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In this study, the ANSYS 2020R1 software simulation is employed to examine the diffusion process of oil leakage and the underground water solute transport law in the Dashagou Yellow River crossing section of the oil pipeline. The simulation results demonstrate that under identical leakage pressure conditions, diesel fuel leakage in powdery, sandy soil is diminished, the emergency window is extended, and the corresponding leakage risk is reduced. In addition, the leakage rate of crude oil is slower than that of diesel oil. After 850 days of downward migration of approximately 190 m, the pollutant reaches quasi-static equilibrium in the big sand ditch. The results of the surface water oil spill analysis demonstrated that the oil film on the river surface migrated for 100 min after the spill, with a thickness that remained between 0.02 and 0.05 mm and a concentration that approached equilibrium. [ABSTRACT FROM AUTHOR]

This study investigates the impact of natural gas pipeline leakage on the soil temperature field through numerical simulations. Physical and mathematical models were developed to analyze the temperature and flow field changes resulting from pipeline leaks. The study explores the influence of various leakage factors on the temperature distribution in the surrounding soil. Key findings include the identification of the buried pipeline temperature as a critical factor influencing the soil temperature gradient when surface temperatures are similar to the subsurface constant temperature. Upon leakage, the pressure distribution around the leak is symmetrical, with a higher pressure at the leak point, and the Joule–Thomson effect causes a rapid decrease in gas temperature, forming a permafrost zone. The study also reveals that increased transport pressure expands the permafrost area, with pressure playing a significant role in the temperature field distribution. Additionally, an increase in the leak orifice diameter accelerates the expansion of the permafrost area and reduces the time for temperature stabilization at monitoring points. Conversely, changes in the leak direction mainly affect the spatial distribution of the permafrost zone without significantly altering its size. The findings provide valuable insights for monitoring natural gas pipeline leaks through temperature field variations. [ABSTRACT FROM AUTHOR]

This study introduces an advanced deep-learning framework for the real-time detection of pipeline leaks in smart city infrastructure. The methodology transforms acoustic emission (AE) signals from the time domain into scalogram images using continuous wavelet transform (CWT) to enhance leak-related features. A Gaussian filter minimizes background noise and clarifies these features further. The core of the framework combines convolutional neural networks (CNNs) with long short-term memory (LSTM), ensuring a comprehensive examination of both spatial and temporal features of AE signals. A genetic algorithm (GA) optimizes the neural network by isolating the most important features for leak detection. The final classification stage uses a fully connected neural network to categorize pipeline health conditions as either 'leak' or 'non-leak'. Experimental validation on real-world pipeline data demonstrated the framework's efficacy, achieving accuracy rates of 99.69%. This approach significantly advances smart city capabilities in pipeline monitoring and maintenance, offering a durable and scalable solution for proactive infrastructure management. [ABSTRACT FROM AUTHOR]

The drainage pipelines put into use in the early stage have been faced with various problems with the rising of groundwater level and the increase of service time of drainage pipelines in Beijing, among which the failure of drainage pipeline structure is particularly prominent. In order to study the failure mechanism of the drainage pipeline structure, the causes of the failure of the drainage pipeline structure were analyzed based on the site investigation on the diseases of the existing drainage pipelines in Beijing. Three key factors including the backside empties, the change of groundwater level and the load of ground vehicles were obtained, and the influences of these three factors on the failure mechanism of pipelines were further investigated. Based on the parameters of a practical project, a series of numerical simulations were carried out to calculate and analyze the related parameters of pipelines such as horizontal displacement, vertical displacement and safety factor so as to investigate the failure mechanism of pipelines. The results show that the key of drainage pipeline accidents is pipeline leakage, and the main reasons are living environment, construction disturbance, bearing capacity, respectively. Moreover, 70% of pipeline accidents are caused by pipeline living environment, and 25% of that are caused by construction disturbance, while only 5% are caused by pipeline itself. Besides, the natural environment and construction quality problems easily lead to the formation of a small range of non-dense areas around the pipeline, which then evolve into serious unstable bodies such as voids. The appearance of voids between the drainage pipelines and the soil will lead to a large reduction in the safety factor of pipeline structure. The rise of groundwater level will affect the safety performance of the drainage pipeline structure and reduce the safety factor of the using drainage pipeline. When the road vehicle load is orthogonal to the drainage pipeline, the influence of the vehicle load on pipeline structure is the most, and the safety factor of the upper part of the structure is significantly smaller than that of other conditions. Therefore, more attention should be paid to the influence of voids between the drainage pipelines and the soil on the structure of drainage pipelines. And line, the main measurement and treatment measures should be adopted for the drainage pipeline orthogonal to the ground traffic to maintain the safety of the pipeline structure and prevent leakage and disease. [ABSTRACT FROM AUTHOR]

