Source Of Leakage Research Articles

Acoustic Imaging Method for Gas Leak Detection and Localization Using Virtual Ultrasonic Sensor Array.

An acoustic imaging method for detecting and locating gas leaks based on a virtual ultrasonic sensor array is proposed and experimentally demonstrated. A scanning sensor array of only two sensors is used to collect the acoustic signals generated by the leakage hole. The matrix of the leakage signal is processed by the cross-power spectrum method to achieve time consistency, afterward, the location of the leakage source can be calculated by the virtual beamforming method. The influence of the number of sensors and the distance between adjacent sensors on the effect of the proposed method are compared and discussed. To verify the effectiveness and operability of the detection and localization method, several experiments were carried out. Furthermore, a series of experiments were conducted to assess the accuracy and stability of this method. The experimental results demonstrate that the proposed method based on a virtual sensor array can achieve highly accurate localization of gas leaks and performs well regarding stability.

Research on Multi-Point Monitoring Data Grid Model and Inversion Positioning Method for Gas Leakage in Oil and Gas Stations

The safe and stable operation of oil and gas stations makes a positive contribution to the stability and reliability of the natural gas supply. In order to reduce the impact of leakage and diffusion accidents in the station, it is necessary to develop an effective method to monitor and locate the leakage source quickly and accurately. This study proposes a multi-point monitoring data grid model to achieve the full-coverage monitoring of oil and gas stations. In addition, on this basis, a leakage source inversion positioning model is established to realize the leakage positioning of the station. A field experiment was carried out with an oil and gas station as an example. The results show that the optimal layout of points needs to consider the influence of environmental factors, confirmed by computational fluid dynamics (CFD) simulation. The optimized data interpolation not only reduces the cost of the monitoring point layout. In addition, through the comparison of multi-objective optimization algorithms and a robustness test, it can be found that the convergence efficiency and accuracy of the inversion positioning algorithm in this study have been greatly improved. Compared with the manual auxiliary positioning method, this method effectively solves the problem of leakage monitoring and positioning of oil and gas stations, and can achieve the purpose of leakage risk monitoring and “reducing cost and increasing efficiency”.

Cloud leakage in higher education in South Africa: A case of University of Technology

Background: Users with knowledge of an organisation can pose risks to Cloud Computing, including current and past employees and external stakeholders with access to the organisation’s cloud. These insiders may engage in intentional or unintentional disruptive behaviors, causing significant harm to the organisation. A study focused on insider threats in South African higher education examined the tactics used by cybersecurity leaders to enforce cybersecurity policies.Objectives: The goal of this study was to develop a comprehensive insider mitigation framework for cloud leakage in a South African University.Method: The study employed qualitative methodologies and a case study approach. Open-ended interviews were conducted to collect data from the participants. The collected data was coded and analysed using ATLAS.ti 22.Results: The study’s findings revealed that some of the major sources of cloud leakage are a lack of effective training, ineffective information security (IS) policy regulation, and the implementation of information security awareness workshops that provided advice on how information security should be managed in the university.Conclusion: Insider threats pose a serious risk to organisations. To mitigate this threat, it is crucial for organisations to establish strong security policies and closely monitor employee activities. By conducting a thorough assessment of insider threats, organisations can enhance their understanding of this dynamic threat and strengthen their defenses.Contribution: Although every employee is ultimately responsible for an organisation’s security, the most effective IS programmes demonstrate strong top-level leadership by setting a ‘tone at the top’ and promoting the benefits of IS through careful policy and guidance.

