Source Of Leakage Research Articles

Numerical Simulation Study on Reverse Source Tracing for Heating Pipeline Network Leaks Based on Adjoint Equations

In order to identify the leak source in complex heating pipeline networks, a timely and effective simulation of the leakage process was conducted. The open-source computational fluid dynamics software OpenFOAM 5.0 was combined with the PISO algorithm to simulate the pressure during the leakage in water supply networks, transforming the reverse source tracing problem into the solution of an adjoint equation. The validation of the transient adjoint equation for single-phase flow was completed through simulation, and the pressure wave change graph at the moment of the network leakage was solved, which was consistent with the experimental results. Using the open-source finite element analysis software OpenFOAM 5.0, the positioning accuracy of pipeline leak points can be controlled within the range from 92% to 96%. Based on the pressure wave change graph, the position of the leak source in the complex network was determined using the reverse source tracing method combined with the second correlation theory. The results show that the calculation speed of the PISO algorithm combined with the adjoint equation is significantly better than that of the individual SIMPLE and PISO algorithms, thereby proving the superiority of the adjoint method.

Hygrothermal and airtightness performance assessment of prefabricated lightweight wall systems for cold climates

Wooden prefabricated wall panels (WPWP) are increasingly used in the residential sector due to their practical and environmental benefits compared to traditional on-site systems. However, there is limited information regarding their influence on the building envelope´s performance, particularly at panel junctions. Therefore, this study investigated the hygrothermal and airtightness performance of two WPWP systems, one standard and one optimized, focusing mainly on panel-to-panel junctions. Laboratory heat and vapor transfer, thermography, and airtightness assessments were conducted over two full-scale prefabricated walls and a reference on-site traditional wall. In addition, hygrothermal simulations were carried out utilizing WUFI Pro, to compare laboratory heat and vapor transfer results with theoretical values. The results showed that the prefabricated walls reduced heat loss through the air leakage sources of the junctions by up to 17.8% and a reduction of air infiltration by up to 76%, compared to the on-site reference. It was also found that using polyurethane foam in panel junctions generated thermal benefits. Still, care should be taken with the amount of polyurethane foam used to avoid increasing relative humidity. In addition, bio-based insulation (hemp fiber) used in the optimized prefabricated wall exhibited adequate hygrothermal performance as it slightly improved the thermal resistance without increasing the risk of interstitial condensation. Overall, the findings demonstrated that wooden prefabricated wall systems can significantly reduce heat loss and air infiltration, highlighting their potential as a hygrothermally efficient alternative to traditional construction methods.

Fast search for toxic gas leakage on offshore platforms based on deep learning methods

In the context of addressing dangerous gas leakage on offshore platforms, the prompt identification of leakage sources is paramount for implementing targeted emergency measures. This paper introduces a rapid leakage source location method leveraging a preexisting database on gas leakage and dispersion consequences, coupled with a deep learning approach. Initially, latin hypercube sampling (LHS) and computational fluid dynamics (CFD) are employed to simulate the consequences of gas leakage and dispersion on offshore platforms. Subsequently, a neural network model, specifically a long short-term memory (LSTM) model, is trained using concentration data. This trained model facilitates the rapid localization of the leakage source based on real-time detector data. Furthermore, optimization of the model is conducted through an analysis of various hyperparameters. Finally, the efficacy of the proposed methodology is demonstrated through a comparison between predicted results and actual operating conditions. This approach enables swift and precise identification of leakage sources during incidents, thereby facilitating a more efficient emergency response.

Numerical Simulation and Experimental Investigation on Friction and Lubrication Behaviors of Chevron Micro-Textured Valve Plate/Cylinder Block Interface With Various Area Densities

Abstract As the critical friction pairs of swashplate-type axial piston pumps, the cylinder block/valve plate lubricating interface is the primary source of friction, wear, and leakage in axial piston pumps. Surface micro-texturing has been widely employed in cylinder block/valve plate conjunctions to ameliorate tribological performance. However, little research has been conducted to investigate the influence of chevron micro-texture area density on the tribological behaviors and lubricating properties of cylinder block/valve plate interface both experimentally and numerically. In this article, the chevron micro-textures with various area densities were manufactured on H62 brass by micro-milling and subsequently characterized with a laser scanning confocal microscope (LSCM). The friction and wear performance of H62 brass/38CrMoAl conjunctions were obtained via disc-on-disc tribological tests to simulate the cylinder block/valve plate lubricating interface. Moreover, the load-carrying capacity of untextured and chevron micro-textured samples was numerically investigated under hydrodynamic lubrication. It was found that the chevron micro-textured surface with an area density of 30.1% displayed the lowest friction coefficient and the shallowest wear depth. Although the load-carrying capacity of chevron micro-textured samples with a high dimple area density was larger, the severe stress concentration induced by the reduced micro-texture spacing caused the increment and large fluctuations of friction coefficient.

