Leak Parameters Research Articles

Risks and Safety of CO2 Pipeline Transport: A Case Study of the Analysis and Modeling of the Risk of Accidental Release of CO2 into the Atmosphere

In the field of CO2 capture and sequestration, ensuring the safety of pipeline infrastructure is paramount to successful climate change mitigation efforts. This study investigates the dynamics of CO2 dispersion from pipeline systems, assessing not only the transport process but also the physical properties and associated hazards. Advanced simulation techniques are used to model how different states of CO2 (gas, liquid, and supercritical) and varying pipeline characteristics—such as perforation sizes, flow rates, and orientations—affect the dispersion patterns in the event of a leak. Simulations cover a range of atmospheric conditions, emphasizing the role of atmospheric stability and wind speed in shaping dispersion and defining potential impact zones. An analysis of historical pipeline accidents is included to inform risk management strategies. The results show that the orientation of the pipeline has a significant effect on dispersion, with downward leaks causing the largest impact zones, particularly under supercritical conditions. The results highlight the need for adaptive safety strategies that take into account real-time CO2 transport conditions and localized environmental data. By integrating these factors, the study recommends refining safety protocols and emergency response strategies to improve pipeline resilience and public safety against potential leaks. Key findings include the quantification of the relationship between leak parameters and dispersion areas, providing a valuable framework for future safety improvements.

Identification Algorithm for Simultaneous Leaks in a Pipeline

This work addresses the problem of simultaneous leaks’ identification in a main pipeline by assuming a constant friction factor. The solution is based on the invariance of the physical leak parameters for any operation point. By considering a quadratic function of the errors between one and two leaks in a pipeline with a steady-state behavior, a new procedure is proposed for the estimation of four parameters associated with the two leaks. In comparison with the algorithm reported in Peralta and Verde (2023), the new one simultaneously adjusts both unknown positions with their gradients. The advantages of the new algorithm are the reduction of maneuvers for obtaining satisfactory estimated parameters and the straightforward estimation of the parameters associated with the orifices through direct nonlinear functions. The results with synthetic data show a considerable improvement regarding the estimation previously reported.

Pressure gauge network design for leakage detection in water distribution systems using a combination of the pressure residual vector method and the entropy concept

Abstract Detecting leaks in Water Distribution Systems (WDS) is a challenging task for maintenance managers. One widely used method for leak detection is the Pressure Residual Vector method, which involves comparing pressure values with computed values. In this paper, a new approach using probability distribution function and entropy content is proposed to expand the applicability of the previous models. The study began by collecting real-time information on water consumption using automatic electromechanical water meters over a one-year period. Hourly parameters were calculated to determine the temporal pattern of water consumption per capita. Hypothetical leakage was then imposed on each pipe, and the average entropy of water head variations in the nodes was determined. A combinatorial search process was used to suggest a Pressure Gauge Network (PGN) in a WDS. The study found that after a certain number of pressure gauges, the increase in entropy content is insignificant. This quantity may be deemed as the number of pressure gauges required in the PGN. Furthermore, the study showed that the impact of leak parameters can be disregarded when designing a PGN for leak detection purposes. Overall, the study's findings can be used to design a PGN for leak detection in a WDS.

Effects of yogic interventions on cardiopulmonary and endothelial health at high altitude: A prospective longitudinal interventional study

High-altitude sojourn may lead to risk factors for high-altitude disorders and subsequent altitude sickness. This study was aimed to analyse the effects of sea-level yoga practices on individuals when they ascend to high altitudes (HAs). Healthy male subjects were randomly divided into two groups i) yoga group (n = 43) and ii) control group (n = 44). One month of yoga training was given to the yoga group for 30 min daily. The control group was provided daily routine for one month. Blood samples were collected when subjects were in fasting condition at sea level and at high altitude. The pulmonary function, blood pressure, cardiovascular and resting metabolic parameters and estimated markers of endothelial homeostasis were monitored at sea level and then at high altitude. The study revealed that yoga group had enhanced pulmonary functions (p < 0.001), maintained blood pressure, and increased endothelial homeostasis markers such as corin and ACE; and showed decrease in VO2resting and hypoxia-induced endothelial leak parameters such as Angptl4, sKDR. In control group, the blood pressure, VO2, VCO2, and Angptl4 increased, while ACE, corin, and sKDR decreased. Sea-level yogic intervention before ascending to high altitude was found helpful for individuals.

