Leakage Events Research Articles

Validation study of sodium pool fire modeling efforts in MELCOR and SPHINCS codes

Discharge of sodium coolant into containment from a sodium-cooled fast reactor vessel can occur in the event of a pipe leak or break. In this situation, some of the liquid sodium droplets discharged from the coolant system will react with oxygen in the air before reaching the containment. This phase of the event is normally termed the sodium spray fire phase. Unreacted sodium droplets pool on the containment floor where continued reaction with containment atmospheric oxygen occurs. This phase of the event is normally termed the sodium pool fire phase. Both phases of these sodium-oxygen reactions (or fires) are important to model because of the heat addition and aerosol generation that occur. Any fission products trapped in the sodium coolant may also be released during this progression of events, which if released from containment could pose a health risk to workers and the public. The paper describes progress of an international collaborative research in the area of the sodium fire modeling in the sodium-cooled fast reactors between the United States and Japan under the framework of the Civil Nuclear Energy Research and Development Working Group. In this collaboration between Sandia National Laboratories and Japan Atomic Energy Agency, the validation basis for and modeling capabilities of sodium spray and pool fires in MELCOR of Sandia National Laboratories and SPHINCS of Japan Atomic Energy Agency are being enhanced.This study documents MELCOR and SPHINCS sodium pool fire model validation exercises against the JAEA’s sodium pool fire experiments, F7-1 and F7-2. The proposed enhancement of the sodium pool fire models in MELCOR through addition of thermal hydraulic and sodium spreading models that enable a better representation of experimental results is also described.

MP52-10 CHRONIC, NON-INVASIVE NERVE STIMULATION THERAPY TO TREAT INCONTINENCE IN PEOPLE WITH NEUROGENIC BLADDER

You have accessJournal of UrologyCME1 Apr 2023MP52-10 CHRONIC, NON-INVASIVE NERVE STIMULATION THERAPY TO TREAT INCONTINENCE IN PEOPLE WITH NEUROGENIC BLADDER Seth Meade, Dennis Bourbeau, Steven Brose, Kenneth Gustafson, Cesar Colasante, and Kevin Suarez Seth MeadeSeth Meade More articles by this author , Dennis BourbeauDennis Bourbeau More articles by this author , Steven BroseSteven Brose More articles by this author , Kenneth GustafsonKenneth Gustafson More articles by this author , Cesar ColasanteCesar Colasante More articles by this author , and Kevin SuarezKevin Suarez More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003300.10AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Reduction of urinary urgency and incontinence is a top priority for individuals with neurogenic bladder (NB). Multiple clinical studies show non-invasive genital nerve stimulation (GNS) can modulate sympathetic reflexes to inhibit bladder contractions acutely and is well-tolerated. To translate GNS as an effective adjunct therapy for managing neurogenic bladder, further evidence of chronic efficacy and feasibility is needed. METHODS: In this “before-and-after” prospective cohort study, participants acted as their own controls. 9 individuals with NB were enrolled (8/9 (88.9%) male) and confirmed to: 1) have NB by urodynamics examination and 2) have bladder contractions inhibited by GNS. 5 participants (100% male) who tolerated GNS without additional medications and were adherent to voiding diaries tested the efficacy of at-home, self-administered GNS using a commercial TENS unit. Participants completed voiding diaries, each detailing at least 1 continuous week of tracked voiding, leak, and urgency events. Diaries were completed once at study initiation during a control period without GNS or medications, and then for 3 separate periods during prolonged use of GNS to manage incontinence (baseline, 6 months, 1 year). Participants also completed a bladder-related quality of life questionnaire (Qualiveen) at their initial evaluation, as well as 6 and 12 months later. RESULTS: Preliminary findings show that GNS increased bladder capacity for all 9 individuals tested by an average of 58%, and 3/5 individuals tested chronically maintained an increased bladder capacity after 6 months of GNS. 4/5 individuals reported increased bladder-related QOL by total Qualiveen after 6 months of at-home GNS. Of 2 individuals who experienced leakage with chronic voiding data one reduced their leaks/day by 90% at 6 months of use and the other by 30% at 1 year of GNS. Of 2 individuals with the primary goal of reducing bladder urgency, neither was able to reduce their urgency events/day but were able to reduce their voiding frequency by 65% and 15% after more than 1 month of use. The primary reason individuals reported for dropping from the study was difficulty placing the electrodes and keeping them in place. CONCLUSIONS: GNS can provide a durable reduction in leakage events for individuals with incontinence due to neurogenic bladder up to 6 months. Multiple subjects reported issues with electrode placement and adhesion as well as associated wires from the TENS unit leading to less-than-ideal GNS for some. Source of Funding: -RX002512 Department of Veterans Affairs -642810 Craig H. Neilsen Foundation (CHNF) © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e706 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Seth Meade More articles by this author Dennis Bourbeau More articles by this author Steven Brose More articles by this author Kenneth Gustafson More articles by this author Cesar Colasante More articles by this author Kevin Suarez More articles by this author Expand All Advertisement PDF downloadLoading ...

