30

2. LITERATURE SURVEY

2.1 GENERAL

A detailed report on the literature review covering relevant studies

available so far is presented in this chapter. The investigations were

made on flow through water distribution pipes regarding various

losses, hydraulic friction factor and unsteady turbulent flow.

Formation of biofilm and scale/deposits, their effects and prevention

in water pipe lines were also reported in literature. Research work has

been done on the various factors influencing the formation of scale in

water distributing pipes. Some modeling studies have been carried out

by eminent researchers in the field of hydraulic engineering. The

applications of an Artificial Neural Networks technique in water

resources engineering problems are also reviewed. Special emphasis is

given to the flow through PVC pipes.

Section 2.2 gives the loss coefficients for small diameter pipes.

Section 2.3 deals with the various factors influencing corrosion rate

and releasing of lead and iron from drinking water pipes. Section 2.4

enumerates the effects of biofilm/scale formation in pipes. Sections

2.5 and 2.6 mainly deal with the investigations on friction factor for

pipe flow and domestically used PVC pipes respectively. Section 2.7

describes some modeling studies on pipe flow and water resources

applications. Finally the chapter ended with an explanation on the

importance of present study.

31

2.2 LOSS COEFFICIENTS FOR SMALL DIAMETER PIPES

According to Gabriel Echavez (1997), there is an increase in losses

coefficient with age for small diameter pipes. The hydraulic behavior of

galvanized iron and copper pipes, of ages between 15 and 50 years

and of up to 50.8mm (2 in.) diameter was studied. It was found that

in the galvanized iron pipes, the diameter is reduced and both the

roughness and the losses coefficient increase with age. The increase in

roughness is substantial and follows a parabolic law instead of the

linear one usually found in the literature. For the copper pipes there is

no appreciable change in these variables with age. The apparition of

local narrowness or stenosis, due to age may cause considerable

increase in the losses coefficient and eventually even close the pipe

completely.

V.V.Nageswara Rao and G.K.Viswanadh (2004), reported the

variation of hydraulic loss coefficient in PVC pipes. The various

dissolved particles like salts, present in water, generally tend to

deposit on the walls of the pipes. Such salt deposits may strengthen

over a period of time, thus hindering the flow as well as the friction

factor. In the case of PVC pipes, it is observed that as the usage

increases, the diameter of the pipe is getting reduced because of the

deposits that are taking place along the inner walls of the pipes. The

deposition of salts again influenced by the factors such as quality of

water (presence of salts), velocity of flow, diameter of the pipe,

temperature changes etc., The Hazen-William coefficient may be

varying with the age of the pipe as the deposits are increasing within

32

the pipe. The H-W coefficient for new PVC pipe may be observed as

150 and for 10 years aged pipe, it was 133 and 6 years aged pipe it

was observed to be 142.

2.3 FACTORS INFLUENCING CORROSION SCALES IN WATER

DISTRIBUTION PIPES

In recent times, internal corrosion in water pipes is one of the

major contamination sources of tap water. Internal corrosion in

distribution networks has a close relation with the quality of water

flowing through the pipe. From the experimental investigation, it was

found that Dissolved Oxygen (DO) is the most critical parameter on

the corrosion of metal pipes. Internal corrosion of water pipes could be

effectively prevented by reducing DO concentration. The corrosion

products on the surface of copper pipe were appeared as light brown

and/or blue green color.

Corrosion rates for Galvanized Steel Pipe (GSP), Carbon Steel Pipe

(CSP) and Ductile Cast Iron Pipe (DCIP) were decreased to 72%, 75%

and 91% by reducing DO concentration from 9±0.5 mg/lit to 2±0.5

mg/lit respectively. Dissolved Oxygen significantly influences the

corrosion properties of water pipes. In DCIP, red rust (ferric oxide) was

visually observed on the surface of the pipe in the condition of 9±0.5

mg/lit of DO after 10 weeks of experiment.

Corrosion rates of water pipes decreased as the concentration of

hardness increased. Calcium carbonate deposited in the interface of

pipe surface and water. The deposition layer played a role to hinder

33

the electrochemical reaction between metallic pipes and water.

However, the continuous injection of calcium carbonate would result

in the formation of thick scale in the metallic pipes, which cause

serious increase of pressure.