Characteristics of gas leakage and typical dispersion modes for FPSO's process modules

Gas cloud explosion caused by flammable gas leakage is the most serious accidents which threat to structures and humans on floating production, storage, and offloading (FPSO) production process. However, the shape, position and concentration of leakage gas cloud are irregular in the process modules. Some concepts and standards are proposed to convert irregular gas cloud into rectangular cuboid cloud, but neglect the actual sizes and position of cuboid cloud in the modules. The main purposes of this paper are to summarize the gas dispersion modes based on the gas leakage analyses and propose a new method to determine the position and sizes of cuboid cloud. Considering the effect of leak parameters, the gas dispersion characteristics are analyzed in free field and actual process module and eleven modes can be summarized to represent the gas dispersion, the methods for determining the cuboid cloud characteristics of each mode are proposed. The summary of gas dispersion characteristics and modes can be used to determine the spatial distribution of leakage gas cloud and evaluate the leakage accidents consequence. The determination method for cuboid cloud characteristics are applied to be the input information to assess the consequence of flammable gas explosion.

Reducing Mask-Related Pressure Injuries in Pediatric Patients During Noninvasive Ventilation by Targeting Patient Mask Leak: A Quality Improvement Initiative.

To compare outcomes following implementation of patient mask leak range of 25 to 55 liters per minute (lpm) to guide strap tension of sleep masks during noninvasive ventilation against baseline data with no patient mask leak range on number of noninvasive mask-related pressure injuries (PIs). All noninvasively ventilated pediatric acute care patients admitted to general wards and intensive care units between February 1, 2018, and February 1, 2019, in a quaternary hospital in the southwest United States. Using the Plan Do Study Act model, we employed an intervention to examine the rate of PIs per noninvasive positive pressure ventilation (NIPPV) days and patient-days before and after implementation of patient mask leak parameters between 25 and 55 lpm to guide mask strap tension. Since patients are at an increased risk of sleep mask-related PIs only when on NIPPV, we sought to describe that as number or PIs per number of days at risk, described as NIPPV days, and patient days which is the traditional denominator for wounds and hospital-acquired conditions. Preintervention, 6 out of 115 subjects (5.2 %) incurred PI at a rate of 0.51 per 100 NIPPV days or 0.26 per 1000 patient-days. Of the 1932 NIPPV days since education was completed, only 1 subject out of 87 (1.1%) incurred a high-stage PI (0.05/100 NIPPV days-a 96.79% reduction or 0.05/1000 patient-days-a 92.86% reduction). Upon reaching more than 90% compliance with patient mask leak range in December 2018, 1221 NIPPV days resulted in 0 noninvasive mask-related PIs. Greater than 90% compliance with a patient mask leak of 25 to 55 lpm allowed us to successfully achieve our hospital's operating plan goal of 0.15 of 1000 patient-days within this group. Maintaining a patient mask leak range between 25 and 55 lpm should be considered as part of proper fit for pediatric patients using NIPPV with a mask. Additional work is needed to assess this leak range in more children and in studies conducted at multiple sites.

Consequence Analysis of Accidental LNG Release on the Collided Structure of 500 cbm LNG Bunkering Ship

The growing demand for liquefied natural gas (LNG)-fueled ships necessitates the establishment of an LNG bunkering facility. Ship-to-ship (STS) is one of the most practical forms of LNG bunkering systems. Although there are benefits to the LNG bunkering of ships, risk and safety issues are a concern due to the volatile cargo. Ship collision could result in accidental LNG release. The purpose of this study was to build LNG leakage scenarios, establish critical zones based on gas concentrations, and estimate the temperature reduction in a bunkering ship’s structure resulting from the use of cryogenic fluid. The condition of a target ship’s structure, both intact and when damaged due to collision, was considered. Leak size, leak direction, leak position, release rate, and reservoir pressure were included as leak parameters, and environmental parameters, such as the wind direction, wind speed, and ambient temperature, were also included. The release duration was set based on the shutdown duration of the emergency shutdown valve (ESD). A total of 72 leakage scenarios were generated for the main CFD analysis. Convergence tests were conducted to determine the appropriate grid and iteration numbers for a computational fluid dynamics (CFD) simulation. The gas dispersion characteristics and the cryogenic flow impact on the LNG bunkering ship’s structure are discussed through a parametric study.