Early-stage identification of indoor leakage sources in factories

Identifying the leakage source in industrial factories as soon as an incident happens is crucial for controlling the leak in time and safeguarding lives and property. This study developed a rapid source identification method for factories, aiming to identify the characteristics of leakage sources at the early stage of the leakage event. The approach integrates pollutant diffusion theory, sliding window method, correlation coefficient method, and data fitting method. Based on an experimental cabin, 162 verification conditions were established, encompassing constant sources as well as two time-varying sources: linear and non-linear ones. Additionally, various aspects of the method were evaluated. It was found that considering the error and noise in sensor monitoring data, the method exhibits identification accuracies of 83.3%, 79.6%, and 74.1% for constant sources, linear sources, and non-linear sources, respectively. In the case of non-linear sources, the coefficient “b” must be taken into account when generating the dimensionless concentration vector. A sliding window containing 50 to 60 valid data points can achieve the highest source identification accuracy. This research can provide help in quickly locating leakage sources in factories, help with the timely control of leaks, and protect workers’ health and property safety.

Dynamic evolution of flame and overpressure of leakage and explosion of hydrogen-blended natural gas in utility tunnels

In order to ascertain the true characteristics of the leakage explosion loads of hydrogen-blended natural gas (HBNG) in utility tunnels, the influence of leakage location on the concentration field distribution of HBNG leakage in utility tunnels was studied through the CFD technique. Furthermore, the characteristics of explosion flame and overpressure load distribution of HBNG of inhomogeneous distribution under effect of different ignition delay times and ignition positions were also investigated. The results show that the greater the distance between the leakage location and the air outlet, the more readily HBNG can propagate throughout the utility tunnel. This, in turn, leads to more prominent downstream gas cloud stratification. The development of explosion flame is primarily seen in the direction of low resistance and ample space within the utility tunnel. With the increase in ignition delay time (tid) and the backward shift of the leakage location (Llk), the average flame velocity generally exhibits a decreasing trend. Overall, the peak overpressure exhibits a concave trend. Under circumstances where the leakage source is in the front or middle section of the tunnel, the average peak overpressure demonstrates a general tendency of increase followed by decline with the increase in tid. In comparison, in the case of a leakage source located at the back end, it decreases gradually as tid increases. The most severe explosion overpressure load is readily induced when the leakage source is located at the front end. In the case of a leakage source located in the front section of the tunnel, the overpressure load at each measuring point decreases continuously and then rises as the ignition position moves backward. However, in the case of a leakage source located in the rear section of the tunnel, the average value of peak overpressure induced by ignition in the middle of the tunnel is the greatest, while the average value of peak overpressure induced by ignition at the back end is the smallest. The maximum explosion overpressure load is found at the front-most leakage source under front-end ignition conditions. The findings of this study offer scientific substantiation for the structure of the utility tunnel, which is designed for explosion withstanding, prevention, and control as well as risk assessment.

Improved fast deconvolution algorithms based on functional beamforming for gas leakage sound source imaging

Fast deconvolution algorithms have high computational efficiency, but they often overlook spatial variations in the point spread function (PSF) and wrap-around errors, resulting in the degradation of sound source imaging performance. Accordingly, this paper proposes two improved fast deconvolution algorithms based on functional beamforming (FB). The proposed algorithms establish a linear equation involving the FB output, sound source distribution, and raised-power spatial shift-invariant PSF, specially with irregular focus grid point. Furthermore, iterative solutions are obtained based on fast Fourier transform, enhancing imaging performance while maintaining computational efficiency. Simulations and experimental results demonstrate that compared to the fast deconvolution algorithms, the proposed algorithms expand the effective imaging area to 36.3°, and the Rényi entropy is reduced by approximately 45 %, which has higher imaging spatial resolution and dynamic range. Finally, the compressed gas leakage source imaging results validate the effectiveness of the proposed algorithms, indicating that they have promising engineering application prospects.