Water distribution network leak localization with histogram-based gradient boosting

Abstract Accurate and rapid leak localization in water distribution networks is extremely important as it prevents further loss of water and reduces water scarcity. A framework for identifying relevant leak event parameters such as the leak location, leakage area, and start time is presented in this paper. Firstly, the proposed data-driven methodology consists of acquiring pressure data at nodes in the network through hydraulic simulations by randomly changing the leak event initial conditions (leak location, area, and start time). Pressure uncertainties are added to the sensor measurements in order to make the problem more realistic. Secondly, the acquired data are then used to train, test, and validate a machine learning model in order to predict the relevant parameters. The random forest and the histogram-based gradient boosting machine learning algorithms are investigated and compared for the leak detection problem. The proposed approach with the histogram-based gradient boosting algorithm shows high accuracy in predicting the true leak location.

Real-Time Reliability Access Control Based on Rail Traffic Data Platform

With the introduction of the industrial internet, Internet of Things, and big data technology, the interconnection degree of the industrial control cloud network is getting higher and higher, the data interface needs to be gradually standardized, and there are more and more open interface components. Data-based attacks will continue to emerge. The real-time and reliability of access control are essential for trust value updating between network participants. This paper proposes a fine-grained dynamic real-time credibility access control method based on zero trust. Continuous authentication and trust evaluation should be carried out throughout the access control process. The zero-trust evaluation indicators of a rail transit data platform that conforms to the requirements of grade protection 2.0 are established. According to the risk feedback, the current trust level is dynamically updated in real time, and the results are used in the access control model. It can reject unauthorized access, reduce the occurrence of illegal intrusion data leakage and data loss events, and has great value in rail transit data security.

User Privacy Awareness, Incentive and Data Supply Chain Pricing Strategy

In recent years, the collection, mining, and utilization of data have become a new profit growth point for enterprises, and these events have also accelerated the pace of enterprises to collect users’ data. However, the relevance of personal data privacy and the frequent occurrence of data leakage events have increased users’ privacy awareness. The purpose of our study is to enhance the effective flow of data while protecting users’ data privacy. The data supply chain consists of the end user, data provider, and service provider, and involves the flow of the value-added process of data. Our study focuses on the pricing strategy of data products considering data incentive and data protection levels. We propose three models—centralized pricing, decentralized pricing, and revenue-sharing pricing—and solve them, and then we analyze the impact of users’ privacy awareness on data incentives, protection, and pricing of data products in the three models. We also analyze which pricing method works best for participants.

Dynamic calcium-calmodulin-dependent stabilization of leaky RyR2 in live cardiac myocytes.

