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.

Prediction on Failure Pressure of Pipeline Containing Corrosion Defects Based on ISSA-BPNNModel

Oil and gas pipelines are affected by many factors,such as pipe wall thinning and pipeline rupture.Accurate prediction of failure pressure of oil and gas pipelines can provide technical support for pipeline safety management.Aiming at the shortcomings of the BP Neural Network(BPNN)model,such as low learning efficiency,sensitivity to initial weights,and easy falling into a local optimal state,an Improved Sparrow Search Algorithm(ISSA)is adopted to optimize the initial weights and thresholds of BPNN,and an ISSA-BPNN failure pressure prediction model for corroded pipelines is established.Taking 61 sets of pipelines blasting test data as an example,the prediction model was built and predicted by MATLAB software,and compared with the BPNN model,GA-BPNN model,and SSA-BPNN model.The results show that the MAPE of the ISSA-BPNN model is 3.4177%,and the R2 is 0.9880,both of which are superior to its comparison model.Using the ISSA-BPNN model has high prediction accuracy and stability,and can provide support for pipeline inspection and maintenance.

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.

Fuzzy Reliability Analysis for Seabed Oil Gas Pipeline Networks Under Earthquakes

The seabed oil gas pipeline network is simplified to a network with stochastic edge weight by means of the fuzzy graphics theory. With the help of network analysis, fuzzy mathematics, and stochastic theory, the problem of reliability analysis for the seabed oil gas pipeline network under earthquakes is transformed into the calculation of the transitive closure of fuzzy matrix of the stochastic fuzzy network. In classical network reliability analysis, the node is supposed to be non invalidated; in this paper, this premise is modified by introducing a disposal method which has taken the possible invalidated node into account. A good result is obtained by use of the Monte Carlo simulation analysis.

A two-stage optimization method for unmanned aerial vehicle inspection of an oil and gas pipeline network

Oil and gas pipeline networks are a key link in the coordinated development of oil and gas both upstream and downstream.To improve the reliability and safety of the oil and gas pipeline network, inspections are implemented to minimize the risk of leakage, spill and theft, as well as documenting actual incidents. In recent years, unmanned aerial vehicles have been recognized as a promising option for inspection due to their high efficiency. However, the integrated optimization of unmanned aerial vehicle inspection for oil and gas pipeline networks, including physical feasibility, the performance of mission, cooperation, real-time implementation and three-dimensional(3-D) space, is a strategic problem due to its large-scale,complexity as well as the need for efficiency. In this work, a novel mixed-integer nonlinear programming model is proposed that takes into account the constraints of the mission scenario and the safety performance of unmanned aerial vehicles. To minimize the total length of the inspection path, the model is solved by a two-stage solution method. Finally, a virtual pipeline network and a practical pipeline network are set as two examples to demonstrate the performance of the optimization schemes. Moreover, compared with the traditional genetic algorithm and simulated annealing algorithm, the self-adaptive genetic simulated annealing algorithm proposed in this paper provides strong stability.

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.

Risk analysis and maintenance decision making of natural gas pipelines with external corrosion based on Bayesian network

Buried natural gas pipelines are vulnerable to external corrosion because they are encased in a soil environment for a long time.Identifying the causes of external corrosion and taking specific maintenance measures is essential.In this work,a risk analysis and maintenance decision-making model for natural gas pipelines with external corrosion is proposed based on a Bayesian network.A fault tree model is first employed to identify the causes of external corrosion.The Bayesian network for risk analysis is determined accordingly.The maintenance strategies are then inserted into the Bayesian network to show a reduction of the risk.The costs of maintenance strategies and the reduced risk after maintenance are combined in an optimization function to build a decision-making model.Because of the limitations of historical data,some of the parameters in the Bayesian network are obtained from a probabilistic estimation model,which combines expert experience and fuzzy set theory.Finally,a case study is carried out to verify the feasibility of the maintenance decision model.This indicates that the method proposed in this work can be used to provide effective maintenance schemes for different pipeline external corrosion scenarios and to reduce the possible losses caused by external corrosion.

Development of an integrated dynamic model for supply security and resilience analysis of natural gas pipeline network systems

An integrated dynamic model of natural gas pipeline networks is developed in this paper.Components for gas supply,e.g.,pipelines,junctions,compressor stations,LNG terminals,regulation stations and gas storage facilities are included in the model.These components are firstly modeled with respect to their properties and functions and,then,integrated at the system level by Graph Theory.The model can be used for simulating the system response in different scenarios of operation,and evaluate the consequences from the perspectives of supply security and resilience.A case study is considered to evaluate the accuracy of the model by benchmarking its results against those from literature and the software Pipeline Studio.Finally,the model is applied on a relatively complex natural gas pipeline network and the results are analyzed in detail from the supply security and resilience points of view.The main contributions of the paper are:firstly,a novel model of a complex gas pipeline network is proposed as a dynamic state-space model at system level;a method,based on the dynamic model,is proposed to analyze the security and resilience of supply from a system perspective.

