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Modelling of water network systems

Modelling of water network systems. Prof Tiit Koppel Department of Mechanics Tallinn University of Technology October 13, 2008. Contents. Department of Mechanics Modelling Reconstruction Leakages Hydraulic Efficiency Calibration.

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Modelling of water network systems

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  1. Modelling of water network systems Prof Tiit Koppel Department of Mechanics Tallinn University of Technology October 13, 2008

  2. Contents Department of Mechanics Modelling Reconstruction Leakages Hydraulic Efficiency Calibration

  3. DepartmentofMechanicsTheFacultyofCivilEngineering Chair of Technical Mechanics Laboratory of Strength of Materials Chair of Hydromechanics Laboratory of Hydromechanics Chair of Applied Mechanics

  4. Research and Applied Projects Target Financing of Estonia SF0140072s08 “Mechanics of fluid-structure interaction”, 2008-2013 ESF Grant 6169 “Non-destructive control of pipes using Lamb waves”, 2005-2008 ESF Grant 6740 “The effect of the shear stiffness on the bending of a passenger ship”, 2006-2009 ESF Grant 7646 “Dynamics of liquid flow in pressure pipes”, 2008-2011

  5. Research and Applied Projects 6th Framework HYDRALAB III Integrated Infrastructure Project “Unsteady friction in pipes and ducts”, Deltares, Delft, 2007-2008 6th Framework HYDRALAB III Integrated Infrastructure Project “Transient vaporous and gaseous cavitation in pipelines”, Deltares, Delft, 2008-2009 SWECO Project AS “Investigation of storm water in Tallinn”, 2008

  6. Research and Applied Projects OÜ Qcell “Flue gas and CO2 for the growing of algae”, 2008-2010 INNOVE Project “Invitation of foreign lecturers to open in the Faculty of Civil Engineering a new specialization – Port Construction and Coastal Engineering”, 2005-2008

  7. City Centre of Tallinn

  8. Pipes

  9. Domestic Water Consumption in Estonia

  10. Water and Wastewater Price

  11. Annual Water Production

  12. Network Model

  13. Subjects of Modelling Model skeletonization Demand allocation using GIS Water quality sampling and calibration Integrating modelling and SCADA systems Genetic-algorithm-based calibration and design Modelling variable-speed pumps Water system security Hydraulic transients Using flow emmiters Integrating GIS with hydraulic modelling

  14. Application of Models • Long-range master planning • Fire protection studies • Water quality investigations • Energy management • System design • Risk analysis • Daily operations

  15. Modeling of Leakages

  16. Modelling of Fire

  17. Age and Length of Pipes

  18. Capacity of the City Centre Network Pipes

  19. Reduction of Pipe Diameters and Water Age

  20. Network Geometry

  21. Water Mains Geometry of City Centre

  22. Mean Water Age Coefficient

  23. Optimal Diameters of Main Pipe

  24. Model Based Leak Detection 1/2 • Deterministic approaches: - most of present methods - single value for leak location and size • Probabilistic approaches: - very little research undertaken - a value with the associated probability for leak location and size is given

  25. Model Based Leak Detection 2/2 Deterministic vs. Probabilistic Leak size: 20 units/second (with probability of 0.1) Leak size: 20 units/second Leak size: 2 units/second Leak size: 2 units/second (with probability of 0.8) AREA 1 AREA 2

  26. SCEM-UA Based ProbabilisticLeak Detection Methodology • Shuffled Complex Evolution Metropolis algorithm • For leak detection purposes, EPANET software linked to the SCEM-UA algorithm in the MATLAB environment • Methodology has been tested with real network data (Rakvere City network)

  27. Examples of the Results • PDF captures the probabilistic beliefs about the parameters in the light of the observed data TRUE = 0.5 SCEM-UA: AV = 0.28 TRUE = 3.00 SCEM-UA: AV = 1.81

  28. The Advantages of the Methodology • Both the leak size and the associated error (i.e. uncertainty) can be determined in a single, optimisation type model run. • The probability density function can be continuously updated when additional information about the system becomes available.

  29. Further Studies Continues • The possibility to use other pressure dependent leakage model equation that includes more info about network itself (seek a broken pipe not just node!) • Alternative WDS models (e.g. extended period simulation) might come into effect when analysing real life systems. • Use of additional type of measurements (e.g. flows). Measuring pressures at low flows does not give valuable information about the network

  30. Diurnal Water Consumption 68 elamut (7 päeva)

  31. Diurnal Water Consumption

  32. Diurnal Leakage Dynamic

  33. Diurnal Leakage Dynamic

  34. Diurnal Leakage Dynamic sL = 1 , kH0 = 10, q0 = 10, β0 = 1, β1 = -0.5, β2 = -0.4

  35. Diurnal Leakage Dynamic

  36. Diurnal Leakage Dynamic

  37. The Hydraulic Efficiency of Network

  38. Optimization of Pumping

  39. Efficiency of Pumping Station

  40. Methodology of ModelCalibrationin TUT • use much faster algorithm than GA. • use preliminary analysis to find unaccounted consumption and real losses for the whole WDS. • includes possibilities to use pressure differentials instead of pressures for calibration. It eliminates influence of wrong elevation of nodes and reduce influence of leakages on calibration results.

  41. Analysis of the Patterns in Order to Find Unaccounted Consumption and Real Losses where - Pattern of i-th demand - Pattern of leakages After this first iteration of calibration and analysis of errors

  42. Analysis of the Dynamics of Errors Dependence of errors on water flow because of a) wrong demand and roughness; b) leakages

  43. Comparison of the Results Obtained by Calibration Using Pressures and Pressure Differentials

  44. Thank You for Your Attention!

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