Calmodulin (CaM) is a major regulator of cardiac ryanodine receptors (RyR2). CaM binding shifts RyR2 channel conformation to a state that suppresses diastolic SR Ca leak that can become pro arrhythmic as commonly observed in cardiac diseases such as heart failure (HF) or diabetic cardiomyopathy. In these diseases, CaM affinity for RyR2 is reduced, promoting a leaky pathological RyR2 structural state that is arrhythmogenic. Conversely, increased CaM binding can suppress RyR2-mediated Ca leak and promote a conformational shift to the stable physiological non-leaky state. Still, there are unknowns regarding the dynamic interplay between CaM-RyR2 during each heartbeat and in pathogenic RyR states in live cardiac myocytes. We hypothesize that high [Ca] elevations reached during Ca transients in a healthy beating myocyte would increase CaM-RyR binding throughout the cardiac cycle, promoting RyR stability. Further, the lower CaM-RyR2 affinity in disease, exacerbated by reduced Ca transient amplitude, resulting in lower CaM-RyR2 binding, destabilized RyR2s and enhance arrhythmogenic diastolic SR Ca leak. We used permeabilized cardiac myocytes from WT and HF mouse models as a controlled approach to measuring both the [Ca]i and [CaM] dependence of CaM-RyR2 binding via FRET between donor-labeled FKBP12.6 and acceptor-labeled CaM. Moreover, we simultaneously recorded [Ca]i transients within myocytes, enabling measurement of dynamic CaM-RyR2 saturation during periodic Ca waves that emulate paced Ca transients. We find that CaM-RyR2 FRET is reduced in the leaky pathological state and associated with alterations in Ca leak event frequencies. We infer that the healthy higher CaM-RyR2 affinity favors more complete saturation of RyR2 with CaM throughout the cardiac cycle, versus pathological states. These data suggest that a modest fraction of CaM-free RyR2s with pathological diastolic conformation might suffice to elicit proarrhythmic SR Ca leak in cardiac disease.

Acoustic feature-based leakage event detection in near real-time for large-scale water distribution networks

Abstract Acoustic sensors are widely deployed to detect hidden leakages in water distribution networks (WDNs). However, few studies have been conducted to quantitatively understand the dominant leakage acoustic characteristics, which are usually mixed with unknown environmental noises, coupled with the constraint of sparse deployment of acoustic sensors. In this paper, a comprehensive approach, that performs acoustic data feature analysis, is developed to detect pipe leakages in near real-time via a series of systematic analyses, namely: (1) data quality assessment; (2) features identifications; (3) outlier detection and event classification; and finally (4) near real-time leakage detection. The proposed solution has been tested on two major WDNs in Singapore having around 1,000 km of water pipelines installed with 74 permanently installed hydrophone sensors. The leakage detection results obtained from our case study demonstrate that the dominant leakage acoustic characteristics can be captured in lower intrinsic mode functions (IMFs), to within the frequency range of 100–750 Hz approximately, by decomposing the original acoustic signal. Systemwide leakage event detection and classification models are subsequently trained and tested on acoustic datasets collected over 13 historical months, where more than 70% F1-scores can be obtained from the emulated near real-time leakage detection analysis.

Assessment of the effectiveness of ventilation for gas turbine generator set safety

Increasing global warming problems appeal to the employment of clean power generation machineries such as gas turbines. Gas turbine is gas-fired engine which is usually enclosed in an acoustic enclosure for higher reliability and lower operating noise. However, it may face the risk of fuel gas accumulation inside the package which can be extremely dangerous. For the safety of a gas turbine generator set, proper ventilation system is crucial to prevent it from danger by diluting the potential leaked gas. Inspired by the significance of ventilation, this paper aims to present and propose study for the assessment of the effectiveness of ventilation for gas turbine packages in worst-case gas leak. A 2 MW ZK2000 gas turbine was chosen for the study. Method of assessment was introduced and numerical studies by Computational Fluid Dynamics (CFD) were adopted, including both transient and steady state analysis. Aiming to figure out the conditions for worst-case gas leak, the location of leak that may cause the most dangerous gas accumulation was obtained by identifying the poorly ventilated regions within the enclosure. After that, the leak rate was estimated that just to trigger the gas detector at the ventilation outlet according to ISO 21789. Worst-case gas leak simulations were performed in cold engine situation because of the possibility analysis of gas leak. The results at standard conditions show great compliance with the limited value in the code thus indicates that the capability of the current ventilation design. Effect of dilution was studied with various ambient temperatures and ventilation flow rates. One of the key findings is that the flow uniformity has the major impact on enhancing dilution of gas cloud. Structures improvements within enclosure and operation instructions were suggested for the safety of gas turbine unit in the event of gas leak.