Cellular Metabolism and Bioenergetic Function in Human Fibroblasts and Preadipocytes of Type 2 Familial Partial Lipodystrophy.

LMNA mutation is associated with type-2 familial partial lipodystrophy (FPLD2). The disease causes a disorder characterized by anomalous accumulation of body fat in humans. The dysfunction at the molecular level is triggered by a lamin A/C mutation, impairing the cell metabolism. In human fibroblasts and preadipocytes, a trend for ATP production, mainly supported by mitochondrial oxidative metabolism, is detected. Moreover, primary cell lines with FPLD2 mutation decrease the mitochondrial ATP production if compared with the control, even if no differences are observed in the oxygen consumption rate of bioenergetic parameters (i.e., basal and maximal respiration, spare respiratory capacity, and ATP turnover). Conversely, glycolysis is only inhibited in FPLD2 fibroblast cell lines. We notice that the amount of ATP produced in the fibroblasts is higher than in the preadipocytes, and likewise in the control, with respect to FPLD2, due to a more active oxidative phosphorylation (OXPHOS) and glycolysis. Moreover, the proton leak parameter, which characterizes the transformation of white adipose tissue to brown/beige adipose tissue, is unaffected by FPLD2 mutation. The metabolic profile of fibroblasts and preadipocytes is confirmed by the ability of these cell lines to increase the metabolic potential of both OXPHOS and glycolysis under energy required independently by the FPLD2 mutation.

Exploring the key facets of leakage dynamics in water distribution networks: Experimental verification, hydraulic modeling, and sensitivity analysis

Hydraulic solvers are extensively used for the real-time operational analysis and asset management of water distribution networks. However, the influence of leakage on network hydraulics is still not fully understood, and limited experimental verification concerning leakage modeling and calibration efficacy of hydraulic solvers is presently available. To address these limitations, this research develops a novel strategy to quantitatively assess the efficacy of the popular hydraulic solver, EPANET 2.0 in emulating the leakage dynamics through an extensive experimental study conducted on a realistic sensor-equipped network prototype followed by comprehensive leakage modeling and analysis. The network response to diverse leak scenarios is experimentally analyzed, and the inferences derived are utilized to configure the leak models. A stage-wise calibration routine of the base and leak models is executed, which exhibits reasonable accuracy with mean absolute errors of 0.011 and 0.060, respectively. The calibrated leak models manifest satisfactory performance concerning the simulated system pressure and effectively predict the leak flow trends at all leakage levels. The sensitivity matrix of the leak models also captures the variation in the localized and global impact of leak levels on system flow and pressure concerning different leak scenarios. Thus, this research experimentally verifies the efficacy of leakage modeling using EPANET 2.0 and delineates the potential challenges concerning efficient calibration and performance analysis of leak models using the predefined leak parameters. The implications of this study can be instrumental for utility managers and researchers in configuring improved leak models using hydraulic solvers and strategizing model-based leakage management plans.

EKF-based observers for multi-leak diagnosis in branched pipeline systems

The present work deals with the multi-leak diagnosis problem in a branched pipeline configuration as in water distribution systems. Here, it is assumed that the flow rate and pressure head measurements are available upstream and at all delivering points of the network. The proposed Leak Detection and Isolation (LDI) scheme basically involves two essential steps: leak region identification based on flow-rate residuals with a related k-Nearest Neighbors (k-NN) classifier, and then leak parameter identification (magnitude and position) via the use of the so-called Extended Kalman Filters (EKFs) for each leak based on a simple generic model and fed with pressure head estimations provided by an initial EKF. For the sake of illustration, successful experimental results of a two sequential leak scenario are provided using databases generated by a test bed plant with two branchings built at the Tuxtla Gutiérrez Institute of Technology.

