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Pipeline transportation of hydrogen-blended natural gas is susceptible to leakage or rupture accidents caused by pipeline construction, corrosion, and hydrogen embrittlement, posing significant threats to the environment, human safety, and property. This paper improves the model of non-adiabatic pipeline leakage to study the flow characteristics of hydrogen-blended natural gas leakage, and its accuracy is validated using OLGA software and experiment data. The impact of the heat transfer coefficient, initial pressure and hydrogen blending ratio on the leakage flow characteristics is also analyzed. The findings indicate that the initial pressure in the pipe increases linearly at 0.5 MPa and the mass leakage velocity decreases linearly at a rate of nearly 9 kg/s; meanwhile, the temperature drop in the pipe and the overall leakage time increase. An increased hydrogen blending ratio corresponds to lower mass leakage velocity and shorter leakage time. The maximum dangerous distance of pure methane pipeline leakage is greater than that of a pure hydrogen pipeline. Furthermore, changes in the heat transfer coefficient predominantly affect the temperature inside the pipe. • The non-adiabatic leakage model of natural gas pipeline is improved. • The variation law of parameters in pipeline leakage process is discussed. • The effects of heat transfer, initial pressure and hydrogen blended ratio on pipeline leakage flow are studied. [ABSTRACT FROM AUTHOR]

Assessment of pipeline safety is of paramount significance for safety of petrochemical plants. Any leakage of flammable gas from a pipeline poses a potential risk of an explosion followed by fire. Following a leak, a non-burning inert gaseous jet is first formed and, thereafter, ignition happens and an exothermal torch or flame occur. Safety assessments of such a scenario can be made by considering free-boundary-layer turbulent flows of fuel propagating in the surrounding atmosphere based on Prandtl's mixing length theory. That implies modeling the fields of fuel/oxidizer concentrations, velocity, and temperature, and the overall shape of the axisymmetric inert jets and exothermal diffusion torches. Axisymmetric turbulent boundary layer equations have been solved numerically, and these solutions have been compared with self-similar solutions for inert jets. The comparison confirms that the difference between the numerical and self-similar solutions in the longitudinal velocity profile is within 1%. Furthermore, the numerical model predicts the fuel concentration within an uncertainty level of 15% when compared with the experimental data. In an exothermal torch, the combustion temperature keeps increasing until the fuel is depleted. Outside the fuel zone, the temperature of the torch cools abruptly in the radial direction. The longitudinal velocity of an exothermal torch is moderately higher than that of an inert jet because of the thermal expansion. In this work, results of parametric studies are presented for various values of the Reynolds number, which can be useful for petrochemical engineers who assess the risk level associated with potential fire hazards caused by pipeline leakages. [ABSTRACT FROM AUTHOR]

Ground collapse is one of the most common urban disasters, with pipeline leakage being a significant cause. Pipelines are regarded either parallel or perpendicular to the road, using a single variable to control the soil strength or the cavity shape, which over-simplified the development of soil cavities. In this paper, with a coupled method of element deactivation and strength reduction, a more realistic 3D finite element model was established to simulate the development of soil cavities caused by pipeline leakage. Effects of burial depths, intersection angles between loading areas and pipelines, and loading magnitudes on the process of road collapse were explored. The results showed that: (1) an increase in load could alter the collapse mode, with plastic zones appearing at the edges of the soil cavity, leading to a larger collapse impact range; (2) the orientation of the loading area significantly influenced the rate of surface subsidence post-collapse and the range of collapse impacts; (3) using the strength reduction method, a factor of collapse risk was defined to access the collapse risk under pipeline leakage. Based on the computational results, stability charts and collapse risk charts were proposed, providing a theoretical foundation for the prevention and control of urban traffic disasters. [ABSTRACT FROM AUTHOR]

Online monitoring of weld crack leakage in pressure pipelines of nuclear power ship based on acoustic emission (AE) technology is of great significance for maintaining the safe and stable operation of the system. However, most of the current leakage studies are conducted through artificially designed pipeline hole types, which deviate from the actual crack morphology and are weakly online, with low identification accuracy and slow monitoring speed. Therefore, a convolutional network of FGI and multi-scale channel information cross fusion based on AE technology is proposed in this paper. First, the FBank feature of the AE signal of pipeline weld leakage are extracted. On this basis, the Gini Index (GI) preference feature is used to filter the useless information in the FBank feature. Then, a multi-scale channel information cross fusion module is designed to improve the feature learning ability of the network through the interaction and fusion of different channel information. Finally, the superiority of the proposed FGI feature extraction method and the effectiveness of the proposed multi-scale channel information cross fusion CondenseNet (MCCF-CondenseNet) convolutional neural network are verified by the pipeline leakage AE monitoring experiments under three crack morphologies. The results show that the identification accuracy of the proposed method is as high as 96.42 %, and the identification speed is significantly faster than other state-of-the-art approaches under the premise of ensuring the identification accuracy. This work provides a new method for the online leakage monitoring of nuclear power pressure pipelines, and has important supporting significance for the online leakage monitoring of other large and complex equipment. • A novel FGI feature extraction method was proposed to characterize the crack leakage state with different morphologies. • The proposed multi-scale channel information fusion module significantly improves the classification ability of the model. • The proposed mccf-consenet network has faster recognition speed on the premise of ensuring recognition accuracy. • Three kinds of weld crack leakage pipes with different morphologies were innovatively designed. [ABSTRACT FROM AUTHOR]