Numerical Simulation and Environmental Impact Assessment of VOCs Diffusion Based on Multi-Emission Sources in the Natural Gas Purification Plant

The rising number of natural gas purification plants has raised concerns about safety and environmental issues related to VOC (Volatile Organic Compounds) leakage. Therefore, it is crucial to conduct in-depth research on oil vapor emission patterns in these plants. Taking a typical natural gas purification plant as an example, a 1:1 scale model was established. Using methanol as the simulated medium, a study was conducted to investigate the impact of multiple leaks on the dispersion process of VOCs at the plant, combining field sampling with numerical simulation. The results indicate that wind speed influences the concentration of oil vapor, particularly on the leeward side, where vortex and reflux phenomena occur. The area of high concentration of oil vapor at v = 4 m·s−1 is eight times that at v = 8 m·s−1. Gravity and eddy currents contribute to the accumulation of oil vapor, especially closer to the central area of the plant where surrounding buildings obstruct dispersion. Smaller distances between leakage sources result in higher concentrations of oil vapor in the central region, leading to a larger affected area in the event of an accident. The study holds significant practical significance for the research, prevention, and management of leakage and dispersion incidents.

Constraining the Existence of Axion Clouds in M87* with Closure Trace Analyses

Black holes can amplify incoming bosonic waves via rotational superradiance, inducing bound states of ultralight bosons around them. This phenomenon has the potential to confine the parameter spaces of new bosons. Axions and axion-like particles (ALPs) are candidate beyond-standard-model particles that can form such clouds around supermassive black holes (SMBHs) and impact the polarization signal in a similar fashion to Faraday rotation via axion–photon coupling. Prior efforts have used polarized images from the Event Horizon Telescope (EHT) M87 2017 observations to limit the dimensionless axion–photon coupling to previously unexplored regions. However, with the novel calibration-insensitive quantities, closure traces, and conjugate closure trace products, it is possible to constrain the existence of axion clouds while avoiding the dominant sources of systematic uncertainties, e.g., station gains and polarization leakages. We utilize a simple geometric model for the polarization map of M87* to fit the model parameters with both simulated and real data sets and reach a comparable level of constraint in the accuracy with which an axion cloud may be excluded in M87. Future applications of our approach include subsequent M87* and Sgr A* observations by EHT and the next-generation EHT that are expected to produce stronger constraints across a wider range of axion and ALP masses. Because it does not require imaging, closure trace analyses may be applied to target active galactic nuclei for which imaging is marginal, extending the number of SMBHs from which axion limits may be obtained significantly.

Multi-leakage localization in water supply pipes based on convolutional blind source separation

In acoustic-based methods for water pipeline leak localization, there are few studies on complex multi-leak localization since these sources mix with and affect each other during propagation. In this paper, a novel multi-leak localization method based on the convolutional blind source separation (CBSS) algorithm is proposed, in which the time delays are estimated by the quadrature filter of the demixing matrix or the cross-correlation function between the separated signals and the measured signals, and then the leakage locations are found by linear localization formula. In theory, this paper analyzes the basic principle of the algorithm for locating multiple leaks. In simulation results, the algorithm’s feasibility is verified. In addition, it is obtained that the location distribution of the leakage sources will affect the location of the peak of the delay, and that strong noise will reduce the applicability of the algorithm. In prototype experiments, the algorithm is verified to locate the leaks effectively. This paper is the first to apply CBSS to multi-leakage localization, providing a feasible solution to this complex problem.

How does news affect biopharma stock prices?: An event study.

We investigate the impact of information on biopharmaceutical stock prices via an event study encompassing 503,107 news releases from 1,012 companies. We distinguish between pharmaceutical and biotechnology companies, and apply three asset pricing models to estimate their abnormal returns. Acquisition-related news yields the highest positive return, while drug-development setbacks trigger significant negative returns. We also find that biotechnology companies have larger means and standard deviations of abnormal returns, while the abnormal returns of pharmaceutical companies are influenced by more general financial news. To better understand the empirical properties of price movement dynamics, we regress abnormal returns on market capitalization and a sub-industry indicator variable to distinguish biotechnology and pharmaceutical companies, and find that biopharma companies with larger capitalization generally experience lower magnitude of abnormal returns in response to events. Using longer event windows, we show that news related to acquisitions and clinical trials are the sources of potential news leakage. We expect this study to provide valuable insights into how diverse news types affect market perceptions and stock valuations, particularly in the volatile and information-sensitive biopharmaceutical sector, thus aiding stakeholders in making informed investment and strategic decisions.