124: DETERMINING SEVERITY OF ESOPHAGEAL ANASTOMOTIC LEAK IN PATIENTS AFTER ESOPHAGECTOMY: DEVELOPMENT OF THE SEAL SCORE

Abstract Background and aim Anastomotic leakage (AL) is a common and severe complication after esophagectomy, but it is largely unknown how to assess leakage severity at the time of diagnosis. We aimed to develop the Severity of Esophageal Anastomotic Leak (SEAL) score, a tool to predict leakage severity at the time of diagnosis. Methods The TENTACLE—Esophagus study is an international retrospective cohort study, in which 1509 patients with AL after esophagectomy between 2011 and 2019 were included in 71 centers from 20 countries. Potential predictors of mortality were patient parameters at diagnosis (e.g. diet at diagnosis, organ failure) and leakage parameters (e.g. fluid collections, gastric tube condition, leakage circumference). Primary outcome was 90-day mortality. Logistic regression was used to develop the SEAL score, comprising four grades of leakage severity (i.e. mild, moderate, severe and critical). Severity grade cut-offs were predefined and based on the individually predicted risk of 90-day mortality: 0–5%, 5–15%, 15–25% and 25–100%, respectively. Internal validation was performed by bootstrapping. The study protocol is accessible at www.tentaclestudy.com. Results The 90-day mortality rate in the included 1509 patients was 11.7%. The model had good discrimination (c-index 0.77; 95%CI 0.73–0.82) and calibration (calibration intercept 0.00; 95%CI -0.17– 0.17; calibration slope 1.14; 95%CI 0.95–1.33) after internal validation. The model classified 25.9% leakage patients as mild, 50.6% as moderate, 13.1% as severe and 10.4% as critical. The in-hospital, 30-day, 90-day and 180-day mortality rates, length of (ICU) stay, time to leak healing, Comprehensive Complication Index and Esophageal Complication Consensus Group classification worsened statistically significantly with higher classes of AL severity. Conclusion Anastomotic leakage is a severe complication with a 90-day mortality rate of 11.7%. The Severity of Esophageal Anastomotic Leak (SEAL) score is a tool which combines 12 patient and leak parameters at time of leak diagnosis and comprises four classes of leakage severity based on individual predictions of mortality. The SEAL score is instrumental in understanding AL severity in clinical practice and may be used to correct for leakage severity when assessing the efficacy of different treatments of AL after esophagectomy.

Two Simultaneous Leak Diagnosis in Pipelines Based on Input-Output Numerical Differentiation.

This paper addresses the two simultaneous leak diagnosis problem in pipelines based on a state vector reconstruction as a strategy to improve water shortages in large cities by only considering the availability of the flow rate and pressure head measurements at both ends of the pipeline. The proposed algorithm considers the parameters of both leaks as new state variables with constant dynamics, which results in an extended state representation. By applying a suitable persistent input, an invertible mapping in x can be obtained as a function of the input and output, including their time derivatives of the third-order. The state vector can then be reconstructed by means of an algebraic-like observer through the computation of time derivatives using a Numerical Differentiation with Annihilatorsconsidering its inherent noise rejection properties. Experimental results showed that leak parameters were reconstructed with accuracy using a test bed plant built at Cinvestav Guadalajara.

Model for Cryogenic Flashing LNG Leak

The growth of liquefied natural gas (LNG)’s importance for curbing greenhouse gas emissions has increased the interest in understanding LNG’s risks, particularly regarding small-diameter leaks (&lt;25 mm). Recently, INERIS performed a series of pressurized LNG release experiments for orifice sizes of up to 9 mm. Based on INERIS findings and the Isenthalpic Homogeneous Equilibrium Model (HEM), this paper created a leak model for flashing LNG leak. The leak model consists of nine equations and quantifies leak parameters for risk assessment. One potential use of this leak model is providing an equivalent leak boundary condition for computational fluid dynamic (CFD) simulation to predict gas dispersion. Using the leak model as input, FLACS gas dispersion simulation was carried out for one INERIS experiment leak case. Compared to TR56, the Singapore safety guideline for LNG bunkering, the dispersion result does not contradict the expected plume reach. Further validation with risk analysis tools and actual experiments is needed to confirm that the leak model is fit for use.