This paper focuses on the characteristics of pressure wave generated by an active mechanical shock in leaking pipelines. A piston type pressure wave generator was designed and applied to the pipeline experimental system and a three-dimensional (3D) numerical simulation model was developed based on the k - ε turbulence model and dynamic mesh. The experimental results showed that the maximum amplitude of pressure wave was about 104.71 kPa, and the leakage intensified the attenuation of pressure wave in space and time through quantitative analysis. Based on the 3D simulations, the processes of the pressure wave generation and propagation were revealed. The amplitude of pressure wave increased linearly with the increase of the maximum velocity of mechanical shock, and the effects of leakage on the waveforms of pressure wave were observed under the dimensionless analysis. In addition, a shorter duration of mechanical shock can increase the amplitude of the pressure wave generated. The results presented in this paper will contribute to the development and application of the transient leak detection method for pipeline systems. [ABSTRACT FROM AUTHOR]

[Display omitted] • Accurate prediction of pipeline leakage fluid spread crucial for risk assessment. • Method to predict diffusion range of pipeline seepage fluid in unsaturated stratum. • Method considering real-time changes in saturation, permeability, and pressure. • Simulation test to verify the accuracy of the seepage–diffusion model. Accurate prediction of the spread of fluids leaking from underground pipelines is crucial for risk assessment. In this study, we developed a method for predicting the diffusion range of pipeline seepage fluids within an unsaturated stratum. First, we derived a seepage–diffusion equation based on the generalized Darcy's law and subsequently analyzed the mechanism of fluid diffusion in the unsaturated stratum. We then constructed a model for the seepage–diffusion of a pipeline leakage fluid, incorporating variables such as the saturation, permeability coefficient, diffusion pressure, and diffusion distance of soil microelements across various time intervals, using a stepwise algorithm in tandem with the Green–Ampt model and VG–Mualen permeability coefficient function. We investigated the influence of fluid self-gravity, saturated permeability coefficient, initial saturation, and intra-pipe pressure on the diffusion distance of the pipeline leakage fluid in an unsaturated stratum, focusing on specific cases. The results indicate that an increase in the saturated permeability coefficient, initial saturation, and intra-pipe pressure leads to an increase in the fluid diffusion distance. A simulation test was conducted to validate the proposed seepage–diffusion model. The findings of this study can be employed to predict the diffusion range of pipeline leakage fluids in various formation types, providing a vital foundation for pipeline leakage accident management. [ABSTRACT FROM AUTHOR]

In petrochemical system, how to predict the leakage of pipeline is one of the most critical problems due to its huge side effects. Prior studies are mostly based on statistical analysis and they focus on the healthiest state of the system. However, these approaches ignore the trend of changes among data and lead to low accuracies. In this paper, we conduct research on characteristic of petrochemical data and get four core findings. We find that the data are (1) noisy, (2) high dimension, (3) imbalanced, (4) temporal correlated. According to these findings, we propose a novel neural network based classification model using GAN and LSTM to predict the leakage of pipeline. Specifically, we firstly apply a sliding window and a key feature selecting method in data preparation stage to address the noise and high dimension problems. We then propose a GAN based data enhancement approach to synthesize fault data to address the imbalance problem. After that, we propose an LSTM based classifier approach to learn the temporal correlation of data and classify the state of pipeline to predict leakage. We conduct extensive experiments and they show that our approach achieves 2 × F1 score (0.8166) and 3 × AUC (0.8940) compared to the existing methods. Our proposed neural network based approach is more suitable for fault prediction of pipeline leakage in petrochemical system. [ABSTRACT FROM AUTHOR]