Shock Absorber Leakage Impact on Aircraft Lateral Stability During Ground Handling Maneuvers

Aircraft braking maneuvers are safety-critical on-ground motions that exhibit complex dynamics and significant dependence on system operating conditions. The fundamental interface between the aircraft and the ground is the landing gear. Among the landing gear components, the shock absorbers may be subject to gas leakage during their lifetime, which is an anomaly that could compromise the lateral stability properties of the aircraft on the operating regimes found during braking maneuvers. In this paper, an explicit link is established between main landing gear shock absorber leakage and aircraft lateral stability. To investigate lateral stability, a high-fidelity multibody nonlinear aircraft simulator is developed in a MATLAB/Simulink framework and validated against experimental data. To generate insight into the problem and to quantify shock absorber leakage impact on aircraft lateral stability, two simple but descriptive analytical models are also developed, each one on a different operating mode of the system. The analysis of the models reveals that shock absorber leakage can have a significant effect on aircraft lateral stability, especially at high velocities and highly damped nose wheel steering conditions. The models developed in this work may be used by aircraft control system designers to come up with more effective lateral stability controllers in the event of main landing gear shock absorber leakage.

Adaptive Dynamic Programming-Based Method for Signal Evaluation of Energy Transportation System

When a leak event happens in energy transportation system, emergency procedures would be taken by staff according to the abnormal signal evaluation result. The wrong evaluation result leads to inadequate treatment and preparation, and then the leak event would evolve to fire and explosion hazards. To solve it, a data-driven signal evaluation method based on adaptive dynamic programming is proposed in this paper. First, a signal estimation model based on three-layer neural networks is proposed to describe the pressure signal change along pipeline. Then, based on the estimation result of pressure change, a flow signal evaluation method based on value iteration scheme is proposed to obtain the leak flow rate. Moreover, the abnormal signal analysis principles are added into the iterative solving process to close to the actual leak event situation. For different leak scenarios, the proposed method could give the reasonable and reliable result through the designed neural network structures. Finally, different types of case results demonstrate that the proposed method could be applied to the signal evaluation problem.

Application of Drones in Nuclear Contaminated Sites

The impact of nuclear radiation following the Japan earthquake has been significant. This article states that on March 11, 2011, a 9.0 magnitude earthquake occurred near Honshu Island, Japan, followed by a tsunami. The Fukushima Daiichi nuclear power plant was severely damaged by the earthquake and tsunami and triggered the Fukushima nuclear leakage event, which had an impact beyond the national boundaries of Japan and caused a global nuclear pollution accident. Nuclear pollution can have severe and long-lasting impacts on the environment and human health. Exposure to radioactive materials can cause radiation sickness, cancer, and genetic mutations. Unmanned aerial vehicle (UAV) technology is the best monitoring mechanical, can carry out monitor and post-quake repairs. This paper explores the application of drones in nuclear contaminated sites from the perspective of drone technology characteristics, the special features of nuclear contaminated sites, and the advantages of drone application in these sites, such as reducing radiation risks, improving operational efficiency, and lowering operating costs. For example, it can be used in nuclear radiation restoration investigation, image collection communication in disaster areas, operational assistance. It improves the nuclear emergency rescue system. Additionally, this paper provides an outlook on the future of drone application in nuclear contaminated sites. Drones can be equipped with more advanced sensors and equipment, enabling more efficient work and restoration.