Corrosion Behavior of X80 Steel in a Simulated Soil Solution Under Square-Wave Current Interference

The buried pipeline is disturbed by the dynamic direct current (DC) stray current with the subway as the main leakage source, which has the safety risk of accelerating corrosion, resulting in pipeline failure, which not only causes economic losses but also threatens personal safety. Therefore, it is necessary to study the corrosion behavior of pipeline steel under dynamic DC interference. The corrosion behavior of X80 steel under dynamic DC interference were studied by a mass loss test, alternating current impedance, circuit simulation, x-ray diffraction, and a Pourbaix diagram. Combined with the corrosion efficiency and Pourbaix diagram of the Fe-H2O system, the reversible process and reduction process mechanism in the Faraday process are proposed. The reason why the corrosion efficiency slows down in the process of non-Faraday is analyzed by the electric double-layer model of equivalent circuit calculation. In addition, based on the above corrosion process, the corresponding conceptual model of the corrosion mechanism is proposed. The experimental results show that with the asymmetry of positive and negative half-cycle interference duration and the increase of current density, the corrosion efficiency, and current corrosion efficiency of X80 steel decrease, and local corrosion intensifies. The length of the negative half-cycle interference affects the capacitive charge-discharge effect at the metal/solution interface and the reduction reaction process of corrosion products, resulting in corrosion slowing down and corrosion efficiency reduction. This is also an important reason for the reduction of corrosion mass loss observed in the experiment compared with steady-state DC.

Failure prediction in the refinery piping system using machine learning algorithms: classification and comparison

Pipelines play a pivotal role in transporting large volumes of oil and gas within refineries. However, over time, they are susceptible to deterioration, leading to potential failures. Effective monitoring is imperative to maintain their optimal performance and safety. This research introduces a machine learning (ML) approach to pinpoint failure sources in oil and gas pipelines. Analysing an industrial dataset, we compared six ML models to predict failures in refinery pipelines. Leakage sources are predicted based on three operational parameters: transported fluid, temperature, and pressure. The models are evaluated and compared in terms of precision, recall, F1-score, accuracy, and the ROC-AUC. Remarkably, the XGBoost classifier exhibited a 99.7% accuracy, outperforming other algorithms in predicting the failure source. Emphasizing the value of Industry 4.0 solutions, this study underscores the potential of advanced ML in enhancing pipeline monitoring. Such predictions empower operators to pre-empt failures, reinforcing industry safety and sustainability.

Methane gas spectral imaging method based on dual wedge scanning mirrors

With the increase in the number of oil and gas pipelines laid in China, more attention needs to be paid to pipeline maintenance work. At present, the main methods of detecting natural gas leaks in oil and gas transmission stations include manual inspections, opposing natural gas detection equipment, and cloud desktop natural gas detection equipment. Hand held natural gas detection equipment is used for manual inspection, which requires regular manual inspection. However, the response speed is poor and gas leaks cannot be detected in a timely manner. The opposed laser gas detection method can only detect the presence of gas on the beam path. If a larger area of leakage detection is desired, more equipment needs to be installed, resulting in a greatly increase in hardware costs. The existing cloud desktop laser gas detection method controls the deflection of the laser beam through the cloud platform to achieve leak detection at various points in the area to be tested. However, the rotation speed of the cloud platform is slow, and a complete detection cycle takes dozens of minutes, and only the presence of gas can be detected. For accurate leak location, manual on-site survey is also required to further determine the leak location. In order to meet the needs of the real-time monitoring and rapid positioning of oil and gas pipeline leaks, in this work, a fast and accurately controlled dual wedge scanning mirror system is designed, which combines tunable semiconductor laser absorption spectroscopy technology to convert the gas measurement laser beam from point measurement to surface measurement, thereby obtaining the two-dimensional distribution of gas, which is conducive to subsequent analysis and positioning of gas leakage sources. By using the inverse solution iterative optimization algorithm, the angle of the wedge mirror is controlled to obtain an efficient and uniform beam scanning trajectory. The deflection direction and detection position of the laser beam are fused with the corresponding methane concentration information, and a methane concentration data containing position information is constructed. In order to quantitatively verify the measurement accuracy and spatial resolution in the experiment, a standard air bag is used to simulate the methane leakage distribution. The results show that the minimum detection limit of the system can be lower than 5×10<sup>–4</sup> m, and the spatial resolution can be less than 6 cm. At the same time, this method can adjust the scanning step node based on the measurement distance of the oil from gas station, thereby achieving adjustable imaging resolution. This imaging method provides a new idea for accurately positioning and detecting the methane leakage location and amount.