Variable leaky steepest descent algorithm for autoregressive fading estimation in OFDM systems

This paper presents an adaptive algorithm for unbiased estimation of autoregressive Rayleigh flat fading parameters in OFDM systems. To this end, a variable leaky steepest descent algorithm is proposed. The authors use a variable leak parameter in the algorithm to remove the noise induced bias from the autoregressive estimates. Theoretical performance analyses of the proposed method are conducted. Moreover, computer simulations are presented to evaluate the performance of the proposed algorithm and to compare it with other existing methods.

AutoFlow\xae versus volume-controlled ventilation for laparoscopic gynecological surgery using LMA\xae ProSeal\u2122: a randomized controlled trial

BackgroundDuring laparoscopic gynecological surgery, increased peak airway pressure (PAWP) can cause airway leak upon ventilation with the LMA® ProSeal™. We hypothesized that compared with the use of volume-controlled ventilation (VCV), the use of the AutoFlow® mode would decrease PAWP and airway leak during laparoscopic gynecological surgery with LMA ProSeal.MethodsThis single-center, randomized, controlled trial allocated 80 adult women undergoing elective laparoscopic gynecological surgery to one of two groups, namely, the AutoFlow group or the VCV group. Ventilation settings for both groups were 8 ml/kg of tidal volume and 5 cmH2O of positive end-expiratory pressure, and respiratory rate was adjusted to maintain end-tidal carbon dioxide at 35–40 mmHg. Airway leak, PAWP, and other ventilatory parameters and vital signs were recorded at four timepoints (1, 1 min after insertion of the gastric tube; 2, 2 min after intravenous administration of rocuronium 0.6–0.8 mg/kg; 3, 1 min after initiation of pneumoperitoneum; and 4, 1 min after changing to the Trendelenburg position). The primary outcome was PAWP during pneumoperitoneum and in the Trendelenburg position, whereas the secondary outcomes included PAWP at other timepoints and airway leak development. We used the Mann–Whitney U test for PAWP and Fisher’s exact test for comparing airway leak among the groups.ResultsData from 40 patients in the AutoFlow group and 39 in the VCV group were used for analysis. PAWP at pneumoperitoneum pressure and in the Trendelenburg position was significantly lower in the AutoFlow group than in the VCV group [median (interquartile range), 16 (15–18) cmH2O vs. 18 (17–19) cmH2O; P < 0.001]. Similarly, patients in the AutoFlow group showed lower PAWP at the other three timepoints measured. Airway leak occurred in four patients in the AutoFlow group and in two patients in the VCV group; however, this incidence was not significantly different (P = 0.68).ConclusionsEven though AutoFlow ventilation decreased PAWP, it did not reduce the incidence of airway leak compared with VCV during laparoscopic gynecological surgery with the LMA ProSeal.Trial registrationUMIN Clinical Trials Registry, identifier UMIN000023173.

Data-Driven Leak Localization in Urban Water Distribution Networks Using Big Data for Random Forest Classifier

In the present paper, a Random Forest classifier is used to detect leak locations on two different sized water distribution networks with sparse sensor placement. A great number of leak scenarios were simulated with Monte Carlo determined leak parameters (leak location and emitter coefficient). In order to account for demand variations that occur on a daily basis and to obtain a larger dataset, scenarios were simulated with random base demand increments or reductions for each network node. Classifier accuracy was assessed for different sensor layouts and numbers of sensors. Multiple prediction models were constructed for differently sized leakage and demand range variations in order to investigate model accuracy under various conditions. Results indicate that the prediction model provides the greatest accuracy for the largest leaks, with the smallest variation in base demand (62% accuracy for greater- and 82% for smaller-sized networks, for the largest considered leak size and a base demand variation of ±2.5%). However, even for small leaks and the greatest base demand variations, the prediction model provided considerable accuracy, especially when localizing the sources of leaks when the true leak node and neighbor nodes were considered (for a smaller-sized network and a base demand of variation ±20% the model accuracy increased from 44% to 89% when top five nodes with greatest probability were considered, and for a greater-sized network with a base demand variation of ±10% the accuracy increased from 36% to 77%).