Numerous types of liquid, such as water, oil, and so on, are transported via pipelines. Thus, leakage in the pipelines can cause severe hazards to liquid transportation and pose great risks to industries, environment and dwellers. For the purpose of an accurate identification of the leakage location, a novel location algorithm is proposed in this paper based on the attenuation of negative pressure wave (ANPW). As such, this paper presents some of the first efforts in the investigation of the relationship between the pressure difference and leakage location. This is in direct contrast with the traditional method, which relies on the use of the time difference and the velocity of NPW for leakage detection and location. Accordingly, the ANPW method avoids the potential problems of the traditional NPW method – the difficulty for pinpointing the time difference and the disturbance of the velocity of NPW by the liquid flow rate in the pipeline. To explore this new method, this paper firstly deduces the propagation equation of NPW along with the pipeline using the momentum equation and the continuity equation. This is followed by proposing that the ANPW method depends on the change of pressure in place of the time difference. Thirdly, a test of the method in the actual pipeline and comparison between the ANPW method and the time difference method are conducted. Yielding the largest error of 1.161% and the smallest error of 0.355%, the experiment leads to the conclusion that the new method can be applied to the location of pipeline leakage. [ABSTRACT FROM AUTHOR]

With trends in urbanization, many underground pipelines have been constructed, forming a huge spider web-like network, and accidents in such systems can lead to disastrous consequences, as observed in the Qingdao and Kaohsiung gas explosion accidents. For such a complex system, questions related to identifying the accident causes and predicting the evolving trends represent research challenges. Such accidents are often catastrophic and dynamic, and traditional methods are only appropriate for static accident analyses. Based on the above problems, an accident tracing and prediction method based on the bow-tie model and the catastrophe theory dynamic Bayesian network (DBN) model (C-DBN) is proposed in this paper. First, a fishbone diagram (FD) is used to analyze all possible causes of the accident, and the bow-tie and Bayesian network (BN) models are established according to the logical relationship. The conditional probability tables (CPTs) of the BN are obtained using the transformation rule and catastrophe theory. Dynamic analyses of the accident causes and development trends can be conducted using the powerful reasoning ability of the DBN, which can supply guidance for emergency response and rescue. Finally, the Kaohsiung gas explosion accident is used as an example to verify the feasibility of the method. The results show that the analysis results are highly consistent with the real scenario. [ABSTRACT FROM AUTHOR]

• The failure behavior of composite strata (i.e., an upper soft soil layer and a lower hard rock layer) caused by tunnel excavation under seepage conditions was investigated via indoor model tests and finite difference methods. • The distribution, shape, and size of the collapse zone were studied and compared. • The tunnel collapse mechanism was systematically studied and a minimum overburden thickness was devised. Pipeline leakage is a frequent phenomenon in subway construction and has contributed to numerous underground collapses. In this study, the failure behavior of composite strata (i.e., an upper soft soil layer and a lower hard rock layer) caused by tunnel excavation under seepage conditions was investigated via indoor model tests and finite difference methods. It was found that the thickness of the hard overburden above a tunnel considerably influenced the stability of the tunnel. When the overburden thickness was less than half of the tunnel span (D), an increase in the seepage zone could induce the formation of sinkholes after tunnel excavation. When the overburden thickness was greater than 1D, a stable collapse arch was formed after tunnel excavation. Thus, strata between a tunnel overburden and a leakage zone should be reinforced to improve excavation stability. [ABSTRACT FROM AUTHOR]

• Active distributed temperature sensing solution for leakage detection was given. • The Hydro-thermal coupling due to leakage was considered in theoretical modeling. • New actively heated fiber optic sensors were designed to facilitate laboratory tests. • Optical frequency domain reflectometry technique was used to obtain temperature field. This paper presents a feasibility study on leak detection of buried pipelines using the active distributed temperature sensing (ADTS) method. The proposed solution involves the use of actively heated fiber optic (AHFO) cables arranged parallel to the pipelines, allowing for changes in the thermal properties of the surrounding soil to be captured and correlated with leakage accidents. This problem was modeled as an infinite cylindrical or linear heat source buried in porous media with seepage flowing inside. AHFO sensors were designed to verify the proposed method through scale-reduced model tests. The fiber optic temperature sensing technology based on optical frequency domain reflectometry (OFDR) was used to continuously monitor temperature profiles. The test results demonstrate that the ADTS solution has greatly improved the accuracy of passive temperature sensing methods. The feasibility of the solution was evaluated regarding different levels of leakage estimation, namely, identification, location, and quantification. This paper presents the first attempt to monitor the infiltration process associated with pipeline leaks using the AHFO-OFDR technique, which is a reliable method for structural health monitoring of underground pipelines. [ABSTRACT FROM AUTHOR]