Advantages of unsupervised learning analysis methods in single-trace SCA attacks

Machine learning techniques are commonly employed in the context of Side Channel Analysis attacks. The clustering algorithms can be successfully used as classifiers in single execution attacks against implementations of Elliptic Curve point multiplication known as kP operation. They can distinguish between the processing of ‘ones’ and ‘zeros’ during secret scalar processing in the binary kP algorithm. The successful SCA performed by designers can aid in recognizing the leakage sources in cryptographic designs and lead to improvement of the cryptographic implementations. In this work we investigate the influence of the hamming weight of scalar k on the success rate of the single-trace attack. We used the clustering method K-means and the statistical method the comparison to the mean. We analysed simulated power traces and power traces of an FPGA implementation to conclude that K-means, unlike the comparison to the mean, was able to deal with extracting the scalar even when it is consisted of less than 30% of ‘ones’ and more than 70% of ‘ones’.

Degradation and Failure Mechanism of p-GaN Gate E-Mode GaN HEMTs

GaN-based power transistors are becoming pivotal devices for high power and high voltage operation due to their higher breakdown voltage, higher carrier mobility because of two-dimensional electron gas (2-DEG), better thermal resistance, higher frequency switching, and lower on-state resistance (RON) [1]. P-GaN gate E-mode GaN HEMTs have emerged as a center of extensive research to understand their operation and reliability [2]. However, one of the reasons the E-mode GaN HEMTs are not yet successfully utilized to their maximum potential is the lack of complete understanding of gate breakdown. Gate degradation of p-GaN gate E-mode GaN HEMT is less understood because of the complexities involving the operating conditions and variables such as gate length, the acceptor doping concentration in the p-GaN layer [3], p-GaN gate thickness, and design of the device [4-5]. To outline a consistent degradation mechanism, this work aims to present an electro-physical model of gate breakdown in p-GaN gate E-mode HEMTs.Lateral E-mode GaN HEMT devices with p-GaN gate configuration are used for this study. According to the Scanning Transmission Electron Microscopy (STEM) imaging of a new device, the AlGaN barrier is 10 nm thick, and the p-GaN cap is 65 nm. The gate-source and gate-drain lengths are 0.5 micrometer and 2.5 micrometer, respectively. The gate length defined by the p-GaN layer is 0.5 micrometer and the edges of the gate metal are pulled in from the top edge of the p-GaN layer by 40 nm. A 0.7 micrometer-long, source-connected field plate is over the p-GaN gate. The p-GaN gate is subjected to static stress to induce accelerated breakdown. Devices were stressed with HP 4155 Semiconductor Analyzer. The electrical measurements were performed with the source, drain, and substrate connected to the ground. FEI 200 Nova was used for Scanning Electron Microscopy (SEM) imaging and Focused Ion-Beam (FIB) milling. ARM JEM-200F with a Cs corrector was used for STEM.Our results show that time-to-fail is highly dependent on device operating conditions. We found no correlation between time-to-fail and initial gate current (IG0). Instead, a higher impact of temperature was found on the gate current of the device (IG) and time-to-fail at 100 °C. Gate failure at constant voltage stress was a single-stage failure. Under constant current stress, on the other hand, the gate shows a multi-stage failure. The breakdown mechanism depends on the impact of static stress on the gate degradation, starting with increased gate current leading to Schottky barrier leakage and finally to catastrophic contact failure. Our study shows that the metal/p-GaN interface at the gate finger becomes the weakest part of the gate stack, likely to be the initiation point of degradation. Metal/p-GaN interface and surface defects develop as percolation paths acting as leakage sources, and nano-cracks have been observed in the gate cap. FA also shows physical degradation at the metal/p-GaN cap due to electrical stress. Observed physical damage in the gate region is correlated to the gate leakage characteristics to develop an electro-physical failure model for gate breakdown in p-GaN gate E-mode GaN HEMTs.

