Numerical Model for Finding Leaks in Pipe Networks PDF Download
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Author: Ranko Stojan Pudar
Publisher:
ISBN:
Category :
Languages : en
Pages : 206
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Author: Ranko Stojan Pudar
Publisher:
ISBN:
Category :
Languages : en
Pages : 206
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Author: Joseph Cornelius Van der Walt
Publisher:
ISBN:
Category :
Languages : en
Pages : 216
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In 2012, the National Non-Revenue Water assessment revealed that South Africa has 37% of non-revenue water. With the steadily growing demand for this scarce resource, the detection of leaks in pipe networks is becoming more important. Currently, in South Africa the primary method of detecting leaks is to install pressure management systems and monitoring minimum night time ows [1]. The pressure- ow deviation method, can be used to formulate an inverse analysis model based leak detection problem. This problem can then be solved using Arti cial Neural Networks, Support Vector Machines and other optimization methods. With EPANET, di erent networks were tested to compare these methods to nding leaks, using an inverse analysis formulated problem. Four di erent numerical networks were modeled and tested, a simple single pipe network, a small agricultural site, a distribution network proposed and investigated by Poulakis et al. [2] and the simulated model of the experimental network that was designed and commissioned during the study in our laboratory. From the numerical investigation, it was found that the optimization methods struggled to nd solutions for simple networks with in nite number of solutions for the problem. For more complex numerical networks, it was seen that the Support Vector machine and the Arti cial Neural Networks trained to the averages of their respective data sets. Errors to ensure an accurate solution found by these algorithms were calculated as 2:6% for the numerical experimental network. The experimental network consisted of six possible leaking pipes, each having a length of 3m and a diameter of 10mm. Three leak cases were tested with diameters of 3mm and 2mm. Overall, the Support Vector machine could locate the leaking pipe with the best accuracy, while the minimizing of non-regularized error could calculate the size and location of the leak the most accurately. Multiple leak cases were measured with the experimental network. The Support Vector machine was tested on these measurements, where it was found that two of the three leak cases could be solved with relative accuracies. Sensor usage optimization was completed on the measurements for the experimental network, where it was found that the leaks could be classi ed correctly with probabilities higher than 98% if only two sensors were used in the training of the SVM instead of all twelve. Overall this method of leak detection shows promise for certain applications in the future. With practical applications on water distribution, transportation, and agricultural networks.
Author:
Publisher:
ISBN:
Category : Pipe
Languages : en
Pages : 176
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Author: Abdulrahman Amin Alawadhi
Publisher:
ISBN:
Category :
Languages : en
Pages : 95
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Leakage is an undesired abnormality that causes economical losses and impacts the environment. Leak detection tests in pipe networks are usually interpreted using water-hammer equations (WHE). These equations are nonlinear hyperbolic partial differential equations (PDEs) used to describe transient flows in pipes. The associated uncertainties in initial and boundary conditions, parameters, and leak strength and location increases the stochastic behavior of these equations. The method of distributions is used to derive a deterministic PDE for probability density function (PDF) of pressure head and flow velocity under uncertain initial conditions. The derivation requires a closure approximation that ensures its consistency with the mean and the variance of the state variables. A series of numerical experiments confirms the computational gain of this method over Monte Carlo simulations. The PDF of pressure head obtained using the method of distributions serves as a prior PDF for data assimilation. Bayesian framework is used to update this distribution with a statistical model for observations obtained from the data collected by a pressure sensor. The result is posterior PDFs for leak location and leak strength. Series of numerical experiments are conducted for a single pipe and pipe networks under uncertain initial velocity and measurement noise to identify leak location and leak strength. The results are compared with the best fitness function that is used in inverse transient analysis.
Author: Raido Puust
Publisher:
ISBN: 9789985597286
Category :
Languages : en
Pages : 132
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Author: J. P. Vitkovsky
Publisher:
ISBN: 9780863965920
Category : Calibration
Languages : en
Pages : 96
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Author: Mohammad Nawwar Shamout
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ISBN:
Category :
Languages : en
Pages : 0
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Author: Mohammad Nawwar Shamout
Publisher:
ISBN:
Category :
Languages : en
Pages : 308
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Author: Samer El-Zahab
Publisher:
ISBN:
Category :
Languages : en
Pages : 238
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Today, 844 million humans around the world have no access to safe drinking water. Furthermore, every 90 seconds, one child dies from water-related illnesses. Major cities lose 15% - 50% of their water and, in some cases, losses may reach up to 70%, mostly due to leaks. Therefore, it is paramount to preserve water as an invaluable resource through water networks, particularly in large cities in which leak repair may cause disruption. Municipalities usually tackle leak problems using various detection systems and technologies, often long after leaks occur; however, such efforts are not enough to detect leaks at early stages. Therefore, the main objectives of the present research are to develop and validate a leak detection system and to optimize leak repair prioritization. The development of the leak detection models goes through several phases: (1) technology and device selection, (2) experimental work, (3) signal analysis, (4) selection of parameters, (5) machine learning model development and (6) validation of developed models. To detect leaks, vibration signals are collected through a variety of controlled experiments on PVC and ductile iron pipelines using wireless accelerometers, i.e., micro-electronic mechanical sensors (MEMS). The signals are analyzed to pinpoint leaks in water pipelines. Similarly, acoustic signals are collected from a pilot project in the city of Montreal, using noise loggers as another detection technology. The collected signals are also analyzed to detect and pinpoint the leaks. The leak detection system has presented promising results using both technologies. The developed MEMS model is capable of accurately pinpointing leaks within 12 centimeters from the exact location. Comparatively, for noise loggers, the developed model can detect the exact leak location within a 25-cm radius for an actual leak. The leak repair prioritization model uses two optimization techniques: (1) a well-known genetic algorithm and (2) a newly innovative Lazy Serpent Algorithm that is developed in the present research. The Lazy Serpent Algorithm has proved capable of surpassing the genetic algorithm in determining a more optimal schedule using much less computation time. The developed research proves that automated real-time leak detection is possible and can help governments save water resource and funds. The developed research proves the viability of accelerometers as a standalone leak detection technology and opens the door for further research and experimentations. The leak detection system model helps municipalities and water resource agencies rapidly detect leaks when they occur in real-time. The developed pinpointing models facilitate the leak repair process by precisely determine the leak location where the repair works should be conducted. The Lazy Serpent Algorithm helps municipalities better distribute their resources to maximize their desired benefits.
Author: Wadie R. Chalgham
Publisher:
ISBN:
Category : Leak detectors
Languages : en
Pages : 84
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