Noise in pipeline pressure signal is a small noise of which the amplitude is much smaller than that of signal. But it affects recognition of pressure drop and results in inaccurate leakage localization or false alarm. Thus, a small noise reduction method based on empirical mode decomposition (SNR-EMD) is proposed to reduce the noise in pipeline pressure signal. SNR-EMD removes the noise considered as small fluctuations in signal around the mean line calculated by signal's upper envelope and lower envelope. Meanwhile, end effect of SNR-EMD is restrained by extrema mirror extension (EME). Then tested by pressure signal in field, SNR-EMD can reduce small noise well through making the denoised signal smooth and conserving the mutation characteristics of the original signal. Finally, negative pressure wave (NPW) combined with SNR-EMD is used to locate pipeline leakage. The case study indicates that pressure drop can be well recognized and leakage can be accurately located. [ABSTRACT FROM AUTHOR]

This paper develops a method for separation and extraction of pipeline leakage signals. The independent component analysis is applied to separate acoustic signals, which is then coupled with the image similarity distance matching fusion algorithm for the extraction of the signals due to leakage. Acoustic signals generated by real pipeline leakage and common interference noise are collected respectively in order to form a multi-channel matrix with random mixing of leakage signal and uncorrected noise signals. It is demonstrated that the suspected leakage signals extracted after re-sequencing match well with the original leakage signal in terms of coherence and peak cross-correlation. They are further verified based on the cumulative energy distribution and the energy ratio at low and high frequencies. By effective separation of leak signals from the uncorrected background noise signals, the method presented in this paper provides the basis for the improvement of pipeline leak localization. • The proposed method is applied to separate pipe leakage signals. • Tests were conducted to collet signals from four different noise sources. • Experimental results confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The 2015 Paris agreement aims to cut greenhouse gas emissions and keep global temperature rise below 2 °C above pre-industrial levels. Reducing CH 4 emissions from leaking pipelines presents a relatively achievable objective. While walking and driving surveys are commonly used to detect leaks, the detection probability (DP) is poorly characterized. This study aims to investigate how leak rates, survey distance and speed, and atmospheric conditions affect the DP in controlled belowground conditions with release rates of 0.5–8.5 g min−1. Results show that DP is highly influenced by survey speed, atmospheric stability, and wind speed. The average DP in Pasquill–Gifford stability (PG) class A is 85% at a low survey speed (2–11 mph) and decreases to 68%, 63%, 65%, and 60% in PGSC B/C, D, E/F, and G respectively. It is generally less than 25% at a high survey speed (22–34 mph), regardless of stability conditions and leak rates. Using the measurement data, a validated DP model was further constructed and showed good performance (R 2 : 0.76). The options of modeled favorable weather conditions (i.e., PG stability class and wind speed) to have a high DP (e.g., >50%) are rapidly decreased with the increase in survey speed. Walking survey is applicable over a wider range of weather conditions, including PG stability class A to E/F and calm to medium winds (0–5 m s−1). A driving survey at a low speed (11 mph) can only be conducted under calm to low wind speed conditions (0–3 m s−1) to have an equivalent DP to a walking survey. Only calm wind conditions in PG A (0–1 m s−1) are appropriate for a high driving speed (34 mph). These findings showed that driving survey providers need to optimize the survey schemes to achieve a DP equivalence to the traditional walking survey. [Display omitted] • A method is developed to estimate the leak detection probability of mobile surveys. • Detection probability is highly impacted by survey speed and weather conditions. • The driving survey needs to be optimized to improve the detection probability. [ABSTRACT FROM AUTHOR]

Abstract: A SVM-based pipeline leakage detection and pre-warning system is presented in this paper. In the system an optical cable is laid in parallel with a pipeline in the same ditch and three single mode optical fibers inside constitute the distributed vibration sensor. The sensor is based on Mach–Zehnder optical fiber interferometer and can detect the vibration signals along a pipeline in real time. Then the eigenvectors of vibration signals are extracted by “energy-pattern” method based on wavelet packet decomposition. Subsequently the vibration signals are recognized by support vector machine (SVM) through the features so that it can judge whether any abnormal event is taking place. If any abnormal event is found along a pipeline, the location is thus calculated. A series of trials in situ have been done, showing that the system is of good accuracy and real time performance both in recognition and locating. [Copyright &y& Elsevier]