A lightweight network based on local–global feature fusion for real-time industrial invisible gas detection with infrared thermography

The detection of industrial invisible gas plays a vital role in preventing environmental pollution and fire accidents. Optical gas imaging (OGI) with infrared thermography is widely used in the field of gas leak monitoring and treatment by visualizing gases to quickly and accurately detect and locate the gas leak sources. However, this method still relies on manual visual inspection. Existing automatic visual gas detection methods suffer from insufficient gas feature extraction and high computational cost due to the indistinct gas features in thermal images. To address these problems, we propose a new lightweight network specialized for thermal gas feature extraction, namely GasViT, sufficiently extracting gas features at very low computational cost by local–global feature fusion. Specifically, two new feature extraction modules Multi-scale Fusion Feature Attention (MsFFA) and Multi-head Linear Self-attention (MhLSa) are proposed for GasViT. MsFFA enhances the gas local feature extraction ability by constructing multi-scale channel and spatial feature fusion maps, enabling the network to focus on more valid local information. MhLSa complements the gas global features with very low computational cost by efficiently encoding the global information of the image in terms of the innovative linear self-attention mechanism. Our experimental results on the self-made Industrial Invisible Gas (IIG) Dataset show GasViT achieves 82.7% mAP50, significantly outperforming the state-of-the-art lightweight networks. Moreover, GasViT achieves 33 FPS real-time detection with a running memory footprint of only 47.2 MB on edge computing devices, making it extremely suitable for portable and embedded detection devices than existing methods to cover gas leakage detection in complex and hazardous industrial scenarios.

Analysis and prediction of hydrogen-blended natural gas diffusion from various pipeline leakage sources based on CFD and ANN approach

The influences of pipeline leakage source types on the hazard area distribution caused by accidental hydrogen-blended natural gas leakage are still the focus of gas leakage accidents. Three types of pipeline leakage sources are discussed in this study. Namely, the gas in the vertical pipeline flows from top to bottom, and the leakage orifice is in the pipeline wall; the gas in the vertical pipeline flows from bottom to top, and the leakage orifice is in the pipeline wall; the leakage orifice is in the horizontal pipeline end. The common feature of the above three pipeline leakage sources is that the normal of leakage orifice is parallel to the horizontal plane. The differences in the flow field distribution of hydrogen-blended natural gas released from three pipeline leakage sources are compared through numerical simulation. The effect of various factors on the jet centerline, concentration decay, and maximum horizontal diffusion distance of hydrogen-blended natural gas released from three pipeline leakage sources are analyzed. The predictive models of hydrogen-blended natural gas's maximum horizontal diffusion distance in three pipeline leakage sources under multi-factor coupling are established through a backward propagation neural network (BPNN). Results show that the angle between the gas flow direction in the pipeline and the normal of the leakage orifice, and the background step flow cause differences in the concentration field near the leakage orifice from the three pipeline leakage sources at the same working conditions. In the near-field region, the leakage orifice diameter has the most significant influence on the jet centerline of three hydrogen-blended natural gas jets among three factors. The concentration decay rate of gas released from the horizontal pipeline end is greater than that of gas released from the other two pipeline leakage sources. With the increase of volumetric flow rate, the maximum horizontal diffusion distances of methane released from three pipeline leakage sources all increase, while the maximum horizontal diffusion distances of hydrogen released from three pipeline leakage sources have little change. The predictive models of the maximum horizontal diffusion distance of hydrogen-blended natural gas released from three pipeline leakage sources are established through BPNN. The maximum horizontal hazard distances of the new working conditions are predicted, and the predicted results are in good agreement with the numerical simulation results. This investigation has guiding value for fast and accurate prediction of flammable gas hazard areas.