Online leakage detection system based on EWMA-enhanced Tukey method for water distribution systems

Abstract Real-time leakage detection based on pressure and flow data has become increasingly essential for water distribution systems (WDSs). Recent data-driven leakage detection approaches have largely focused on burst detection characterised as sudden outflow or sudden pressure drops but did not mention the ability to detect gradual leakage events that do not have sudden change and could cause more water loss. This study proposes an online leakage detection system based on the exponential weighted moving average (EWMA)-enhanced Tukey method to help monitor gradual leakage events of WDSs. The proposed online system comprises three main parts: data pre-processing, the online detection sub-system, and the parameter updating sub-system. The proposed online system is based on lightweight and powerful statistical tools without complex model construction. The effectiveness of the proposed system is demonstrated on leakage datasets under various real-world scenarios, including gradual leakages and bursts. The results showed that the proposed EWMA-enhanced Tukey method could detect gradual leakage events quickly while generating low false alarms. The proposed method is computationally effective and able to deal with non-stationary behaviours automatically.

The Scenario Construction and Evolution Method of Casualties in Liquid Ammonia Leakage Based on Bayesian Network.

In China, food-freezing plants that use liquid ammonia, which were established in the suburbs in the 1970s, are being surrounded by urban built-up areas as urbanization progresses. These plants lead to extremely serious casualties in the event of a liquid ammonia leakage. The purpose of this thesis was to explore the key factors of personnel protection failure through the scenario evolution analysis of liquid ammonia leakage. The chain of emergencies and their secondary events were used to portray the evolutionary process of a full scenario of casualties caused by liquid ammonia leakage from three dimensions: disaster, disaster-bearing bodies, and emergency management. A Bayesian network model of liquid ammonia leakage casualties based on the scenario chain was constructed, and key nodes in the network were derived by examining the sensitivity of risk factors. Then, this model was applied to a food-freezing plant in Beijing. The results showed that inadequate risk identification capability is a key node in accident prevention; the level of emergency preparedness is closely related to the degree of casualties; the emergency disposal by collaborative onsite and offsite is the key to avoiding mass casualties. A basis for emergency response to the integration of personnel protection is provided.

Calculating the large leakage flux of a breached hydrocarbon trap using geophysical interpretation of a paleo-gas-water contact

Integration of three-dimensional seismic and well data from the Northern Carnarvon Basin on the North West Shelf of Australia was used to assess the evidence for top seal breach of a paleo-gas accumulation. Several seismic indicators of vertical hydrocarbon leakage from the crest of the stratigraphic Mungaroo Trap point to a significant flux of gas within the past few hundred thousand years. Mapping of the top reservoir within the Triassic aged Mungaroo Formation revealed strong evidence for a paleo-gas-water contact (GWC), approximately 100 m down flank from the erosional crest of the trap. This contact conforms to structure and delimits an original volume of 1.1 trillion cubic feet (Tcf), based on reservoir property calibration from nearby wells. Mapping also revealed a present-day gas-water contact with a closed volume of 0.2 Tcf, 30 m down flank from the crest. This contact displays a discordant geometry indicative of a dynamic contact.The leakage zone is located directly above the crest of the structure. It is seismically well imaged and comprises seafloor pockmarks, shallow gas anomalies and gas hydrate anomalies. The presence of a leakage zone distributed vertically above a reservoir containing a present and paleo-GWC provides compelling evidence this trap has leaked by breaching of the top seal. Volumetric calculations using the two GWCs indicate that the Mungaroo Trap has been depleted by 0.9–1.1 Tcf. A dynamic-GWC indicates the leakage event within the trap has occurred recently. Analysis of dating of horizons correlated to the seafloor pockmarks places the leakage event at sometime within the last 300 Ka.