Study on Simulation Method of Pipeline Networks' Dynamic Characteristic in Hydraulic Manifold Block

In the design of Hydraulic Manifold Blocks (HMB), dynamic performance of inner pipeline networks usually should be evaluated. To meet the design requirements, dynamic characteristic simulation is often needed. Based on comprehensive study on the existing simulation methods, a new method combined of Power Bond Graph(PBG) and Computational Fluid Dynamic (CFD) is proposed. In this method, flow field of typical channels inside HMB is analyzed with CFD to obtain the local resistance coefficients. Then, with these coefficients, a new sectional lumped-parameter model including kinetic friction factor is developed using PBG. A typical HMB design example is given and the comparison between the simulation and the experimental results demonstrates the feasibility and effectiveness of the proposed method.

Spatial analysis for underground pipeline network information system

It is appropriate to establish underground pipeline network information system based on MapInfo software platform in many enterprises when taking account of the firm size and data amount. Since some functions of MapInfo in spatial analysis are not very strong relatively, it is difficult for MapInfo to fulfill some common functions about pipeline analysis such as spatial configuration, three-dimensional display, pipe exploding and so on. The thought and arithmetic to solve the above problems are approached based on respect theories of computer graphics and graph theory. A variety of function moduli have developed by means of senior computer languages and the system integration is realized.

A Hybrid Encryption Algorithm for Security Enhancement of Wireless Sensor Networks:A Supervisory Approach to Pipelines

Transmission pipelines are vulnerable to various accidents and acts of vandalism.Therefore,a reliable monitoring system is needed to secure the transmission pipelines.A wireless sensor network is a wireless network consisting of distributed devices distributed at various distances,which monitors the physical and environmental conditions using sensors.Wireless sensor networks have many uses,including the built-in sensor on the outside of the pipeline or installed to support bridge structures,robotics,healthcare,environmental monitoring,etc.Wireless Sensor networks could be used to monitor the temperature,pressure,leak detection and sabotage of transmission lines.Wireless sensor networks are vulnerable to various attacks.Cryptographic algorithms have a good role in information security for wireless sensor networks.Now,various types of cryptographic algorithms provide security in networks,but there are still some problems.In this research,to improve the power of these algorithms,a new hybrid encryption algorithm for monitoring energy transmission lines and increasing the security of wireless sensor networks is proposed.The proposed hybrid encryption algorithm provides the security and timely transmission of data in wireless sensor networks to monitor the transmission pipelines.The proposed algorithm fulfills three principles of cryptography:integrity,confidentiality and authentication.The details of the algorithm and basic concepts are presented in such a way that the algorithm can be operational.

Numerical simulation of a gas pipeline network using computational fluid dynamics simulators

This article describes numerical simulation of gas pipeline network operation using high-accuracy computational fluid dynamics (CFD) simulators of the modes of gas mixture transmission through long, multi-line pipeline systems (CFD-simulator). The approach used in CFD-simulators for modeling gas mixture transmission through long, branched, multi-section pipelines is based on tailoring the full system of fluid dynamics equations to conditions of unsteady, non-isothermal processes of the gas mixture flow. Identification, in a CFD-simulator, of safe parameters for gas transmission through compressor stations amounts to finding the interior points of admissible sets described by systems of nonlinear algebraic equalities and inequalities. Such systems of equalities and inequalities comprise a formal statement of technological, design, operational and other constraints to which operation of the network equipment is subject. To illustrate the practicability of the method of numerical simulation of a gas transmission network, we compare computation results and gas flow parameters measured on-site at the gas transmission enter-prise.

Pipeline Scheduling Based on Constructive Interference in Strip Wireless Sensor Networks

Strip Wireless Sensor Networks(SWSNs)have drawn much attention in many applications such as monitoring rivers,highways and coal mines.Packet delivery in SWSN usually requires a large number of multi-hop transmissions which leads to long transmission latency in low-duty-cycle SWSNs.Several pipeline scheduling schemes have been proposed to reduce latency.However,when communication links are unreliable,pipeline scheduling is prone to failure.In this paper,we propose a pipeline scheduling transmission protocol based on constructive interference.The protocol first divides the whole network into multiple partitions and uses a pipelined mechanism to allocate active time slots for each partition.The nodes in the same partition wake up at the same time for concurrent transmission.Multiple identical signals interfere constructively at the receiver node,which enhances received signal strength and improves link quality.Simulations show that the proposed scheme can significantly reduce the transmission latency while maintaining low energy consumption compared with other schemes.