Simulation of hydrogen leakage diffusion behavior in confined space

In this paper, the diffusion characteristics and laws of hydrogen leakage in confined spaces are investigated using a combination of numerical simulations and experiments, and a small-scale cube model (dimensions: 0.47 m×0.33 m × 0.20 m) is used. The effects of leak source location, obstacles, leak hole size and shape on hydrogen diffusion and concentration evolution were investigated. The results show that the top funnel is sprayed vertically downward, the diffusion velocity decreases rapidly under the blocking effect of air and buoyancy force, and the momentum of the airflow is exchanged. During the injection phase, the leaking gas is mainly subjected to strong buoyancy forces and the hydrogen concentration shows a stratification effect. When the injection stops, the concentration difference decreases and eventually the hydrogen concentration reaches a steady state. The obstacle model in turbulence (4.3%) increases the concentration difference by a factor of about three compared to the empty room model (1.7%), which seriously affects the diffusive behavior of hydrogen. The obstacle in laminar flow model (10.4%) has almost the same concentration difference as the empty room model (10.2%). Rectangular leakage holes have a greater initial kinetic energy and diffuse more widely and faster.

Narrative review of traumatic pneumorrhachis.

Pneumorrhachis (PR) is defined as presence of free air in the spinal canal. Traumatic PR is very rare, and its exact incidence and pathogenesis is unknown. A comprehensive literature search was performed using the PubMed, Cochrane Library, Google Scholar and Scopus databases to identify articles relevant to traumatic PR published till January 2023. A total of 34 resources were selected for inclusion in this narrative review. Traumatic PR can be classified anatomically into epidural and intradural types. In the epidural type, air is present peripherally in the spinal canal and the patients are usually asymptomatic. In contrast, in intradural PR, air is seen centrally in the spinal canal and patients present with neurological symptoms, and it is a marker of severe trauma. It is frequently associated with traumatic pneumocephalus, skull fractures or thoracic spine fracture. Computed tomography (CT) is considered to be the diagnostic modality of choice. Epidural PR is self-limited and patients are generally managed conservatively. Patients with neurological symptoms or persistent air in spinal canal require further evaluation for a potential source of air leak, with a need for surgical intervention. Differentiation between epidural and intradural PR is important, because the latter is an indication of severe underlying injury. CT imaging of the entire spine must be performed to look for extension of air, as well as to identify concomitant skull, torso or spinal injuries Most patients are asymptomatic and are managed conservatively, but a few may develop neurological symptoms that need further evaluation and management.

On the mixed acoustic and vibration sensors for the cross-correlation analysis of pipe leakage signals

Pipe leak detection can be carried out by various acoustic/vibration sensors, such as acoustic pressure, velocity and acceleration sensors. The purpose of this study is to investigate the effectiveness of the mixed acoustic/vibration sensors for the determination of pipe leakage. Firstly, a propagation and attenuation model of leakage signal in the liquid-filled pipeline is established based on frequency response functions. Further, the expressions of the cross-correlation function of different sensor types are derived with respect to the differential properties of Fourier transform. Then, the influence of signal-to-noise ratio (SNR) on the cross-correlation function is analyzed. Numerical simulation is employed to generate pressure, velocity and acceleration signals by differentiating the leakage source signals. Simulation results indicate that the use of pressure sensor acts as low-pass filtering operation, whereas the velocity and acceleration sensors act as band-pass filtering. Finally, experimental work is carried out on a medium density polyethylene (MDPE) pipe. Leakage signals are collected synchronously by hydrophones and accelerometers. Experimental results show that the cross-correlation function of hydrophones fluctuates slowly with peak value being less obvious, while the cross-correlation function of accelerometers fluctuates rapidly and has a sharper peak. In comparison, there is a compromise in the fluctuation for the sake of peak sharpness in the cross-correlation function of the mixed sensors, indicating that the corresponding cross-correlation result are less affected by background noise.

