Pattern Recognition and Clustering of Transient Pressure Signals for Burst Location

[EN] A large volume of the water produced for public supply is lost in the systems between sources and consumers. An important-in many cases the greatest-fraction of these losses are physical losses, mainly related to leaks and bursts in pipes and in consumer connections. Fast detection and location of bursts plays an important role in the design of operation strategies for water loss control, since this helps reduce the volume lost from the instant the event occurs until its effective repair (run time). The transient pressure signals caused by bursts contain important information about their location and magnitude, and stamp on any of these events a specific "hydraulic signature". The present work proposes and evaluates three methods to disaggregate transient signals, which are used afterwards to train artificial neural networks (ANNs) to identify burst locations and calculate the leaked flow. In addition, a clustering process is also used to group similar signals, and then train specific ANNs for each group, thus improving both the computational efficiency and the location accuracy. The proposed methods are applied to two real distribution networks, and the results show good accuracy in burst location and characterization.Manzi, D.; Brentan, BM.; Meirelles, G.; Izquierdo Sebastián, J.; Luvizotto Jr., E. (2019). Pattern Recognition and Clustering of Transient Pressure Signals for Burst Location. Water. 11(11):1-13. https://doi.org/10.3390/w11112279S1131111Creaco, E., & Walski, T. (2017). Economic Analysis of Pressure Control for Leakage and Pipe Burst Reduction. Journal of Water Resources Planning and Management, 143(12), 04017074. doi:10.1061/(asce)wr.1943-5452.0000846Campisano, A., Creaco, E., & Modica, C. (2010). RTC of Valves for Leakage Reduction in Water Supply Networks. Journal of Water Resources Planning and Management, 136(1), 138-141. doi:10.1061/(asce)0733-9496(2010)136:1(138)Campisano, A., Modica, C., Reitano, S., Ugarelli, R., & Bagherian, S. (2016). Field-Oriented Methodology for Real-Time Pressure Control to Reduce Leakage in Water Distribution Networks. Journal of Water Resources Planning and Management, 142(12), 04016057. doi:10.1061/(asce)wr.1943-5452.0000697Vítkovský, J. P., Simpson, A. R., & Lambert, M. F. (2000). Leak Detection and Calibration Using Transients and Genetic Algorithms. Journal of Water Resources Planning and Management, 126(4), 262-265. doi:10.1061/(asce)0733-9496(2000)126:4(262)Pérez, R., Puig, V., Pascual, J., Quevedo, J., Landeros, E., & Peralta, A. (2011). Methodology for leakage isolation using pressure sensitivity analysis in water distribution networks. Control Engineering Practice, 19(10), 1157-1167. doi:10.1016/j.conengprac.2011.06.004Jung, D., & Kim, J. (2017). Robust Meter Network for Water Distribution Pipe Burst Detection. Water, 9(11), 820. doi:10.3390/w9110820Colombo, A. F., Lee, P., & Karney, B. W. (2009). A selective literature review of transient-based leak detection methods. Journal of Hydro-environment Research, 2(4), 212-227. doi:10.1016/j.jher.2009.02.003Choi, D., Kim, S.-W., Choi, M.-A., & Geem, Z. (2016). Adaptive Kalman Filter Based on Adjustable Sampling Interval in Burst Detection for Water Distribution System. Water, 8(4), 142. doi:10.3390/w8040142Christodoulou, S. E., Kourti, E., & Agathokleous, A. (2016). Waterloss Detection in Water Distribution Networks using Wavelet Change-Point Detection. Water Resources Management, 31(3), 979-994. doi:10.1007/s11269-016-1558-5Guo, X., Yang, K., & Guo, Y. (2012). Leak detection in pipelines by exclusively frequency domain method. Science China Technological Sciences, 55(3), 743-752. doi:10.1007/s11431-011-4707-3Holloway, M. B., & Hanif Chaudhry, M. (1985). Stability and accuracy of waterhammer analysis. Advances in Water Resources, 8(3), 121-128. doi:10.1016/0309-1708(85)90052-1Sanz, G., Pérez, R., Kapelan, Z., & Savic, D. (2016). Leak Detection and Localization through Demand Components Calibration. Journal of Water Resources Planning and Management, 142(2), 04015057. doi:10.1061/(asce)wr.1943-5452.0000592Zhang, Q., Wu, Z. Y., Zhao, M., Qi, J., Huang, Y., & Zhao, H. (2016). Leakage Zone Identification in Large-Scale Water Distribution Systems Using Multiclass Support Vector Machines. Journal of Water Resources Planning and Management, 142(11), 04016042. doi:10.1061/(asce)wr.1943-5452.0000661Mounce, S. R., & Machell, J. (2006). Burst detection using hydraulic data from water distribution systems with artificial neural networks. Urban Water Journal, 3(1), 21-31. doi:10.1080/15730620600578538Covas, D., Ramos, H., & de Almeida, A. B. (2005). Standing Wave Difference Method for Leak Detection in Pipeline Systems. Journal of Hydraulic Engineering, 131(12), 1106-1116. doi:10.1061/(asce)0733-9429(2005)131:12(1106)Liggett, J. A., & Chen, L. (1994). Inverse Transient Analysis in Pipe Networks. Journal of Hydraulic Engineering, 120(8), 934-955. doi:10.1061/(asce)0733-9429(1994)120:8(934)Caputo, A. C., & Pelagagge, P. M. (2002). An inverse approach for piping networks monitoring. Journal of Loss Prevention in the Process Industries, 15(6), 497-505. doi:10.1016/s0950-4230(02)00036-0Van Zyl, J. E. (2014). Theoretical Modeling of Pressure and Leakage in Water Distribution Systems. Procedia Engineering, 89, 273-277. doi:10.1016/j.proeng.2014.11.187Izquierdo, J., & Iglesias, P. . (2004). Mathematical modelling of hydraulic transients in complex systems. Mathematical and Computer Modelling, 39(4-5), 529-540. doi:10.1016/s0895-7177(04)90524-9Lin, J., Keogh, E., Wei, L., & Lonardi, S. (2007). Experiencing SAX: a novel symbolic representation of time series. Data Mining and Knowledge Discovery, 15(2), 107-144. doi:10.1007/s10618-007-0064-zNavarrete-López, C., Herrera, M., Brentan, B., Luvizotto, E., & Izquierdo, J. (2019). Enhanced Water Demand Analysis via Symbolic Approximation within an Epidemiology-Based Forecasting Framework. Water, 11(2), 246. doi:10.3390/w11020246Meirelles, G., Manzi, D., Brentan, B., Goulart, T., & Luvizotto, E. (2017). Calibration Model for Water Distribution Network Using Pressures Estimated by Artificial Neural Networks. Water Resources Management, 31(13), 4339-4351. doi:10.1007/s11269-017-1750-2Adamowski, J., & Chan, H. F. (2011). A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology, 407(1-4), 28-40. doi:10.1016/j.jhydrol.2011.06.013Brentan, B., Meirelles, G., Luvizotto, E., & Izquierdo, J. (2018). Hybrid SOM+ k -Means clustering to improve planning, operation and management in water distribution systems. Environmental Modelling & Software, 106, 77-88. doi:10.1016/j.envsoft.2018.02.013Calinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics - Theory and Methods, 3(1), 1-27. doi:10.1080/0361092740882710