Computational Tools to Support Ethanol Pipeline Network Design Decisions

This paper considers the pipeline network design problem (PND) in ethanol transportation, with a view to providing robust and efficient computational tools to assist decision makers in evaluating the technical and economic feasibility of ethanol pipeline network designs. Such tools must be able to address major design decisions and technical characteristics, and estimate network construction and operation costs to any time horizon. The specific context in which the study was conducted was the ethanol industry in Sao Paulo. Five instances were constructed using pseudo-real data to test the methodologies developed.

Research on Optimization Operation of Urban Gas Pipeline Network

The optimiz at ion operation of gas pipeline network is investigated in this paper. Based on th e theories of system optimization and the multi object decision, a mathematical model about the multi object optimization operation of gas pipeline network is established, in line with the demand of urban gas pipeline network operation. A t the same time, an effective solution of the mathematical model is presented. A calculating software about optimization operation is compiled, coupling the actual operation of gas pipeline network. It can be applied to the operation of the gas pipeline network. The software was examined by real examples. The resul ts indicated that 2.13% energy consumption and 3.12% gas supply cost can be reduced through optimization operation.

PIPELINE软件在气田集输实时监控中的应用

PIPELINE软件是一种离线或者在线的集输管网仿真模拟计算软件,不但可以用来测试和评价输气管道的设计或进行实际操作参数的设置,还能与气田集输实时监控系统进行有机结合,为集输管网运行提供辅助专家诊断分析。以大牛地气田集输生产实际为例,进行管网水力、热力计算,天然气调峰正常工况分析和用户中断用气工况模拟分析,结果表明,该仿真模拟软件和实时监控系统有机结合,改变了气田根据人工计算或凭经验来预测天然气集输管网的运行情况和调峰的现状,不但降低了劳动强度,而且使预测效果得到了根本性提升,排除了管网运行管理的安全隐患。PIPELINE软件不仅方便了管网的运营管理,还避免了生产调度上的盲目性,为科学地管理气田集输管网系统提供了专家式的理论指导。

A Molecular Description of Superconductivity of Sulfur Hydride and Related Systems under High-Pressure Conditions

It has been shown that the recently discovered sulfur trihydride (H3S) can be considered as a superconductor with a transition temperature Tc of 203 Kelvin (K) at 155 GigaPascals (GPa). This is the highest Tc value reported for any superconductor. The established superconductivity occurs via the formation of a molecular system with sulfur atoms arranged on a body-centered cubic lattice. It has been generally accepted that the high Tc value is the result of an efficient electron-phonon interaction. The responsible substance formed by H2S under high pressure, may be considered as a compound with H3S stoichiometry creating an impressive network with hydrogens. We will focus on the hydrogen bonding between sulfur and hydrogens demonstrating a symmetrical arrangement. The geometry of the individual radical compound in relation to corresponding systems will be discussed. Ab initio calculations based on a linear three-center two-, three- and four-electron type of bonding clearly visualized in combination with the dynamics of the Van’t Hoff concept, as described by us in various papers, give a good description of this exclusive network. We also discuss the superconductivity of related phosphorus hydrides and focus on the stability and geometrical differences with respect to the H3S system. These differences are significant, demonstrating the diversity in various structures in showing superconductivity.

Design of motion control system of pipeline detection AUV

A great number of pipelines in China are in unsatisfactory condition and faced with problems of corrosion and cracking,but there are very few approaches for underwater pipeline detection.Pipeline detection autonomous underwater vehicle(PDAUV) is hereby designed to solve these problems when working with advanced optical,acoustical and electrical sensors for underwater pipeline detection.PDAUV is a test bed that not only examines the logical rationality of the program,effectiveness of the hardware architecture,accuracy of the software interface protocol as well as the reliability and stability of the control system but also verifies the effectiveness of the control system in tank experiments and sea trials.The motion control system of PDAUV,including both the hardware and software architectures,is introduced in this work.The software module and information flow of the motion control system of PDAUV and a novel neural network-based control(NNC) are also covered.Besides,a real-time identification method based on neural network is used to realize system identification.The tank experiments and sea trials are carried out to verify the feasibility and capability of PDAUV control system to complete underwater pipeline detection task.

中国西部地区成品油管网线路规划建议

在介绍西部地区成品油运输格局的基础上,分析该地区成品油管网的增输潜力。2020年,西部地区成品油有628万吨铁路量通过管网线路优化互通后可转为成品油管道运输量。西部地区成品油管网线路规划主要包括两个方面:一是重点结合西北地区铁路远距离发运路线,依托现有成品油管道,开展管道延伸研究;二是逐步形成西南地区成品油管网互联互通研究,推动区域物流优化。建议编制完善全国成品油管网规划建设与互联互通方案,成品油管输实行标签化管理,建立合作共赢的管网运营协同协作机制。