Upon leakage in underground gas pipelines, the interaction between soil particles and gas will produce acoustic events exhibiting varied frequencies, amplitudes, and energy characteristics. In order to obtain the acoustic response of gas pipeline leaks that are buried, experiments were conducted using a two-dimensional visual leak testing facility. Employing time-domain parameter analysis, fast Fourier transform (FFT), and wavelet packet analysis (WPT), this study meticulously investigated the impact of gas pressure and soil moisture on the time-frequency characteristics of the acoustic waves throughout the leakage process. The results show that: (1) the amplitude, dominant frequency, and energy of acoustic waves closely relate to the deformation and disturbance of soil morphology, (2) the amplitude of acoustic waves increases and decreases exponentially with the increase of gas pressure and soil moisture content, respectively, (3) the main frequency response of acoustic waves during the erosion process predominantly lies within the 0 to 1 kHz range, exhibiting an "N-shaped" cyclical variation, and it tends to decrease with the increase in gas pressure and increase with the rise in soil moisture content, (4) as the leakage process continues, the energy ratio of 0–156.25 Hz increases continuously, the maximum is 45.24%, and the frequency bands of 0–156.25 Hz and 156.25–312.5 Hz demonstrate a strong responsive pattern to variations in soil moisture content and gas pressure, respectively. Therefore, these two can be utilized as the characteristic frequency bands to represent the effects of moisture content and gas pressure, and (5) the leakage acoustic sources primarily originate from pipe wall vibrations, gas impact on soil particles, and friction within the soil particle medium, with the latter two types of vibrations generating more propagative acoustic waves. The research results are of great significance to the prediction of soil structure damage and the acoustic monitoring of gas leakage. [ABSTRACT FROM AUTHOR]

In order to study the changes in two‐phase flow parameters after pipeline damage and the impact of gas injection on water leakage from damaged pipelines, experimental and simulation studies were conducted on the damaged pipelines. The main objective is to compare the void fraction in intact and damaged states and to investigate the factors influencing and variations in gas–liquid leakage flow rates. The results show that increasing the liquid superficial velocity can reduce the difference in void fraction distribution caused by different damage directions. The leakage flow rate is affected by the direction of damage and the superficial velocity of the respective phase. It has been discovered that gas injection in the pipeline can make it easier to find small‐area pipeline damage. The change rate of the volume void fraction shows different changes with the increase in gas proportion in different directions; the shape of the bubble has no bearing on this change. [ABSTRACT FROM AUTHOR]

Pipeline leakage, which leads to water wastage, financial losses, and contamination, is a significant challenge in urban water supply networks. Leak detection and prediction is urgent to secure the safety of the water supply system. Relaying on deep learning artificial neural networks and a specific optimization algorithm, an intelligential detection approach in identifying the pipeline leaks is proposed. A hydraulic model is initially constructed on the simplified Net2 benchmark pipe network. The District Metering Area (DMA) algorithm and the Cuckoo Search (CS) algorithm are integrated as the DMA-CS algorithm, which is employed for the hydraulic model optimization. Attributing to the suspected leak area identification and the exact leak location, the DMA-CS algorithm possess higher accuracy for pipeline leakage (97.43%) than that of the DMA algorithm (92.67%). The identification pattern of leakage nodes is correlated to the maximum number of leakage points set with the participation of the DMA-CS algorithm, which provide a more accurate pathway for identifying and predicting the specific pipeline leaks. [ABSTRACT FROM AUTHOR]

This study explores a novel approach utilizing acoustic emission (AE) signaling technology for pipeline leakage detection and analysis. Pipeline leaks are a significant concern in the liquids and gases industries, prompting the development of innovative detection methods. Unlike conventional methods, which often require contact and visual inspection with the pipeline surface, the proposed time-series-based deep learning approach offers real-time detection with higher safety and efficiency. In this study, we propose an automatic detection system of pipeline leakage for efficient transportation of liquid (water) and gas across the city, considering the smart city approach. We propose an AE-based framework combined with time-series deep learning algorithms to detect pipeline leaks through time-series features. The time-series AE signal detection module is designed to capture subtle changes in the AE signal state caused by leaks. Sequential deep learning models, including long short-term memory (LSTM), bi-directional LSTM (Bi-LSTM), and gated recurrent units (GRUs), are used to classify the AE response into normal and leakage detection from minor seepage, moderate leakage, and major ruptures in the pipeline. Three AE sensors are installed at different configurations on a pipeline, and data are acquired at 1 MHz sample/sec, which is decimated to 4K sample/second for efficiently utilizing the memory constraints of a remote system. The performance of these models is evaluated using metrics, namely accuracy, precision, recall, F1 score, and convergence, demonstrating classification accuracies of up to 99.78%. An accuracy comparison shows that BiLSTM performed better mostly with all hyperparameter settings. This research contributes to the advancement of pipeline leakage detection technology, offering improved accuracy and reliability in identifying and addressing pipeline integrity issues.

Abstract The evolution and mechanism of ground collapse caused by underground water pipeline leakage have become increasingly significant as more urban areas experience collapses. Based on the principle of similarity, and considering the engineering context of road collapses in Anqing City, Anhui Province, this study designed a 3 m × 2 m × 2 m rupture-collapse model test device. Digital Image Correlation (DIC) technology was employed to investigate the erosion process and collapse mechanisms caused by underground pipeline leakage. The results indicate that groundwater seepage provides the driving force for collapses, combined with the migration space provided by defects, collectively triggering the collapses. When groundwater seepage is minimal, the cohesive forces between soil particles maintain soil stability. As groundwater seepage increases, the soil particle framework is eroded, leading to soil structure destabilization and collapse initiation. The depth of collapse significantly influences stress evolution: stress evolution intensity beneath and above the collapse pit is positively correlated with the distance from the collapse pit bottom, but negatively correlated with the distance from the defect. The research provides insights for the early warning and management of ground collapse.