Enhanced removal of trace pesticides and alleviation of membrane fouling using hydrophobic-modified inorganic-organic hybrid flocculants in the flocculation-sedimentation-ultrafiltration process for surface water treatment

Pesticide concentrations in surface water occasionally exceed regulated values due to seasonal events (rainy season in high intensity agricultural areas) or intermittent discharges (leakage, spillage, or other emergency events). The need to remove pesticide compounds in these situations poses a challenge for drinking water treatment plants (DWTPs). In this work, the performance of dosing hydrophobic-modified inorganic-organic hybrid flocculants (HOC-M; lower acute toxicity than corresponding metal salt coagulants; acceptable economic costs when M=Al or Fe; prepared in large-scale quantities), for the removal of four different pesticides (each initial concentration: 0.25 μg/L) from Yangtze River water, and in mitigating membrane fouling, by an integrated flocculation-sedimentation-ultrafiltration (FSUF) process, was evaluated over a period of 40 days; the FSUF is well-established in many DWTPs. The mechanisms underlying the treatment were unveiled by employing a combination of instrumental characterizations, chemical computations, material flow analyses, and statistical analyses. Efficient pesticide removal (80.3%∼94.3%) and membrane fouling reduction (26.6%∼37.3% and 28.3%∼57.6% for reversible and irreversible membrane resistance, respectively) in the FSUF process were achieved by dosing HOC-M, whereas conventional inorganic coagulants were substantially inferior for pesticide removal (< 50%) and displayed more severe fouling development. Hydrophobic association between the pesticides and the hydrophobic organic chain of HOC-M played a predominant role in the improvement in pesticide removal; coexisting particulate/colloid inorganic minerals and natural organic matter with HOC-M adsorbed on the surface, acting as floc building materials, provided sites for the indirect combination of pesticides into flocs. The observed fouling alleviation from dosing HOC-M was ascribed to both the pre-removal of fouling-causing materials in the flocculation-sedimentation prior to UF, and a stable hydrophilization modification effect of residual HOC-M in the UF unit. The latter effect resulted from a hydrophobic association between the PVDF substrate of the membranes and the hydrophobic organic chains of the HOC-M, causing the hydrophilic ends of the HOC-M to be exposed away from the membrane surface, thereby inhibiting foulant accumulation. This work has not only demonstrated the superior performance of dosing HOC-M in the FSUF process for trace pesticide removal in DWTPs, but also clarified the underlying mechanisms.

Battle of the Leakage Detection and Isolation Methods

A key challenge in designing algorithms for leakage detection and isolation in drinking water distribution systems is the performance evaluation and comparison between methodologies using benchmarks. For this purpose, the Battle of the Leakage Detection and Isolation Methods (BattLeDIM) competition was organized in 2020 with the aim to objectively compare the performance of methods for the detection and localization of leakage events, relying on supervisory control and data acquisition (SCADA) measurements of flow and pressure sensors installed within a virtual water distribution system. Several teams from academia and the industry submitted their solutions using various techniques including time series analysis, statistical methods, machine learning, mathematical programming, met-heuristics, and engineering judgment, and were evaluated using realistic economic criteria. This paper summarizes the results of the competition and conducts an analysis of the different leakage detection and isolation methods used by the teams. The competition results highlight the need for further development of methods for leakage detection and isolation, and also the need to develop additional open benchmark problems for this purpose.

Modeling and assessment of accidental subsea gas leakage using a coupled computational fluid dynamics and machine learning approaches

All over the world, the oil and gas industries are the most important source of energy. The likelihood of subsea gas and oil leakage is increasing, which can cause threats and harmful impacts on the marine environment and potentially catastrophic events such as fires, explosives, and the loss of structural integrity of subsea infrastructure. Also, Physical models have usually been used to predict sea currents, but they are unstable to disturbances and hence incorrect over long periods of time. Machine learning approaches are more resistant than the physical models that have usually been used to predict sea currents. Also, Physical models have usually been used to predict sea currents. Nonetheless, they are not stable to disturbances and thus are not correct for long periods of time. Machine learning approaches are more resistant than the physical models that have usually been used to predict sea currents. Machine learning approaches are more resistant to change and perturbation. Therefore, the goal of this research is to assess the potential of hazards of the gas plume from subsea pipeline rupture till reaching the sea surface by changing the influence parameters on gas plume to assist petroleum companies in developing risk assessment strategies by assessing and simulating subsea gas release in order to contain the leakage by developing coupling models one for machine learning code which can predict the upcoming water current speed by using Multiple Linear Regression algorithm and hooked it by UDF to a second model which implements Computational Fluid Dynamics (CFD) model to study subsea gas release under current effects. Engebretsen’s Rotvoll experiment data is being used to validate the numerical computational fluid dynamics model. The rising time and fountain height and horizontal migration for gas release are the essential factors to be considered while evaluating the gas dispersion through our study by changing the influencing parameters such as leakage hole sizes, water current speeds, gas velocity, and water depths in the presence of water current in all cases. Also, applied our simulation to real case parameters for one of the Egyptian Petroleum Companies. These findings might aid in evaluating the hazards and response planning in the event of subsea gas leakage.