A density-based statistical analysis of graph clustering algorithm performance

This is a pre-copyedited, author-produced version of an article accepted for publication in Journal of Complex Networks following peer review. The version of record: Pierre Miasnikof, Alexander Y Shestopaloff, Anthony J Bonner, Yuri Lawryshyn, Panos M Pardalos, A density-based statistical analysis of graph clustering algorithm performance, Journal of Complex Networks, Volume 8, Issue 3, June 2020, cnaa012, https://doi.org/10.1093/comnet/cnaa012 is available online at: https://doi.org/10.1093/comnet/cnaa012© 2020 The authors. Published by Oxford University Press. All rights reserved. We introduce graph clustering quality measures based on comparisons of global, intra- A nd inter-cluster densities, an accompanying statistical significance test and a step-by-step routine for clustering quality assessment. Our work is centred on the idea that well-clustered graphs will display a mean intra-cluster density that is higher than global density and mean inter-cluster density. We do not rely on any generative model for the null model graph. Our measures are shown to meet the axioms of a good clustering quality function. They have an intuitive graph-theoretic interpretation, a formal statistical interpretation and can be tested for significance. Empirical tests also show they are more responsive to graph structure, less likely to breakdown during numerical implementation and less sensitive to uncertainty in connectivity than the commonly used measures