Analysis of hydrogen leakage characteristics and hazard assessment of hydrogen production container

The compactness and flexibility of hydrogen production containers make them suitable for integration in photovoltaic or wind power stations, yielding versatile applications. However, these enclosed spaces pose a high risk of hydrogen leakage, necessitating thorough analysis of leakage characteristics and exhaust strategies. This study employs a three-dimensional CFD simulation to assess the hazards of hydrogen leakage within a 42 m3 hydrogen production container. Considering its operational context, we examine scenarios of minor and severe hydrogen leaks, evaluating vent fan effects and supplementary ventilation during slight and heavy leaks. Two common scenarios are studied: routine operations with slight hydrogen leakage and emergency situations involving minor leaks. By comparing single longitudinal and combined ventilation approaches, we derive the optimal strategy. Results reveal negligible combustion risk for slight leaks (≤0.6 g/s), with the top exhaust fan reducing combustible area by >99.5 %. Conversely, heavy leaks (>24 g/s) necessitate additional ventilation beyond top fan enhancement. However, maintaining stable room temperature during daily operations requires managed ventilation areas. Ultimately, for significant hydrogen leaks, the optimal solution entails combined ventilation with a blower directed at the leak source. This research underscores the critical importance of addressing hydrogen leakage hazards in enclosed spaces for enhanced safety and efficient operations.

Simulation and Analysis of Diffusion Process for Ammonia Leakages

Abstract Ammonia is a chemical widely used in the industrial production, but the leakage during storage and transportation can lead to serious health and environmental risks. This paper investigates the ammonia leakage diffusion process as represented by the Gaussian plume model using MATLAB. The spatial distribution, plane figure and iso-concentration line are analyzed to examine the effects of the height, wind speed and atmospheric stability on diffusion. The conclusion shows that the ammonia concentration and the hazard range steadily decrease with an increase in the effective height of the leaking source. As the wind speed increases, the intensity of turbulence increases, the concentration of the highest point decreases, and the scope of the hazardous area decreases. As the degree of atmospheric stability improves, the affected area gradually grows larger and the hazardous threat rises.

YOLOGAS: An Intelligent Gas Leakage Source Detection Method Based on Optical Gas Imaging

YOLOGAS: An Intelligent Gas Leakage Source Detection Method Based on Optical Gas Imaging

Data-driven source term estimation of hazardous gas leakages under variable meteorological conditions

Source term estimation (STE) of hazardous gas leakages in chemical industrial parks (CIPs) is important for addressing environmental pollution and improving engineering safety and reliability. For this purpose, some least squares-based STE methods have recently been developed, which performs the real-time STE using an off-line determined response matrix that represents the relationship between the sensor measurements and strengths of hazardous gas leakages. However, these methods require the number and locations of potential hazardous gas leakage sources are known a priori, which is difficult in many practical applications. To resolve this issue, in the present study, a novel multi-sensor data-driven STE (MSDD-STE) approach is proposed, which overcomes the difficulties with existing least squares-based STE methods and can, for the first time, address the MSDD-STE problems in complicated scenarios where meteorological conditions such as wind directions change over a considerable range. A detailed analysis is introduced to evaluate the performance of the proposed approach. The effectiveness of the proposed approach is verified by comprehensive numerical simulation studies.

A novel hybrid information-based PF-WOA algorithm for gas source localization in 3D space

Abstract In recent years, dangerous gas leakage events occur frequently. Rapid and accurate location of gas leakage sources by mobile robots is the key to avoid the expansion of disasters. In order to solve the problem of discontinuous gas concentration gradient and sparse gas environment in three-dimensional space, particle filter, and whale swarm optimization algorithm are integrated to locate gas source. Firstly, the Z-shape search and comb search are used to locate the plume, and then, the particle filter algorithm is combined with the whale optimization method to guide the particle movement, and the random inertial disturbance term is designed to improve the convergence speed and search accuracy of the algorithm. Experimental results in three-dimensional environment show that the proposed information-driven particle filter whale optimization hybrid algorithm effectively guides the robot in localizing gas source within a certain range, significantly enhancing both the efficiency and accuracy of localization compared to other algorithms.

Gas Leakage Risk Management Of Biogas Powerplant Using Aloha Gas Dispersion Modelling And Bowtie Analysis

Biogas, a fuel derived from organic waste, has gained popularity as a sustainable energy source. The adaptability of biogas allows its utilization for electricity and heating. This study aims to evaluate the risk of biogas leakage using ALOHA gas dispersion modeling and Bowtie analysis to identify hazard zones and evaluate the effectiveness of preventive and mitigation measures. The research method using Gas dispersion modeling in ALOHA software can simulate the spread of leaked biogas, identify potential hazard zones, and inform emergency response planning. The results show that gas dispersion reaches up to 296 meters from the leak source in a worst-case scenario without ignition, with significant implications for safety. The ignition scenario produces high thermal radiation, further exacerbating the risk. In conclusion, these findings highlight the intolerable risk of biogas leakage, requiring comprehensive risk mitigation measures. This study recommends the implementation of a robust control system, routine maintenance, and emergency response plan to ensure the safety and sustainability of biogas facility operations

