1. 1. Major and Final project presentation Submitted at Budapest University of Technology and Economic, Department of Hydrodynamic Systems Mahbod Shafiei HHSCIQ
2. 2. Introduction The network of distribution mains is nearly the most expensive item of equipment in a water undertaking The analysis of a pipe network can be one of the most important part in designing ,maintenance and optimization of the underground network system few basic principles of fluid mechanics' have been used Conservation of mass or continuity principal The work-energy principal The relation between fluid friction and energy dissipation Simulation and modeling has tried to optimize and monitoring data s
3. 3. Main goal of simulation Distribute water from reservoir to customers in looped network system through economic pipeline with desired pressure
4. 4. Structure of the modeling Population projection Estimate water demand Capacity of storage tank Build up the model Setting the input data Running the model Analyzing datas
5. 5. Population projection Projected population requires certain information on: 1. Historic population counts 2. Birth 3. Deaths and other rates which affect population change. Using various mathematical method to estimate future population in sample area Budapest historic data's have been used constant growth rate method is selected for this project Considering percent of growth for target area Budapest population in 2010 1721556 Budapest population in 2030 1916540
6. 6. Water demand Considering Budapest water demand trend during last years Correlation coefficient between population and water demand Positive correlation between 1991 to 2006 and negative one between 2007 to 2010 which mean by increasing population the water demand has decreased. Considering water demand dependency to temperature Estimating the months which have highest consumption in summer and winter both from 1990 to 2010 Estimating water demand by Q(Daily) or Q(hourly) and Q(yearly)
7. 7. Sample Figures
8. 8. Estimating capacity of storage tank Estimating demand for fire works 126 lit per person per day Calculation R (ratio between production and consumption) Which is 0.8 Capacity of storage = R.Q(daily)+fire demand Capacity of storage tank=193200 m^3/hr in 2011 Estimating capacity of storage tank for sample area by evaluation population and water demand, which is around 8300 m^3
9. 9. Build up the model Effective area: 525.16 sq Km Population density in 2011: 3301.3 per sq Km Population density in 2025: 3649.4 per sq Km
10. 10. Build up the model 2
11. 11. Setting input datas
12. 12. Setting the model parameters Velocity assumption = 0.7 m/sec
13. 13. Running the first model Getting the results for the first model such as pressure, velocity in each link, flow type and friction loss Define a new goal, increasing pressure in network nodes without pump station Lead to low energy consumption Definition of extra loop , extra mass from the nodes with high pressure to nodes which have lower pressure by considering allowable velocity in each link Pressure in each node decrease in upper hand nodes but in farthest nodes is around 2.8 bar Change the pipe diameter s to get better results for velocity
14. 14. New model with extra loop
15. 15. Comparison between the results Pressure has increased in all branches Velocity has decreased but still in allowable limit Total pressure drop has decreased
16. 16. Velocity
17. 17. Pressure drop
18. 18. Daily and night demand Finally daily and night demand with 80% and 20% of maximum demand has been calculated for the model Overload has been researched for the model with 1.2% of maximum demand without extra loop Vacuum has occurred in some nodes (farthest ones) Vacuum can occur as a results of intense fire fighting Recalculating the model with extra loop shows different results
19. 19. Upper branch
20. 20. Middle branch
21. 21. Lower branch
22. 22. conclusion simulation with pump station has done The results has been reported in major project report Comparison between real case in Budapest with simulation has been reported In real Budapest network each loop has been connected to each other by higher diameter pipe
23. 23. Final project Leak phenomena and leak detection 45 million cubic meters are lost daily through water leakage in the distribution networks which is enough to serve nearly 200 million people EPANET software has been used to simulate and monitoring data s for leakage simulation Support vector machine (SVM) has been used to analyze monitored datas and report results Summery of leak detection has been reported ,Such as acoustic method, Computer base method and etc