In-situ and remote monitoring of environmental water quality

Environmental water pollution affects human health and reduces the quality of our natural water ecosystems and resources. As a result, there is great interest in monitoring water quality and ensuring that all areas are compliant with legislation. Ubiquitous water quality monitoring places considerable demands upon existing sensing technology. The combined challenges of system longevity, autonomous operation, robustness, large-scale sensor networks, operationally difficult deployments and unpredictable and lossy environments collectively represents a technological barrier that has yet to be overcome[1]. Ubiquitous sensing envisages many aspects of our environment being routinely sensed. This will result in data streams from a large variety of heterogeneous sources, which will often vary in their volume and accuracy. The challenge is to develop a networked sensing infrastructure that can support the effective capture, filtering, aggregation and analysis of such data. This will ultimately enable us to dynamically monitor and track the quality of our environment at multiple locations. Microfluidic technology provides a route to the development of miniaturised analytical instruments that could be deployed remotely, and operate autonomously over relatively long periods of time (months–years). An example of such a system is the autonomous phosphate sensor[2] which has been developed at the CLARITY Centre, in Dublin City University. This technology, in combination with the availability of low power, reliable wireless communications platforms that can link sensors and analytical devices to online databases and servers, form the basis for extensive networks of autonomous analytical ‘stations’ or ‘nodes’ that will provide high quality information about key chemical parameters that determine the quality of our aquatic environment. The system must also have sufficient intelligence to enable adaptive sampling regimes as well as accurate and efficient decision-making responses. A particularly exciting area of development is the combination of remote satellite/aircraft based monitoring with the in-situ ground-based monitoring described above. Remote observations from satellites and aircraft can provide significant amounts of information on the state of the aquatic environment over large areas. As in-situ deployments of sensor networks become more widespread and reliable, and satellite data becomes more widely available, information from each of these sources can complement and validate the other, leading to an increased ability to rapidly detect potentially harmful events, and to assess the impact of environmental pressures on scales ranging from small river catchments to the open ocean. In this paper, we will assess the current status of these approaches, and the challenges that must be met in order to realise the vision of true internet- or global-scale monitoring of our environment. References: [1] Integration of analytical measurements and wireless communications – Current issues and future strategies. King Tong Lau, Sarah Brady, John Cleary and Dermot Diamond, Talanta 75 (2008) 606–612. [2] An autonomous microfluidic sensor for phosphate: on-site analysis of treated wastewater. John Cleary, Conor Slater, Christina McGraw and Dermot Diamond, IEEE Sensors Journal, 8 (2008) 508-515

Pathload for available bandwidth esti-mation techniques (ABETs) for an efficient telemedicine content transport network

A research article was submitted to International Journal of Advancements in Research & Technology, Volume 2, Issue 8, August-2013The ability to measure end-to-end Available Bandwidth (unused capacity) in the network path is useful for route selection in overlay networks, for QoS verification, network management and traffic engineering. This paper investigates at applying techniques and measurement of Available Bandwidth (AB) in the congestion control for the transmission of an efficient telemedicine content transport network by using an important ABETs tool such as Pathload. This paper discusses measurement and simulation results of wired and wireless networks for the unused capacity in the real telemedicine network path and normal network path. The results can assist an organization or country in estimating the network bandwidth requirements depending on the ability of exchange multimedia data of an organization or country. The logistics could cater implementation of low cost telemedicine applications. The telemedicine systems could include wireless and wired medical interface and communication infrastructure. A simulation has been done to investigate the network quality of servic

Computational fluid dynamics for sub-atmospheric pressure analysis in pipe drainage