CNN for scalar-source distance estimation in grid-generated turbulence

Countermeasures against material leakage in industrial plants call for rapid leak source estimation techniques based on limited downstream information. Assuming that such scalar diffusion occurs in a turbulent environment, it is necessary to extract features from its instantaneous local concentration distribution and estimate the distance from the observation point to the source. To this end, we proposed an estimation method using a supervised deep learning of a convolutional neural network (CNN) and investigated its estimation performance in grid-generated turbulence fields. The grid turbulence fields were reproduced by direct numerical simulations at three Reynolds numbers (Re), and the CNN was trained using two-dimensional distribution images of passive scalars downstream. We confirmed that the images large enough to cover the spatial integral length scales of the scalar resulted in high estimation accuracy. For images with a coarsened brightness gradation, the small-scale feature of scalar fluctuations was lost, and the CNN attained a low estimation accuracy. By training the CNN with high-Re images, a high estimation accuracy was obtained, even for low-Re images. The opposite case (low-Re training and high-Re estimation) yielded low accuracy, where the CNN significantly underestimated the distance against the source. Based on these results, we discussed the generalizability and essential features to be learned by the CNN.

Machine learning-Based method for gas leakage source term estimation in highway tunnels

Incidents involving the leakage of flammable and explosive chemicals in highway tunnels can lead to disastrous events such as fires and explosions. The distinctive features of highway tunnels, characterized by their confined and elongated structure, intensify the complexity of managing such accidents, potentially resulting in significant casualties and property damage. Rapid detection and acquisition of leakage source information during a flammable gas leak in a highway tunnel are of utmost importance for effective accident management. This study introduces a predictive model developed using a machine learning approach. The model uses time series data from gas concentration sensors and anemometers installed within the tunnel to predict parameters such as gas leakage velocity, source location, and initiation time. A computational fluid dynamics simulation of gas leakage within the tunnel was constructed, and a dataset was generated using sample generation and preprocessing methods. The results demonstrate that this method exhibits robust predictive performance. Furthermore, a prediction strategy is proposed to enhance the model’s predictive accuracy and resilience to data noise. This model offers valuable insights for improving accident management strategies for gas-related incidents in highway tunnels.

Reappraisal of the Previously Described False Localizing Sign at C1-2 in Cases of Spontaneous Intracranial Hypotension

BACKGROUND AND OBJECTIVES: We present an illustrative case of spontaneous intracranial hypotension (SIH) in the setting of a suspected C1-2 cerebrospinal fluid (CSF) leak that was successfully treated with muscle, collagen, and epidural blood patch. We examined the literature to identify similar cases reporting Cl-2 retrospinal fluid collections identified on imaging in the setting of SIH and quantified the success of targeted treatment to this area despite previous reports that caution about a “C1-2 false localizing sign.” METHODS: A systematic search was performed identifying cases of SIH resulting from CSF leak with C1-2 fluid collection observed on imaging. PubMed, Google Scholar, and Web of Science were queried, and articles were screened for possible inclusion by 2 authors and supervised by the senior author. RESULTS: In total, 28 studies were included with a total of 32 patients. The number of patients in each study with C1-2 fluid collections, number of patients with fluid collections at multiple levels, specific intervention used, and outcomes of each intervention were recorded, with a focus on whether treatment occurred at the levels exhibiting fluid signal. CONCLUSION: Although the C1-2 fluid signal in SIH has previously been described as a “false localizing sign,” our study indicates that treating this level as the source of CSF leak results in successful and durable outcomes. Most SIH cases with signal at C1-2 did not have a fluid signal at any other level and were treated successfully and most commonly through epidural blood patch at the C1-2 level. Symptom resolution was also reported after direct repair of C1-2 CSF leaks through primary closure, Gelfoam patch, and muscle fragment with fibrin. In patients with SIH, C1-2 fluid signal, and no other source of CSF leak identified on imaging, surgical intervention at the C1-2 level seemed to have a high success rate.