To alleviate the shortage of natural gas supply, the in-situ conversion of coal to natural gas is more beneficial for advancing the clean and efficient use of energy. Since in-situ coal gas contains complex components, such as H2, CH4, and CO, their leakage poses a serious risk to human life and property. Currently, the area of consequence of the harm caused by a leak in a gathering pipeline transporting in-situ coal gas has not been clarified. Therefore, this paper adopted the method of numerical simulation to pre-study the concentration distribution of each component and determined that the main components of concern are CO and H2 components. Afterward, the diffusion law of in-situ coal gas is analyzed and studied under different working conditions, such as wind speed, temperature, pipe diameter, leakage direction, and leakage aperture ratio. The results indicate that when a pipeline leak occurs, the CO component has the largest influence range. With increasing wind speed, the warning boundary of CO rapidly expands downwind, then gradually diminishes, reaching a peak value of 231.62 m at 7 m/s. The range of influence of the leaked gas is inversely proportional to temperature and directly proportional to pipe diameter and leakage aperture ratio. When the gas leaks laterally, the diffusion early warning boundary value of each component is maximal. Among them, the leakage aperture ratio has a significant impact on the concentration distribution of in-situ coal gas, whereas the effect of temperature is relatively minor. This study contributes to an understanding of the leakage and diffusion characteristics of in-situ coal gas-gathering pipelines. [ABSTRACT FROM AUTHOR]

When water pipelines undergo scenarios such as valve closure or leakage, they often operate in a gas-liquid two-phase flow state, which can easily cause abnormal pressure fluctuations, exacerbating the destructiveness of water hammer and affecting the safe operation of the pipeline. To study the problem of abnormal fluctuations in complex water pipelines, this paper establishes a transient flow model for gas-containing pipelines, considering unsteady friction, and solves it using the discrete gas cavity model (DGCM). It also studies the influence of factors such as valve closing time, initial flow rate, gas content rate, leakage location, and leakage amount on the end-of-valve pressure. Furthermore, it locates the leakage position using a genetic algorithm-backpropagation neural network (GA-BP neural network). The results show that increasing the valve closing time, increasing the gas content rate, decreasing the initial flow rate, and increasing the leakage amount all reduce the pressure peak inside the pipeline. The model constructed using the GA-BP neural network effectively predicts the leakage location with a mean absolute percentage error (MAPE) of 9.26%. The research results provide a reference for studies related to the safety protection of water conveyance projects. [ABSTRACT FROM AUTHOR]

Pipeline leakage seriously threatens the efficient and safe gas drainage in coal mines. To achieve the accurate detection and localization of gas drainage pipeline leakages, this study proposes a gas drainage pipeline leakage detection and localization approach based on the pressure gradient method. Firstly, the basic law of gas flow in the drainage pipeline was analyzed, and a pipeline network resistance correction formula was deduced based on the pressure gradient method. Then, a drainage pipeline model was established based on the realizable k - ε turbulence model, and the pressure and flow velocity distribution during pipeline leakage under different leakage degrees, leakage locations, and pipeline negative pressures were simulated and analyzed, thus verifying the feasibility of the pipeline leakage detection and localization method. It is concluded that the positioning errors of pipeline leakage points under different leakage degrees, different leakage positions, and different pipeline negative pressures were 0.88~1.08%, 0.88~1.49%, and 0.68~0.88%, respectively. Finally, field tests were conducted in the highly located drainage roadway 8421 of the Fifth Mine of Yangquan Coal Industry Group to verify the accuracy of the proposed pipeline leakage detection and localization method, and the relative error was about 8.2%. The results show that with increased pipeline leakage hole diameters, elevated pipeline negative pressures, and closer leakage positions to the pipeline center, the relative localization error was smaller, the localization accuracy was higher, and the stability was greater. The research results could lay the foundation for the fault diagnosis and localization of coal mine gas drainage pipeline networks and provide technical support for safe and efficient coal mine gas drainage. [ABSTRACT FROM AUTHOR]

The evolution and mechanism of ground collapse caused by underground water pipeline leakage have become increasingly significant as more urban areas experience collapses. Based on the principle of similarity, and considering the engineering context of road collapses in Anqing City, Anhui Province, this study designed a 3 m × 2 m × 2 m rupture-collapse model test device. Digital Image Correlation (DIC) technology was employed to investigate the erosion process and collapse mechanisms caused by underground pipeline leakage. The results indicate that groundwater seepage provides the driving force for collapses, combined with the migration space provided by defects, collectively triggering the collapses. When groundwater seepage is minimal, the cohesive forces between soil particles maintain soil stability. As groundwater seepage increases, the soil particle framework is eroded, leading to soil structure destabilization and collapse initiation. The depth of collapse significantly influences stress evolution: stress evolution intensity beneath and above the collapse pit is positively correlated with the distance from the collapse pit bottom, but negatively correlated with the distance from the defect. The research provides insights for the early warning and management of ground collapse. [ABSTRACT FROM AUTHOR]

