1. Major and Final project presentation
Submitted at Budapest University of Technology and
Economic, Department of Hydrodynamic Systems
Mahbod Shafiei
HHSCIQ

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. Main goal of simulation
 Distribute water from reservoir to customers in looped
network system through economic pipeline with desired
pressure

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 data’s

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. 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. Sample Figures

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. 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. Build up the model 2

11. Setting input data’s

12. Setting the model parameters
Velocity assumption = 0.7 m/sec

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. New model with extra loop

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. Velocity

17. Pressure drop

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. Upper branch

20. Middle branch

21. Lower branch

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. 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 data’s and report results
 Summery of leak detection has been reported ,Such as
acoustic method, Computer base method and etc…

24. Leak definition
Simulate leak as an orifice area
Define emitter coefficient
Q : flow rate P internal pressure γ 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. EPANET simulation

26. Candidate Node for leak ‘’E’’

27. Demand multiplier
24 hour hydraulic time step
Define demand multiplier based on real data’s for various
time step during 24 hour of a day

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. Table for leak flow rate and orifice
area and emitter coefficient

30. Flow rate at leak condition

31. Table of flow rate leak at 14:00pm

32. Table of flow rate 2
 Form data’s 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. Pressure monitoring
Pressure difference has shown with different emitter
coefficient’s 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. Percentage of change in pressure
at 8:00 am

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 data’s on scatter
plot)
Finally finding Standard Error of Estimate
Define radios around leak node (E) with radios around 1
km

36. Leak location estimation

37. Correlation of determination for Flow rate and
distance at three hydraulic time step

38. Standard error of estimate for flow
rate and distance to leak node (E)

39. Leak location prediction (Flow rate)

40. Leak location prediction(Flow rate)

41. Leak location prediction (Pressure)

42. Leak location prediction (pressure)

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 data’s especially at 8:00 am in compare with
flow rate data’s
Flow rate in links may give better results in leak finding
and pressure monitoring have better results in leak
location estimation

44. Leak flow rate and orifice area
Correlation between emitter coefficient and orifice area
has been calculated
Leak flow rate , correlation with emitter coefficient’s and
orifice area (mm^2) have been reported

45. Modeling breakage in pipe line
Breakage is a fail in pipe line which doesn’t 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. Pipe line breakage

47. Simulation of Breakage
Simulation of breakage by one out pressure at node(E)
Demand consumption at 8:00 am

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. Pressure in other Branches

50. Simulation of breakage 2

51. Simulation of breakage 3

52. Simulation of breakage 4

53. Simulation of breakage 5

54. Simulation of breakage 6