Transcript of final work presentation
- 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 datas
- 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 datas
- 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 datas 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 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 datas 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 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. 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. 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 datas 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 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. 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. 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. 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