Effective leak detection and leak size identification are essential for maintaining the operational safety, integrity, and longevity of industrial pipelines. Traditional methods often suffer from high noise sensitivity, limited adaptability to non-stationary signals, and excessive computational costs, which limits their feasibility for real-time monitoring applications. This study presents a novel acoustic emission (AE)-based pipeline monitoring approach, integrating Empirical Wavelet Transform (EWT) for adaptive frequency decomposition with customized one-dimensional DenseNet architecture to achieve precise leak detection and size classification. The methodology begins with EWT-based signal segmentation, which isolates meaningful frequency bands to enhance leak-related feature extraction. To further improve signal quality, adaptive thresholding and denoising techniques are applied, filtering out low-amplitude noise while preserving critical diagnostic information. The denoised signals are processed using a DenseNet-based deep learning model, which combines convolutional layers and densely connected feature propagation to extract fine-grained temporal dependencies, ensuring the accurate classification of leak presence and severity. Experimental validation was conducted on real-world AE data collected under controlled leak and non-leak conditions at varying pressure levels. The proposed model achieved an exceptional leak detection accuracy of 99.76%, demonstrating its ability to reliably differentiate between normal operation and multiple leak severities. This method effectively reduces computational costs while maintaining robust performance across diverse operating environments.

In this study, the ANSYS 2020R1 software simulation is employed to examine the diffusion process of oil leakage and the underground water solute transport law in the Dashagou Yellow River crossing section of the oil pipeline. The simulation results demonstrate that under identical leakage pressure conditions, diesel fuel leakage in powdery, sandy soil is diminished, the emergency window is extended, and the corresponding leakage risk is reduced. In addition, the leakage rate of crude oil is slower than that of diesel oil. After 850 days of downward migration of approximately 190 m, the pollutant reaches quasi-static equilibrium in the big sand ditch. The results of the surface water oil spill analysis demonstrated that the oil film on the river surface migrated for 100 min after the spill, with a thickness that remained between 0.02 and 0.05 mm and a concentration that approached equilibrium.

A method of cold-wave resistance serviceability analysis of the buried municipal water distribution system is proposed. Considering the influence of cold-wave temperature change on the temperature stress of water pipeline and the frozen stress of the soil around the pipeline, the water distribution system model with pipeline joint leakage is established, and the first-order second-moment method is used to calculate the cold-wave resistance serviceability of the water distribution system. Taking one water distribution system as an example, the serviceability of the water distribution system under the blue, yellow, orange and red cold-wave warning is presented respectively. The study shows that, the cold-wave temperature change has a significant impact on the serviceability of buried municipal water distribution system, and code-wave intensity, pipeline joint, and network topology are important factors affecting the serviceability of buried municipal water distribution system. The study may have certain reference value for the hazard resistance design of buried municipal water distribution system. [ABSTRACT FROM AUTHOR]

This study introduces an advanced deep-learning framework for the real-time detection of pipeline leaks in smart city infrastructure. The methodology transforms acoustic emission (AE) signals from the time domain into scalogram images using continuous wavelet transform (CWT) to enhance leak-related features. A Gaussian filter minimizes background noise and clarifies these features further. The core of the framework combines convolutional neural networks (CNNs) with long short-term memory (LSTM), ensuring a comprehensive examination of both spatial and temporal features of AE signals. A genetic algorithm (GA) optimizes the neural network by isolating the most important features for leak detection. The final classification stage uses a fully connected neural network to categorize pipeline health conditions as either ‘leak’ or ‘non-leak’. Experimental validation on real-world pipeline data demonstrated the framework’s efficacy, achieving accuracy rates of 99.69%. This approach significantly advances smart city capabilities in pipeline monitoring and maintenance, offering a durable and scalable solution for proactive infrastructure management.

Effective control of water leakage is a critical aspect for ensuring the high-quality development of the water sector. In recent years, the utilization of sniffer dogs in water leakage detection has emerged as a promising technology, progressing from laboratory experiments to small-scale real-world applications. Leveraging their remarkable ability to trace chlorine, sniffer dogs have demonstrated an impressive accuracy and high efficiency in detecting underground pipe leaks. This mini-review comprehensively examines the advancements in sniffer dog usage for leak detection, provides an overview of various application methods, and elucidates the conditions and limitations associated with each approach. It also delves into the management mechanisms of sniffer dogs, offering a comparative analysis of different management models. Lastly, this review addresses the challenges inherent in applying sniffer dogs to water leak detection, poses pertinent research questions concerning sniffer dogs' training and the expansion of odour fingerprint, and presents a forward-looking perspective on the subject. HIGHLIGHTS This article summarizes sniffer dogs in water supply pipeline leak detection, providing a cost-effective, sustainable solution.; It outlines four operational modes and three management approaches for sniffer dog-based leak detection while discussing challenges and research directions.; The paper underscores sniffer dogs’ versatility as a tool suitable for underdeveloped regions and integration with modern technologies.;