24. 24. Leak definition Simulate leak as an orifice area Define emitter coefficient Q : flow rate P internal pressure unit weight of water P internal pressure Cd discharge coefficient Above formula leads to emitter coefficient for simulation 0.5 is Pressure exponent for whole loop
25. 25. EPANET simulation
26. 26. Candidate Node for leak E
27. 27. Demand multiplier 24 hour hydraulic time step Define demand multiplier based on real datas for various time step during 24 hour of a day
28. 28. Emitter coefficient Selection Examine computer based method , I tried to use variety of leak flows rates between 0.87 m^3/hr to 8.76 m^3/hr By assuming 0.5 pressure exponent Emitter coefficient 0.01 to 0.1 by step 0.01 Monitoring pressure and flow rate on the nodes lower and upper hand of candidate leak node E Select radius around leak node by around 1200 meter radius Monitoring pressure and flow rate at three time step according to maximum and minimum demand which are 8:00 am 14:00 pm and 21:00 pm
29. 29. Table for leak flow rate and orifice area and emitter coefficient
30. 30. Flow rate at leak condition
31. 31. Table of flow rate leak at 14:00pm
32. 32. Table of flow rate 2 Form datas it can be interpreted that percentage differences has increased by leak rate and it has been increased slightly when distance has closed to leak point
33. 33. Pressure monitoring Pressure difference has shown with different emitter coefficients in three hydraulic time steps Normally pressure has decreased through upper to lower nodes from leak condition node (E) To show better results , difference percentages has been shown ,which can help to interpret better to leak node The results shown that, pressure difference percentage has increased from upper nodes to leak node (E) and then slightly remain constant by lower hand nodes
34. 34. Percentage of change in pressure at 8:00 am
35. 35. Leak location estimation Finding correlation coefficient between flow rate or pressure , and distance to leak node (E) Show direction of relationship (+1 or -1) Finding regression line ( the best fit of datas on scatter plot) Finally finding Standard Error of Estimate Define radios around leak node (E) with radios around 1 km
36. 36. Leak location estimation
37. 37. Correlation of determination for Flow rate and distance at three hydraulic time step
38. 38. Standard error of estimate for flow rate and distance to leak node (E)
39. 39. Leak location prediction (Flow rate)
40. 40. Leak location prediction(Flow rate)
41. 41. Leak location prediction (Pressure)
42. 42. Leak location prediction (pressure)
43. 43. Interpreting results As can be seen from the above tables , percent of differences in flow rate cases have much higher values in compare with pressure cases Standard error of estimate have higher value in flow rate analysis than pressure one , moreover , in pressure case at 8:00 am it has lowest value than other two , due to higher demand at this time Leak predicted distance , has much lower error with pressure datas especially at 8:00 am in compare with flow rate datas Flow rate in links may give better results in leak finding and pressure monitoring have better results in leak location estimation
44. 44. Leak flow rate and orifice area Correlation between emitter coefficient and orifice area has been calculated Leak flow rate , correlation with emitter coefficients and orifice area (mm^2) have been reported
45. 45. Modeling breakage in pipe line Breakage is a fail in pipe line which doesnt lead liquid to lower hand side of failure point ,but in pipes with higher diameter ,bigger orifice area leads to higher rate of leakages and its physically equal to breakage Detection of breakage is nearly easy due to the fact that there are some out signs Sometimes there is ness on the breakage location when pipe size and flow rate is small In modeling , we want to know how much pressure will drop in other links or branches Find a way to reduce the radius of breakage effect to other links and branches
46. 46. Pipe line breakage
47. 47. Simulation of Breakage Simulation of breakage by one out pressure at node(E) Demand consumption at 8:00 am
48. 48. Simulation of Breakage in the first model the extra loop is out of service and out pressure happen with 1 bar Comparison with the case when extra loop is in service
49. 49. Pressure in other Branches
50. 50. Simulation of breakage 2
51. 51. Simulation of breakage 3
52. 52. Simulation of breakage 4
53. 53. Simulation of breakage 5
54. 54. Simulation of breakage 6