Multi-objective and risk-based optimal sensor placement for leak detection in a water distribution system

The optimal sensor placement for leak detection in a water supply network has been studied extensively. The previous studies usually assume that each node of the water distribution network has the same importance. However, this assumption does not consider the difference in the impact when a leak occurs at the different nodes on the water distribution network. When the number of sensors is limited, the detection of only leakage events that can cause serious consequences is usually prioritized. This study proposes a multi-objective optimization method for sensor placement while taking into consideration the differences in the risk of leakage for the different nodes. First, five types of risk-based leakage functions are obtained based on the impact on the water distribution network when a leakage occurs at each node. The pressure at the node drops, which causes the water demand and pipe pressure to decrease while the flow rate increases. The multi-objective optimization method is then used to obtain the Pareto front according to the various risk-based leakage functions. Finally, the multi-criteria decision analysis method is used to rank and cluster the Pareto fronts, the optimal solution for each cluster is obtained, and these solutions are compared and analyzed. This study contains a case study that is based on the example water distribution network model C-town. When the number of sensors is limited, a more reasonable optimal sensor placement scheme can be obtained by considering the different risk-based leakage functions of the nodes, with a smaller impact on the water distribution network when a leakage occurs. • A multi-objective optimization sensor placement for leak detection in water distribution system. • Five types of risk-based leakage functions are obtained based on the impact of each leakage. • The impacts of the leakage occurring at each node of the pipe network are compared.

시뮬레이션 기반 대규모 방사능 누출 시 대피전략 수립

South Korea, which has a higher population density than other countries, can lead to massive human costs in the event of a disaster such as a radiation leakage. Accordingly, a number of studies have been conducted to simulate disaster situations or derive optimal evacuation routes. However, most of the road network data used in these studies are virtual or small. In this study, actual road network data from the entire Gyeongsang region were used. In addition, various scenarios for evacuation situations in the event of a radiation leakage were constructed in consideration of various variables such as distance, traffic volume, and modes. The analysis results are summarized as follows. First, as a result of performing the simulation, the shadow evacuation rate does not significantly affect the evacuation time. Second, the farther the destination, the more evacuation time it takes. Third, evacuating to some aid stations rather than the entire aid station can reduce the time required for evacuation. Fourth, in the EPZ area, using buses as much as possible helps reduce evacuation time.

Air Leak Syndrome In Patients With Severe Covid Pneumonia.

There are number of emerging studies that link the air leak syndrome (ALS) with COVID 19 disease but still data to explain the association, incidence and outcome in these patients is lacking. We aim to understand the risk factors and clinical outcome of these air leakage events in COVID 19 patients admitted to our institution. This is a single-centered case series conducted at the COVID unit of the SMBBIT in Karachi, Pakistan. Data collection was done from April 24, 2020 to June 10, 2021. There were 19 patients with severe COVID pneumonia who developed air leaks. Most common finding was subcutaneous emphysema 94%. Four patients (21%) didn't receive positive pressure ventilation in any form. Median time of developing air leak from admission is 5 [2-9] and from PPV is 2 [1-3] days. There was high percentage of mortality 84.5 % in these patients.