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]

Urban water supply pipelines serve as vital lifelines for urban operations. However, the occurrence of underground pipeline leakage, caused by various factors, results in significant water loss and gives rise to safety hazards such as pavement collapse due to the erosive action of leaking water on the overlying soil. To conduct a more comprehensive investigation into the erosion characteristics of the leaking jet on the soil, this study employed a custom-built soil-test system to investigate the erosive effects of leakage from the water supply pipe network on the clay roadbed above. The study considered water flow rate, leakage port size, and leakage angle as influential factors. The experimental results demonstrated that reducing the water flow rate significantly enhances the soil's erosion resistance. There is a positive correlation between the caliber of pipe leakage, pipe diameter, and the erosion rate of the soil cavity. Under identical conditions, the erosion rate of the specimen increased consistently with an increase in the leakage port angle. The study also investigated and summarized the curve depicting the formation of soil cavities. The aforementioned findings offer valuable insights for the implementation of reinforcement measures using fine-grained cohesive soil backfill in urban water supply pipelines. [ABSTRACT FROM AUTHOR]

When the gas pipeline leaks, it causes huge economic losses. This paper establishes a digital twin model of a pipeline based on the pressure signal generated by a pipeline leak and researches on pipeline leak detection. First, an online updating of the twin model is established to update the data of the physical information space and the parameters of the twin model online. Second, a visual model is established to display the spatial data of physical information of pipelines and output data of the digital twin of pipelines in real-time. If pipeline leakage is identified, an alarm would be triggered and a corresponding emergency rescue plan would be initiated based on the the leakage. Finally, the pipeline leakage identification model can be established by analysing the finite element model of the pipeline, and the sample data were obtained and preprocessed to extract the feature vectors. The training model of the Support vector machine (SVM) was used to classify the working conditions. Theoretical analysis and experimental results show that the method proposed in this paper has high detection accuracy, so it is feasible to judge gas pipeline leakage by using digital twin prediction. [ABSTRACT FROM AUTHOR]

When the gas pipeline leaks, it causes huge economic losses. This paper establishes a digital twin model of a pipeline based on the pressure signal generated by a pipeline leak and researches on pipeline leak detection. First, an online updating of the twin model is established to update the data of the physical information space and the parameters of the twin model online. Second, a visual model is established to display the spatial data of physical information of pipelines and output data of the digital twin of pipelines in real-time. If pipeline leakage is identified, an alarm would be triggered and a corresponding emergency rescue plan would be initiated based on the the leakage. Finally, the pipeline leakage identification model can be established by analysing the finite element model of the pipeline, and the sample data were obtained and preprocessed to extract the feature vectors. The training model of the Support vector machine (SVM) was used to classify the working conditions. Theoretical analysis and experimental results show that the method proposed in this paper has high detection accuracy, so it is feasible to judge gas pipeline leakage by using digital twin prediction.

Transmission mains (TMs) and water distribution systems (WDS) are, among the others, fundamental networks on which society relies on. Age, pipeline materials, and internal and external stresses are just few of the numerous factors that undermine the efficiency of these infrastructures and potentially reduce performance and functionality, leading to leakages or causing dangerous conditions in the surrounding environment, such as flooding or disruption. Accurate condition assessment, with suitable monitoring techniques, allows for proper management and maintenance throughout the infrastructures' lifetime. Starting from the hypothesis that leakages could be related to soil deformation, this study evaluated the opportunities offered by the differential interferometric synthetic aperture radar (DInSAR) method in identifying soil deformations in nonurban areas potentially related to leak positions along TMs. Satellite images were analyzed to derive millimetric evolution of slow deformation processes over large surfaces and a long time frame. To test the capability of the proposed method and its operability as a monitoring tool, data acquired between 2014 and 2019 by Sentinel-1 satellites, were processed to investigate two transmission pipelines located in two areas of Italy (Veneto and Toscana regions). An analysis of historical damages in the hydraulic network and soil deformations obtained by synthetic aperture radar (SAR) data examination along the pipeline path suggests how and in which conditions potential relationships between leakages along pipeline and deformations of the ground surface can be defined. [ABSTRACT FROM AUTHOR]

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. This work is expected to provide a valuable reference for the safe operation and risk prevention of HENG pipelines in the future. [ABSTRACT FROM AUTHOR]

Leakage from water distribution systems have significant economic and environmental impacts. Reducing leakage from distribution systems has favorable effects on the environment and energy consumption. This paper aims to understand the implications of orifice hydraulic and geometric characteristics on leakage flow. An experimental model was thus designed and built to simulate leaking water pipes with different defects. Various parameters were considered, including the size and shape of the orifice, Reynolds number (Re), and cavitation development in the orifice. During the tests, water was allowed to flow through the defects at controlled pressures while observing leak behavior. The results showed that the discharge coefficient (Cd) exhibited an extensive range (0.35–0.88) depending on the size and shape of the orifice and the flow conditions. The impact of cavitation can be so significant that it leads to variations in the discharge coefficient that are larger than those occurring with Re. A model is then presented to predict leakage rates at different flow conditions (i.e., cavitating and non-cavitating flow). Comparing the results shows a good fit between projected and measured flow values. [ABSTRACT FROM AUTHOR]