Research on leakage detection technology of natural gas pipeline based on modified Gaussian plume model and Markov chain Monte Carlo method

Natural gas pipeline leakage can cause significant economic losses and even jeopardize public safety. Therefore, leakage detection technology is crucial. In this study, the pipeline source term estimation method is proposed to identify the source of natural gas pipeline leakage. The method combines the modified Gaussian plume model, the unmanned aerial vehicle inspection technology equipped with laser methane sensors, and the Markov chain Monte Carlo method based on Bayesian inference. Different from traditional leak detection techniques, this method can simultaneously obtain the location and rate of pipeline leakage. The accuracy of the method is validated by using simulated data from the modified Gaussian plume model, which can be employed to replace experimental tests. Convergence diagnosis and model evaluation are performed on the inversion model in this method, verifying its rationality and feasibility. The impact of system errors on the pipeline source estimation method is analyzed, revealing that the accuracy of inversion can be improved by using prior information and ensuring the reliability of the forward gas dispersion model and monitoring data. The pipeline source term estimation method proposed in this paper can offer technical support for the emergency treatment of natural gas pipeline leakage accidents.

Effect of leakage source condition on the unignited hydrogen released issuing through a horizontal realistic pipeline geometry

An unignited gas release experiment system for the schlieren visualization and concentration quantitative measurement is designed and built to investigate the diffusion and concentration distribution features of turbulent hydrogen jets issuing from a realistic pipeline geometry. Helium is used in place of hydrogen for safety reasons. The spatial and temporal evolution of the asymmetry of turbulent helium jets was studied. The influence mechanisms of the volumetric flow rate (Q), the ratio of orifice diameter to the inner diameter of pipeline (R), and the angle between the normal of the leakage orifice and the opposite direction of gravity (θ) on the asymmetric concentration distribution are revealed. The experimental results show that the diffusion angle (α) and the inclination angle (β) of the turbulent helium jet are the largest when Q is 15 SLPM, R is 0.3, and θ is 0°, and the asymmetric feature of the turbulent helium jet is obvious. As the Q increased, the slope k of the inverse helium concentration (1/volume fraction) and a scaled distance L (L = (x/d)·(ρa/ρ0)1/2) became small. The increase of Q aggravated the concentration attenuation caused by the volumetric flow rate and the asymmetry jet. As R increased, the slope k of the inverse helium concentration and a scaled distance L first decreased and then increased. The slope k got the minimum value when R was 0.3. The downward movement of the leakage orifice position increased the turbulent intensity near the leakage orifice. This work can provide theoretical support for accident prevention and emergency treatment of hydrogen leakage accidents in long-distance pipeline low-pressure hydrogen transportation.

Специфіка розвитку ринку BIG DATA для потреб відновлення економіки України в повоєнний період

This article analyses the characteristics and prospects of the global big data market, and iden-tifies the sources of data leakage in the global big data market. A study of the peculiarities of the de-velopment of the big data market in Ukraine in the economic context before and during the war was conducted, which revealed the influence of international institutions in the fight against cyber-attacks in the context of disturbances. The directions of adaptation of the Ukrainian cyberspace to functioning in the conditions of war are identified, and recommendations are made on the directions of implementation of Big Data technologies in the post-war period for the needs of economic growth. The results of the study of the big data market are summarized. Recommendations on the directions of implementation of big data technologies in the post-war period for the needs of economic growth are given. The purpose of the study is to research and analyse trends, development prospects and threats to the big data market at the global level and at the level of the Ukrainian economy during military operations; to develop recommendations on economic directions to implement the protection of per-sonal data in the context of disruptive factors. The results of the study show that the big data market in Ukraine is operating in a disruptive environment, but this does not prevent its development for the benefit of Ukraine's infrastructure and economy. The biggest threat to this infrastructure is cyber-attacks from the Russian Federation. The aim of these actions is to steal data in order to manipulate it and to destabilise the Ukrainian population and its government institutions. The perspectives for further research are the development of strategies for the implementa-tion of big data technologies for the development of Ukraine and the economy in the post-war era.