[EN] The occurrence of sub-atmospheric pressure in the drainage of pipelines containing an air pocket has been known as a major cause of several serious problems. Accordingly, some system malfunction and pipe buckling events have been reported in the literature. This case has been studied experimentally and numerically in the current research considering objectives for a better understanding of: (i) the emptying process, (ii) the main parameters influencing the drainage, and (iii) the air-water interface deformation. Also, this research demonstrates the ability of a computational fluid dynamic (CFD) model in the simulation of this event. The effects of the air pocket size, the percentage and the time of valve opening on the pressure variation have been studied. Results show the pipeline drainage mostly occurs due to backflow air intrusion. The worst case scenario is associated with a fast valve opening when a tiny air pocket exists in the pipeline.This work is supported by Fundacao para a Ciencia e Tecnologia (FCT), Portugal [grant number PD/BD/114459/2016].Besharat, M.; Coronado-Hernández, OE.; Fuertes-Miquel, VS.; Viseu, MT.; Ramos, HM. (2019). Computational fluid dynamics for sub-atmospheric pressure analysis in pipe drainage. Journal of Hydraulic Research. 58(4):553-565. https://doi.org/10.1080/00221686.2019.1625819S553565584ANSYS FLUENT R19.0 academic [Computer software]. ANSYS, Canonsburg, PA. Retrieved from https://www.ansys.com/academic/free-student-productsApollonio, C., Balacco, G., Fontana, N., Giugni, M., Marini, G., & Piccinni, A. (2016). Hydraulic Transients Caused by Air Expulsion During Rapid Filling of Undulating Pipelines. Water, 8(1), 25. doi:10.3390/w8010025Benjamin, T. B. (1968). Gravity currents and related phenomena. Journal of Fluid Mechanics, 31(2), 209-248. doi:10.1017/s0022112068000133Besharat, M., Coronado-Hernández, O. E., Fuertes-Miquel, V. S., Viseu, M. T., & Ramos, H. M. (2018). Backflow air and pressure analysis in emptying a pipeline containing an entrapped air pocket. Urban Water Journal, 15(8), 769-779. doi:10.1080/1573062x.2018.1540711Besharat, M., Tarinejad, R., Aalami, M. T., & Ramos, H. M. (2016). Study of a Compressed Air Vessel for Controlling the Pressure Surge in Water Networks: CFD and Experimental Analysis. Water Resources Management, 30(8), 2687-2702. doi:10.1007/s11269-016-1310-1Besharat, M., Tarinejad, R., & Ramos, H. M. (2015). The effect of water hammer on a confined air pocket towards flow energy storage system. Journal of Water Supply: Research and Technology-Aqua, 65(2), 116-126. doi:10.2166/aqua.2015.081Besharat, M., Teresa Viseu, M., & Ramos, H. (2017). Experimental Study of Air Vessel Behavior for Energy Storage or System Protection in Water Hammer Events. Water, 9(1), 63. doi:10.3390/w9010063Collins, R. P., Boxall, J. B., Karney, B. W., Brunone, B., & Meniconi, S. (2012). How severe can transients be after a sudden depressurization? Journal - American Water Works Association, 104(4), E243-E251. doi:10.5942/jawwa.2012.104.0055Coronado-Hernández, O., Fuertes-Miquel, V., Besharat, M., & Ramos, H. (2017). Experimental and Numerical Analysis of a Water Emptying Pipeline Using Different Air Valves. Water, 9(2), 98. doi:10.3390/w9020098Coronado-Hernández, O. E., Fuertes-Miquel, V. S., Besharat, M., & Ramos, H. M. (2018). Subatmospheric pressure in a water draining pipeline with an air pocket. Urban Water Journal, 15(4), 346-352. doi:10.1080/1573062x.2018.1475578Coronado-Hernández, O. E., Fuertes-Miquel, V. S., Iglesias-Rey, P. L., & Martínez-Solano, F. J. (2018). Rigid Water Column Model for Simulating the Emptying Process in a Pipeline Using Pressurized Air. Journal of Hydraulic Engineering, 144(4), 06018004. doi:10.1061/(asce)hy.1943-7900.0001446Ding, H., Visser, F. C., Jiang, Y., & Furmanczyk, M. (2011). Demonstration and Validation of a 3D CFD Simulation Tool Predicting Pump Performance and Cavitation for Industrial Applications. Journal of Fluids Engineering, 133(1). doi:10.1115/1.4003196Fuertes-Miquel, V. S., Coronado-Hernández, O. E., Iglesias-Rey, P. L., & Mora-Meliá, D. (2018). Transient phenomena during the emptying process of a single pipe with water–air interaction. Journal of Hydraulic Research, 57(3), 318-326. doi:10.1080/00221686.2018.1492465Izquierdo, J., Fuertes, V. S., Cabrera, E., Iglesias, P. L., & Garcia-Serra, J. (1999). Pipeline start-up with entrapped air. Journal of Hydraulic Research, 37(5), 579-590. doi:10.1080/00221689909498518Laanearu, J., Annus, I., Koppel, T., Bergant, A., Vučković, S., Hou, Q., … van’t