Leakage identification and correlation coefficient method for industrial workshop production process combining with computational fluid dynamics

Identifying leakage sources in industrial factory production is crucial to improving air quality, ensuring people’s health and safety and preventing safety accidents. In this study, a method for leakage source identification in industrial factories combining with computational fluid dynamics (CFD) and correlation coefficient was proposed and validated. The study first experimentally validated the numerical methods, which were fundamental to the leakage identification method. Then impacts of leakage sources, sensor errors and number of sensors on the source identification results were evaluated. The results showed that the identification accuracy could be significantly improved by refining the step size of the coefficient a r in this method. When the number of leakage sources was unknown, the accuracy of this method in identifying the number and location of leakages was 93.5%. The computation time spent on source identification depended on the maximum number of leakage sources. Using four sensors with errors were enough to identify the number of unknown leakage sources. The number of leakage sources did not exceed three at the same time. Overall, coupled CFD and correlation coefficients method could effectively identify the number, location and intensity of leakages.

3D reconstruction of gas cloud concentration field with high temporal and spatial resolution based on an imaging-type FTIR.

Imaging-type FTIR devices provide numerous benefits for the detection and alarm of hazardous gases. This paper presents an improved algorithm for reconstructing the 3D concentration field of gas clouds, utilizing hypothesis testing and a synchronized algebraic iteration algorithm. Specifically designed for use with imaging-type FTIR devices, this algorithm enables rapid reconstruction of gas cloud concentration fields. Using CFD software, an open-space detection scenario for HFC-152a gas was simulated, and the 3D concentration field was reconstructed from dual-angle column concentration data. The accuracy was confirmed, with a deviation of less than 4.6% in re-projected column concentrations along the center streamline and a maximum deviation of 8.8% between simulated and reconstructed voxel concentrations. Laboratory experiments further validated the algorithm. Two sets of line-of-sight angles yielded similar average total mass results calculated from the continuously reconstructed concentration field, measuring 7285.8 mg and 7310.1 mg, with relative standard deviations of 2.4% and 2.7%, respectively. In an open field, an experimental detection of HFC-152a gas leakage was conducted. The algorithm employed facilitated the 3D reconstruction and precise localization of the gas leak source, which underscores the algorithm's versatility across various environmental contexts and its utility in determining the source of gas leaks. The lab and open field experiments share a same temporal resolution of 2.9 seconds. The algorithm proposed in this article effectively expands the practicality of imaging-type FTIR devices for real-time gas leak monitoring applications.

Deep learning-based source term estimation of hydrogen leakages from a hydrogen fueled gas turbine

Hydrogen fueled gas turbine is an efficient and environmentally friendly energy conversion equipment. However, it is prone to gas leakages from corrosion-prone components and pipe connections. In order to address gas leakage problems, source term estimation (STE) can be applied to estimate the location and intensity of the leakage sources. Traditional STE methods are based on an optimization procedure using an atmospheric transport and dispersion model, which requires considerable computation efforts and cannot meet the need of real-time implementation. The complex flow field around the gas turbine, the possible existence of multiple leakage sources, and the high-dimensional time series data further increase the difficulty of the STE problem. To address these challenges, in the present study, a deep learning based STE approach is proposed. The basic idea is to use long short-term memory auto-encoder (LSTM-AE) network to extract the encoded features of multi-sensor data and then use a deep neural network (NN) to establish the relationship between the encoded features and the source term parameters of hydrogen leakages. The multi-sensor data are generated using computational fluid dynamics (CFD) analysis to simulate relevant hydrogen leakage scenarios of concern. The results show that compared with other machine learning algorithms, the proposed approach has an overall best performance in terms of the STE accuracy. Specifically, its hydrogen leakage source localization accuracy is 0.9798, and the leakage strength estimation R-squared (R2) can reach 0.9632. When the training data proportion is only 20–30%, the proposed approach can still perform well, indicating the ability of the model to effectively predict the location and strength of the leakage source even with relatively less training data.

A hybrid gradient climbing algorithm for a swarm robot-based gas leak detector

Methane emissions from leak sources can have a negative climate impact, in addition to contributing to the risk of explosions in urban environments. These risks can be minimized by developing systems that provide for an accurate and timely detection and localization of a gas leakage point. This research used a swarm of robots to detect and locate a leakage point. The localization algorithm derives from further optimization of the gradient climbing algorithm using fireflies acting as opportunistic agents. Firefly agents are characterized by their bioluminescent communication which guides them to dynamically adjust their positions and intensities based on the quality of the gradient information available to them. The proposed research focuses on enhancing gas leak detection through the development of a hybrid gradient climbing algorithm. This algorithm integrates gradient climbing techniques with swarm intelligence, utilizing the strengths of both approaches. This simulation resulted in the hybrid algorithm leading to a reduced convergence time and path lengths when compared to the swarm without opportunistic agents. The suggested approach can be important especially in gas distribution systems or in areas where human intervention is considered to be unsafe.