Pipeline leakage is a common phenomenon encountered in subway construction, which can happen before tunneling or may be induced by tunnel advancement. The strength deterioration of the surrounding soils and the downward seepage force caused by the infiltrated water will reduce the factors of safety of the tunnel. In the present work, a unified three-dimensional upper bound finite element method is established for support pressure assessment of tunnels around leaking pipelines. With the incorporation of suction stress-based effective stress of unsaturated soils, saturated and unsaturated soils could be simulated consistently. Two- and three-dimensional parameter analysis is conducted to study the evolution of limit support pressures and collapse mechanisms with the process of leakage. The effect of leaking location, ground water level, hydraulic properties, water pressure and axial length of leakage on the stability of surrounding tunnels is discussed in detail. It is shown that, the limit support pressure of the tunnel can increase rapidly after the occurrence of pipeline leakage, and a close correlation is found between the limit support pressure and the pore water pressure at the head of the tunnel. [ABSTRACT FROM AUTHOR]

• An adaptive denoising method for pipeline leakage signal based on VMD is presented. • A novel NPW inflection point extraction method based on VMD is proposed. • Noise in leak signal is effectively suppressed and the inflexion is exactly extracted. • The proposed VMD-based method can accurately and stably locate the pipeline leakage. • The presented method has tremendous application prospects in NPW-based technology. Noise makes the inflection point of the negative pressure wave (NPW) difficult to be extracted. Therefore, an adaptive denoising method based on VMD (ADM-VMD) is proposed. ADM-VMD uses correlation coefficient to select effective intrinsic mode function (IMF) components, and obtains the optimal denoising signal according to the minimum information entropy. The traditional inflection point extraction method has the defect of poor robustness. Thus, an adaptive inflection point extraction method based on VMD (IPEM-VMD) is proposed. IPEM-VMD selects the optimal detail signal by kurtosis and extracts the inflection point according to the maximum peak. The experimental conditions with two pipe diameters were set up (0.08 m and 0.125 m). The results show that ADM-VMD can effectively smooth the noise and obtain a clear pressure drop curve. IPEM-VMD obtains a unique and clear maximum peak, which has good robustness to small noise. The average positioning error of the proposed method is 6.42%. [ABSTRACT FROM AUTHOR]

In recent years, underground pipeline leakage occurs frequently in urban regions, which seriously reduces the soil-structure stability in the vicinity of the leakage. In this paper, a numerical study about pipeline leakage-induced instability of adjacent excavation earth retaining wall (EERW) is carried out. To reflect the phenomenon of soil strength degradation when encountering leakage water, the relationship between soil strength parameters and saturation is established by using an empirical formula, which is then implemented in Abaqus via the USDFLD subroutine. After verifying the proposed simulation method, the instability time of EERW under different burial depths of the leakage pipeline is presented. In order to reflect the influence of underlying geotechnical uncertainties on the EERW performance and to improve the probabilistic computational efficiency, the neural network is used as a surrogate model to replace the tedious finite element modeling, and Monte Carlo Simulation (MCS) is used to calculate the time-dependent failure probability of the retaining structure. Extensive sensitivity analyses are carried out to explore the effect of random variables on the failure probabilities. Compared with the unsaturated soil permeability coefficient, the increase of the initial pipeline pressure has more pronounced influence on the stability of EERW, and the initial saturation shows completely different forms in the early and late stages of water leakage. [ABSTRACT FROM AUTHOR]

The pipeline safety management system is crucial to transfer oil and gas because the phenomena of leakage commonly occurs in the long-distant pipelines. Hidden safety hazards can be caused by design and construction defects, affected by natural disasters, or corroded by the transmission fluid. Therefore, quick detection will not only reduce the loss of transported oil and gas but also can protect the local environment. In this study, we develop a new method to detect the pipeline leak by analyzing pressure distribution through the leak. Pipeline leak detection tests are operated using a flow loop. Through the experiments, four parameters which are pressures and flow rates of pipeline inlet and outlet are recorded. Then the specific location of the leak is determined by dimensionless analysis of flow parameters. To generate the solution to locate the leak, we introduce three dimensionless variables, which are dimensionless leak location, dimensionless leak rate, and dimensionless pressure drop. Then the relationship of pressure distribution and leak parameters is built through the simulation models. After running 3D computational fluid dynamics (CFD) simulations with commercial software (FLUENT), the pressure distribution in the pipeline with leakage in the experiments will be verified by the experimental data. Finally, the mathematical models are developed to detect and evaluate the leakage through the pipeline. CFD simulation results show that both the leak rate and location have significant effects on the pressure distribution through the pipe, which is identical to the outcome from the experiments. When the dimensionless leak location is smaller or the dimensionless leak rate is larger, the dimensionless pressure drop is smaller. The mathematical model which is based on these dimensionless variables can be applied in locating the leak point in the real accident. • CFD simulation has been used to detect pipeline leakage. • Experimental investigation on pipeline leak detection has been conducted. • CFD simulation results were validated by experimental data. [ABSTRACT FROM AUTHOR]