Westende, J. M. C. (2012). Emptying of Large-Scale Pipeline by Pressurized Air. Journal of Hydraulic Engineering, 138(12), 1090-1100. doi:10.1061/(asce)hy.1943-7900.0000631Liu, D., & Zhou, L. (2009). Numerical Simulation of Transient Flow in Pressurized Water Pipeline with Trapped Air Mass. 2009 Asia-Pacific Power and Energy Engineering Conference. doi:10.1109/appeec.2009.4918544Martinoia, T., Barreto, C. V., da Rocha, J. C. D. C., Lavoura, J., & Henriques, F. M. P. (2012). Simulation and Planning of Pipeline Emptying Operations. Volume 1: Upstream Pipelines; Project Management; Design and Construction; Environment; Facilities Integrity Management; Operations and Maintenance; Pipeline Automation and Measurement. doi:10.1115/ipc2012-90432Martins, N. M. C., Delgado, J. N., Ramos, H. M., & Covas, D. I. C. (2017). Maximum transient pressures in a rapidly filling pipeline with entrapped air using a CFD model. Journal of Hydraulic Research, 55(4), 506-519. doi:10.1080/00221686.2016.1275046Tijsseling, A. S., Hou, Q., Bozkuş, Z., & Laanearu, J. (2015). Improved One-Dimensional Models for Rapid Emptying and Filling of Pipelines. Journal of Pressure Vessel Technology, 138(3). doi:10.1115/1.4031508Trindade, B. C., & Vasconcelos, J. G. (2013). Modeling of Water Pipeline Filling Events Accounting for Air Phase Interactions. Journal of Hydraulic Engineering, 139(9), 921-934. doi:10.1061/(asce)hy.1943-7900.0000757Vasconcelos, J. G., & Wright, S. J. (2008). Rapid Flow Startup in Filled Horizontal Pipelines. Journal of Hydraulic Engineering, 134(7), 984-992. doi:10.1061/(asce)0733-9429(2008)134:7(984)Wang, L., Wang, F., Karney, B., & Malekpour, A. (2017). Numerical investigation of rapid filling in bypass pipelines. Journal of Hydraulic Research, 55(5), 647-656. doi:10.1080/00221686.2017.1300193Zhou, L., & Liu, D. (2013). Experimental investigation of entrapped air pocket in a partially full water pipe. Journal of Hydraulic Research, 51(4), 469-474. doi:10.1080/00221686.2013.785985Zhou, L., Liu, D., Karney, B., & Wang, P. (2013). Phenomenon of White Mist in Pipelines Rapidly Filling with Water with Entrapped Air Pockets. Journal of Hydraulic Engineering, 139(10), 1041-1051. doi:10.1061/(asce)hy.1943-7900.0000765Zhou, L., Liu, D., & Karney, B. (2013). Investigation of Hydraulic Transients of Two Entrapped Air Pockets in a Water Pipeline. Journal of Hydraulic Engineering, 139(9), 949-959. doi:10.1061/(asce)hy.1943-7900.0000750Zhou, L., Liu, D., Karney, B., & Zhang, Q. (2011). Influence of Entrapped Air Pockets on Hydraulic Transients in Water Pipelines. Journal of Hydraulic Engineering, 137(12), 1686-1692. doi:10.1061/(asce)hy.1943-7900.0000460Zhou, L., Liu, D., & Ou, C. (2011). Simulation of Flow Transients in a Water Filling Pipe Containing Entrapped Air Pocket with VOF Model. Engineering Applications of Computational Fluid Mechanics, 5(1), 127-140. doi:10.1080/19942060.2011.11015357Zukoski, E. E. (1966). Influence of viscosity, surface tension, and inclination angle on motion of long bubbles in closed tubes. Journal of Fluid Mechanics, 25(4), 821-837. doi:10.1017/s002211206600044

Route Planning in Transportation Networks

We survey recent advances in algorithms for route planning in transportation networks. For road networks, we show that one can compute driving directions in milliseconds or less even at continental scale. A variety of techniques provide different trade-offs between preprocessing effort, space requirements, and query time. Some algorithms can answer queries in a fraction of a microsecond, while others can deal efficiently with real-time traffic. Journey planning on public transportation systems, although conceptually similar, is a significantly harder problem due to its inherent time-dependent and multicriteria nature. Although exact algorithms are fast enough for interactive queries on metropolitan transit systems, dealing with continent-sized instances requires simplifications or heavy preprocessing. The multimodal route planning problem, which seeks journeys combining schedule-based transportation (buses, trains) with unrestricted modes (walking, driving), is even harder, relying on approximate solutions even for metropolitan inputs.Comment: This is an updated version of the technical report MSR-TR-2014-4, previously published by Microsoft Research. This work was mostly done while the authors Daniel Delling, Andrew Goldberg, and Renato F. Werneck were at Microsoft Research Silicon Valle