The rapid pace of urbanization has led to an increasing frequency of road collapses, posing a significant threat to urban traffic safety. Underground pipeline leakage stands out as the primary cause of such collapses. This paper presents a macroscopic analysis of the subgrade seepage erosion process caused by pipeline leakage. Model tests were conducted to investigate the formation mechanism and explore the influence of water level, water flow rate, and soil type. The study revealed that the subgrade seepage erosion caused by pipeline leakage undergoes four distinct stages: infiltration, slow erosion, rapid erosion, and erosion convergence. Soil erosion shares similarities with sand erosion in its developmental process. The water level plays a pivotal role in determining the shape and size of the eroded area caused by sand seepage erosion. The size of the erosion cavities formed during the soil seepage erosion increased along with the increase in the water flow rate. The size of the erosion cavity increased by up to 55.7% when the flow rate was increased by three times. In addition, clay soils do not undergo significant erosional damage but do produce significant settlement. The soil erosion process caused by underground leakages in pipelines was investigated using model tests in this study, which provided valuable information for researchers performing an in-depth analysis of the mechanism of roadbed cavities generated by urban underground pipeline leakage, which is critical for safeguarding people's travel safety and decreasing social and economic losses. [ABSTRACT FROM AUTHOR]

Abstract The sCO2 Brayton cycle has gained interest because of its flexibility and ability to provide higher thermomechanical conversion efficiency. Studies on failure conditions and corresponding emergency measures are of great significance to ensure the safety of the system, while there are limited studies related to the sCO2 Brayton cycle because the test circuit has not been applied in practice at present. In this study, we have developed a dynamic model of the CO2 Brayton cycle, and carefully validated its components against experimental data. Preliminary safety assessment and dynamic response under loss of cooling water (LOCW), loss of heat source (LOHS), and pipeline leakage have been analyzed. Emergency strategies are proposed to maintain the safe operation under failure conditions. The primary objective of this study is to reveal the dynamic performance and emergency measures under failure conditions of the sCO2 Brayton cycle. The results demonstrate that the system net power decreases at rates close to 10%/s and less than 0.1%/s under LOCW and LOHS, respectively. The system responds more dramatically under LOCW compared with LOHS. The system needs emergency measures under LOCW, while it has sufficient time to realize a normal slow shutdown under LOHS. The leakage rate is at least five times higher in the high‐pressure pipeline compared with the low‐pressure pipeline for the same area of the leakage hole. Under pipeline leakage, the heat source should be cut‐off slowly, and the rotation speeds of the main compressor and recompressor should be reduced simultaneously to protect the system.

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]

Hydrate blockage and pipeline leakage are two common factors that threaten the safety of natural gas pipelines. However, most of the current research focuses on nonintrusive, passive-like techniques that can only detect one of these abnormal events, with occasional attention to identification technique. This paper introduces an active method to simultaneously detect hydrate blockage and pipeline leakage using intrusive sensors, and further presents a deep forest-based classification method for two types of abnormal events, which aims to avoid the problem that the classification of traditional deep learning depends on huge number of hard-to-acquire samples. Besides, network structure and parameters in deep learning affect the classification performance, and deep forest is just a better solution to this problem. The parameter tuning experiments results of deep forest show that the classification accuracies are mostly 100% whatever in training and testing, proving that different parameter settings have little effect on the classification accuracy. The stability and portability of the classification method are tested, and it is verified that this classification method is easy to implement and has strong universality, which is expected to be applied to other types of natural gas pipeline event classification. [ABSTRACT FROM AUTHOR]

Monitoring the leakage status of water pipes is of great importance for intelligent water services. In this work, the leakage status of the water pipeline was monitored in real time using a fiber optic distributed acoustic sensing (DAS) system, and the experimental study was conducted based on the mathematical modeling method of the response surface, which systematically reveals the quantitative relationship between water delivery pressure, leakage hole diameter, leakage location, and the flow rate at the leakage hole of water pipeline as well as the measured phase change of light. A theoretical mathematical model for assessing and warning the risk of water pipeline leakage was established. The results show that the water delivery pressure, leakage hole diameter, and leakage location are significantly influential on the flow rate and the phase change of light at the leakage hole. The degree of the influence by these three factors on the phase change of light and flow rate at the leakage hole is ranked as follows: leakage hole diameter > water delivery pressure > leakage location. The larger the leakage hole diameter, the higher the water delivery pressure, and the closer the leakage location is to the pipeline delivery port, the larger the phase change of light and flow rate at the leakage hole. The theoretical value of the model is highly consistent with the actual measured value with relative error less than 4% after experimental verification, which indicates that the predictability of the model works well, and is capable of accurately predicting and assessing the leakage status of the water supply pipeline network. [ABSTRACT FROM AUTHOR]