The Leaky Integrating Threshold and its impact on evidence accumulation models of choice response time (RT).

A common assumption in choice response time (RT) modeling is that after evidence accumulation reaches a certain decision threshold, the choice is categorically communicated to the motor system that then executes the response. However, neurophysiological findings suggest that motor preparation partly overlaps with evidence accumulation, and is not independent from stimulus difficulty level. We propose to model this entanglement by changing the nature of the decision criterion from a simple threshold to an actual process. More specifically, we propose a secondary, motor preparation related, leaky accumulation process that takes the accumulated evidence of the original decision process as a continuous input, and triggers the actual response when it reaches its own threshold. We analytically develop this Leaky Integrating Threshold (LIT), applying it to a simple constant drift diffusion model, and show how its parameters can be estimated with the D*M method. Reanalyzing 3 different data sets, the LIT extension is shown to outperform a standard drift diffusion model using multiple statistical approaches. Further, the LIT leak parameter is shown to be better at explaining the speed/accuracy trade-off manipulation than the commonly used boundary separation parameter. These improvements can also be verified using traditional diffusion model analyses, for which the LIT predicts the violation of several common selective parameter influence assumptions. These predictions are consistent with what is found in the data and with what is reported experimentally in the literature. Crucially, this work offers a new benchmark against which to compare neural data to offer neurobiological validation for the proposed processes. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Uniformly optimal multi-sensor design in pipe networks for transient-based leakage localization

The design of sensor placement in water supply pipe networks for the purpose of transient-based leakage localization is investigated. Each sensor is placed at where the Fisher information of measurement concerning leaks is maximized; equivalently, the Cramér–Rao lower bound (CRLB) representing the lower limit of leak localization error is minimized. An explicit algorithm for computing the CRLB with respect to leak parameters in a general pipe network is developed. The presence of leak is considered as stochastic and modeled in a probabilistic framework by assuming that the leak location follows a probabilistic distribution with a support on the whole network. The optimal distribution of the sensors is then computed via a quasi-Monte-Carlo simulation, where the expectation of the CRLB of leak localization error is minimized. The proposed sensor placement methodology is optimal uniformly over all the leak localization methods, all the potential sensor locations and all the situations of leaks. Four examples from a simple single-pipe system to a complex pipe network are presented to illustrate the proposed methodology.

Integrated hydrogeophysical modelling and data assimilation for geoelectrical leak detection

Time-lapse electrical resistivity tomography (ERT) measurements provide indirectobservations of hydrological processes in the Earth's shallow subsurface at high spatial and temporal resolution. ERT has been used in the past decades to detect leaks and monitor the evolution of associated contaminant plumes. Specifically, inverted resistivity images allow visualization of the dynamic changes in the structure of the plume. However, existing methods do not allow the direct estimation of leak parameters (e.g. leak rate, location, etc.) and their uncertainties. We propose an ensemble-based data assimilation framework that evaluates proposed hydrological models against observed time-lapse ERT measurements without directly inverting for the resistivities. Each proposed hydrological model is run through the parallel coupled hydro-geophysical simulation code PFLOTRAN-E4D to obtain simulated ERT measurements. The ensemble of model proposals is then updated using an iterative ensemble smoother. We demonstrate the proposed framework on synthetic and field ERT data from controlled tracer injection experiments. Our results show that the approach allows joint identification of contaminant source location, initial release time, and solute loading from the cross-borehole time-lapse ERT data, alongside with an assessment of uncertainties in these estimates. We demonstrate a reduction in site-wide uncertainty by comparing the prior and posterior plume mass discharges at a selected image plane. This framework is particularly attractive to sites that have previously undergone extensive geological investigation (e.g., nuclear sites). It is well suited to complement ERT imaging and we discuss practical issues in its application to field problems.

Diagnosis of the single leakage in the fluid pipeline through experimental study and CFD simulation

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