It affects the environment, health, life, and livelihood of residents of the Niger Delta region, where oil is explored, processed, and transported via a network of pipelines. Oil spillage is primarily caused by vandalization/sabotage and operational issues such as corrosion, equipment failure, operation, and maintenance errors. Thus, prompt response is required to mitigate the impact of oil spills. In this study, we deployed low-cost Arduino systems, including sensors (vibration and flow), modules (GPS and Wifi) and an IoT platform (ThingSpeak) to detect spillage caused by vandalism and operational inefficiencies proactively. The results demonstrate that low-cost sensors can detect changes in the flow volume between the inflow and outflow attributable to spillage, and vibration shocks caused by vandalism can be detected and linked to the cause of the spillage and communicated in real time to inform response action. Moreover, we proposed a framework for field validation utilizing KoboToolBox (a crowdsourcing/citizen science platform). The prototype system designed and programmed showed promising results, as it could detect spillage for vandalism and operational scenarios in real-time, quantify the volume of spillage, and identify the location and time of spillage occurrence;indicators relevant for response planning to minimize the impact of oil spillage. A video demonstration of the prototype system developed is accessible via: https://youtu.be/wKa9MZvYf1w. .

Leakage and diffusion characteristics of underground hydrogen pipeline

Soil corrosion and hydrogen embrittlement are the main factors of hydrogen pipeline failure. The gas escapes, diffuses and accumulates in the soil and enters the atmosphere when leak occurs. The mechanism of gas diffusion in buried pipelines is very complicated. Mastering the evolution law of hydrogen leakage diffusion is conducive to quickly locating the leakage point and reducing the loss. The leakage model of the underground hydrogen pipeline is established in this paper. The effect of leakage hole, soil type, pipeline pressure, pipeline diameter on hydrogen leakage diffusion were investigated. The results show that when the hydrogen pipeline leaks, the hydrogen concentration increases with the increase of leakage time, showing a symmetrical distribution trend. With the pipeline pressure increase, hydrogen leakage speed is accelerated, and longitudinal diffusion gradually becomes the dominant direction. As the leakage diameter increases, hydrogen leakage per unit of time increases sharply. Hydrogen diffuses more easily in sandy soil, and its diffusion speed, concentration, and range are higher than that in clay soil. The research content provides a reference and basis for the detection and evaluation of buried hydrogen pipeline leakage.