Considering the problem of difficulty in transmission and storage due to a large amount of data in the water-supply network monitoring system based on a wireless sensor network (WSN), we propose a sparse representation of the water-supply network monitoring data by using compressed sensing (CS) method. At the same time, aiming at the problem of low leakage identification accuracy caused by information loss under compressed sensing, we propose a leak identification method for a water-supply pipe network based on compressed sensing and deep residual neural network (ResNet). Firstly, under the condition that the observation matrix ensures the integrity of signal information, the compressed sensing theory is used to compress and observe leakage signals to obtain observation data, to reduce the redundant information and volume of the data. At the same time, the observation data is preprocessed to realize the transformation of a one-dimensional signal to a two-dimensional matrix. Then the residual neural network is trained by using the two-dimensional data to realize the automatic, efficient, and accurate leak identification under different leakage apertures. Experimental results show that the proposed method can obtain relatively high accuracy and greatly reduce the training time of ResNet by using compressed data. When the Compression rate (CR) is 70% and the observation matrix is a Gaussian random matrix, the average accuracy is 96.67% and the training time is only 50% compared to uncompressed data. The research work provides a new intelligent leak identification under different leak apertures using WSN and has important application prospects in saving water resources.

Shield tunneling is widely used in urban subway tunnel construction. Old urban underground pipelines generally have small leakages that are difficult to find. The water leakage significantly reduces the stability of the stratum, posing a threat to the safety of tunnel shield construction. Therefore, this study established 2D and 3D calculation models for analyzing the law of the leakage diffusion in the ground under water pressure, and the influences of the pipeline leakage range and leakage length on the changes in ground settlement during shield tunneling. The 2D model calculation results show that seepage water mainly diffuses vertically under gravity. As the pipeline leakage gradually reaches a predetermined depth, the simulation results tend to be consistent with the test results. The 3D model is more accurate than the theoretical solution in predicting the ground settlement because it can consider the influences of repeated disturbances in twin tunnel shield construction. The maximum ground surface settlement increases with the extent of the leakage length and leakage range, and the range is the main factor determining the settlement. At the interior of the ground, the seepage water has a greater impact on areas with strong disturbances and large soil losses. [ABSTRACT FROM AUTHOR]

The sCO2 Brayton cycle has gained interest because of its flexibility and ability to provide higher thermomechanical conversion efficiency. Studies on failure conditions and corresponding emergency measures are of great significance to ensure the safety of the system, while there are limited studies related to the sCO2 Brayton cycle because the test circuit has not been applied in practice at present. In this study, we have developed a dynamic model of the CO2 Brayton cycle, and carefully validated its components against experimental data. Preliminary safety assessment and dynamic response under loss of cooling water (LOCW), loss of heat source (LOHS), and pipeline leakage have been analyzed. Emergency strategies are proposed to maintain the safe operation under failure conditions. The primary objective of this study is to reveal the dynamic performance and emergency measures under failure conditions of the sCO2 Brayton cycle. The results demonstrate that the system net power decreases at rates close to 10%/s and less than 0.1%/s under LOCW and LOHS, respectively. The system responds more dramatically under LOCW compared with LOHS. The system needs emergency measures under LOCW, while it has sufficient time to realize a normal slow shutdown under LOHS. The leakage rate is at least five times higher in the high‐pressure pipeline compared with the low‐pressure pipeline for the same area of the leakage hole. Under pipeline leakage, the heat source should be cut‐off slowly, and the rotation speeds of the main compressor and recompressor should be reduced simultaneously to protect the system. [ABSTRACT FROM AUTHOR]

Leaks in pipelines caused by acid are destructive to both economic growth and capital which should be avoided at all costs. Damage to underground pipelines is caused by a hard-to-find leak, the unavailability of a real-time monitoring system and the lack of a pipeline history database. The aim of this work is to develop an early warning system to detect acid leaking in the pipeline using Internet of Things (IoT) technology. To detect changes in the pH of acid soil parameters near the pipeline, two mechanisms are required: first, to provide an early warning before the leak is detected and second, to report the occurrence of the leak. The notification system is equipped with three LED indicators, each showing the offline, online and signal detection status. The novelty of the work is a prototype that can detect the acid leak in the pipeline and record the pH values in a database for future research. Using continuous pH monitoring, real-time analysis and a database, this system can detect leaks before they become a major problem. Consequently, the manufacturing industry will benefit from this initiative as it is automated, efficient and cost-effective. [ABSTRACT FROM AUTHOR]

In order to effectively evaluate the risks of emergency repair of long-distance gas pipeline leakage, a dynamic AHP-based risk assessment model was established based on the Analytic Hierarchy process(AHP), with the risk indexes classified into five categories, and the updating algorithm of membership degree of various risk indexes was also described.Meanwhile, by analyzing the risks of emergency repair in the leakage accidents of gas pipelines, a dynamic AHP-based risk assessment evaluation index system was established, and the secondary indexes were classified. In addition, the dynamic evolution of risks faced in the overall construction period of emergency repair was analyzed with the dynamic AHP-based risk assessment model based on a leakage accident of Sichuan-East Gas Pipeline. The results show that: the changes of the risk indexes during emergency repair can cause the changes of the membership degree of the AHP underlying index and the index weight of each layer, which will further result in the risk change in the entire emergency repair process. Therefore, the model could effectively and dynamically evaluate the risks of emergency repair of gas pipeline leakage. Thus, the research results could provide basis for the emergency repair of natural gas pipeline leakage thereof.

For the problem that the conventional oil pipeline detection system cannot effectively detect the leakage of the two-phase flow pipelines, a leakage identification method based on the acoustic emission signal was proposed for the gasliquid two-phase flow pipelines. According to the basic principles of acoustic emission detection technology, the leakage detection tests were carried out for the three flow patterns of laminar flow, slug flow and annular flow at a gas pressure of 0.1 to 0.4 MPa in three different orientations of leak holes with two different diameters. The main signal components extracted by wavelet packet decomposition combined with local mean decomposition were taken as the input of pattern recognition, and the BP artificial neural network pattern recognition was also performed. The test results show that the average accuracy of the leakage identification of the three two-phase flow pipelines of laminar flow, slug flow and annular flow is 83.5%, which is high in accuracy. Further, the proposed method has a reference value for the leakage detection of gas-liquid twophase flow pipelines.

The rapid pace of urbanization has led to an increasing frequency of road collapses, posing a significant threat to urban traffic safety. Underground pipeline leakage stands out as the primary cause of such collapses. This paper presents a macroscopic analysis of the subgrade seepage erosion process caused by pipeline leakage. Model tests were conducted to investigate the formation mechanism and explore the influence of water level, water flow rate, and soil type. The study revealed that the subgrade seepage erosion caused by pipeline leakage undergoes four distinct stages: infiltration, slow erosion, rapid erosion, and erosion convergence. Soil erosion shares similarities with sand erosion in its developmental process. The water level plays a pivotal role in determining the shape and size of the eroded area caused by sand seepage erosion. The size of the erosion cavities formed during the soil seepage erosion increased along with the increase in the water flow rate. The size of the erosion cavity increased by up to 55.7% when the flow rate was increased by three times. In addition, clay soils do not undergo significant erosional damage but do produce significant settlement. The soil erosion process caused by underground leakages in pipelines was investigated using model tests in this study, which provided valuable information for researchers performing an in-depth analysis of the mechanism of roadbed cavities generated by urban underground pipeline leakage, which is critical for safeguarding people’s travel safety and decreasing social and economic losses.

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]

Detection of minor leaks in oil or gas pipelines is a critical and persistent problem in the oil and gas industry. Many organisations have long relied on fixed hardware or manual assessments to monitor leaks. With rapid industrialisation and technological advancements, innovative engineering technologies that are cost-effective, faster, and easier to implement are essential. Herein, machine learning-based anomaly detection models are proposed to solve the problem of oil and gas pipeline leakage. Five machine learning algorithms, namely, random forest, support vector machine, k-nearest neighbour, gradient boosting, and decision tree, were used to develop detection models for pipeline leaks. The support vector machine algorithm, with an accuracy of 97.4%, overperformed the other algorithms in detecting pipeline leakage and thus proved its efficiency as an accurate model for detecting leakage in oil and gas pipelines. [ABSTRACT FROM AUTHOR]

To understand the pipeline leakage characteristics of the large-scale main gas drainage system in coal mines and the multi-physical fields of the gas flow in pipeline, a three-dimensional numerical model is developed based on Ansys CFX and the process of gas drainage pipeline leakage is investigated.The influence of different leakage positions, different leakage intensities, and different negative pressures of the pumping station on the gas flow in the gas drainage pipeline is studied.The results show that the leakage intensity and the negative pressure are the important factors affecting the internal gas flow field in the gas drainage pipeline, and the leakage occurring near the pumping station of main gas drainage system is prone to be captured easily.The results of this paper are expected to provide technical support for the safety prevention and control of pipeline leakage accidents in the main gas drainage system and also suggestions for the optimization of the allocation of safety prevention and control resources.

At present, most of the existing positioning methods of underground gas extraction main pipeline leakage in coal mines use single positioning method. The problems of these methods include poor applicability, low efficiency and susceptibility to environmental impact. In order to solve the above issues, this paper proposes a leakage positioning method of underground gas extraction main pipeline based on transient model by combining flow balance method, negative pressure wave method and wavelet noise reduction principle. Firstly, the method analyzes the flow change law and the difference of the both ends of of the pipeline, eliminate the interference signal, use the flow balance method to identify the pipeline leakage state and determine whether the pipeline is leaking. The method compares the flow changes under normal operation and leakage. When the difference between the both ends of the pipeline flow is greater than the threshold value of the pipeline flow difference, it means that the working conditions have changed or leakage occurs. Secondly, according to the propagation mechanism of negative pressure waves in the pipeline, wavelet noise reduction technique is used to denoise the pressure signal. The neighborhood interpolation method is applied to seek the signal mutation point. Finally, the leak location formula is used to calculate the leak position so as to obtain the leak position of the pipeline effectively. The simulation analysis results verify the accuracy of the method.