Since pH is related with the formation of scale on the internal

surface of water pipe and metal solubility in water, pH adjustment has

been used as a corrosion control strategy of water pipes. (Haeryong

Jung, Unji Kim et al., 2009)

According to Lasheen MR et al., (2008), some factors influences

lead and iron release from drinking water pipes. It has been observed

by them that, the effect of stagnation time, pipe age, pipe material and

water quality parameters such as pH, alkalinity and chloride to sulfate

mass ratio on lead and iron release from different types of water pipes

used in Egypt. The different types of pipe materials used are Poly Vinyl

Chloride (PVC), Poly Propylene (PP) and Galvanized Iron (GI). Low pH

increases lead and iron from pipes. Lead and iron release decreases

with pH and alkalinity increases. It also increases with increasing

chloride to sulfate mass ratio in all the pipes. Lead and iron release

increases and then this release decreases with time. In general, GI

pipes showed to be the most effected by water quality parameters and

the highest iron release. PVC pipes are the most lead releasing pipes,

while PP pipes are the least releasing.

34

Cerrato JM, Reyes LP et al., (2006), studied the effect of PVC and

Iron materials on Mn (II) deposition in drinking water distribution

systems. The influence of Iron and PVC pipe materials on the

concentrations of soluble iron and manganese and the composition of

scales formed on PVC and Iron pipes were determined. Total Fe

concentrations were highest in water from iron pipes. Water samples

obtained from PVC pipes showed higher total Mn concentrations and

more black color than that obtained from iron pipes.

Manganese was incorporated into the iron tubercles and thus not

readily dislodged from the pipes by water flow. The PVC pipes

contained a thin surface scale consisting of white and brown layers of

different chemical composition. The brown layer was in contact with

water and contained 6% manganese by weight. Mn composed a

greater percentage by weight of the PVC scale than the iron pipe scale.

The PVC scale was easily dislodged by flowing water.

The various water quality parameters such as pH, temperature,

total dissolved solids and time of water circulation were all having an

effect on the migration of lead, tin and other metal stabilizers from

unplasticized polyvinyl chloride pipes. Water temperature did not

affect the migration of vinyl chloride monomer, unless it was raised to

high values (i.e.,450C).

35

2.4 EFFECTS OF BIOFILM/SCALE FORMATION IN PIPES

The formation of biofilms in drinking water distribution networks is

a significant technical, aesthetic and hygienic problem. Biofilms on

pipe walls in water distribution systems are composed of bacteria in a

polymeric matrix which can lead to chlorine demand, coliform growth,

pipe corrosion and water taste and odour problems.

According to Karloren et al., (2007), the formation of biological

films on the inner surface of the pipe not only affects the

characteristics of the surface, such as the roughness coefficient, but

may also promote the accumulation of organic and inorganic particles

because of its adhesive properties. These particles, in turn, can

contribute to sediment buildup, thus increasing the roughness

coefficient of the pipe surface. For clean 150 to 200mm PVC pipes, the

average value of Manning’s roughness coefficient is around 0.01, and

it was found to be independent of water depth, slope and pipe

diameter. The development of a biofilm on the internal surface of a

sewer pipe significantly affects the roughness coefficient. For small

diameter PVC pipes, covered with biofilm the minimum velocity and

shear stress to carry 90% of sand is around 0.55m/s and 1.4 N/m2

respectively. For the range of slopes studied, between 0.1% and 0.5%,

and for pipe diameters between 150 and 200mm, the shear stress

required to move particles of a given size is independent of slope and

pipe diameter.

36

Maggy NB Momba and N Makala (2004) compared the effect of

various pipe materials on biofilm formation. This investigation

indicated the colonization of all test pipe materials (PVC, uPVC,

MDPE, Cement and Asbestos cement) by coliforms and heterotrophic

plate count bacteria within 20min under chlorination treatment.

Statistical evidence showed that the generic type of pipe materials

greatly influenced the density of bacteria in the water system.

Cement-based materials (cement and asbestos) support less fixed

bacteria than plastic-based materials (PVC, uPVC and MDPE). No

significant difference in attached bacterial counts was found between

the generic types of pipe materials. The time factor also cannot be

ignored in determining the effect of pipe materials on biofilm formation

in potable water distribution systems. This study, therefore,

recommends the use of cement and asbestos cement pipe for the

distribution of chlorine-monochloramine treated water.

2.5 FRICTION FACTOR INVESTIGATIONS ON PIPE FLOW

The determination of frictional head losses is an important

engineering factor in the design of pipe networks that affects total cost

as well as the hydraulic balance of the network. Pipe sizing in a

network is dependent on the magnitude of allowable friction losses

calculated by the pipeline designer. Operating cost is inversely affected

by pipe diameter. As the pipe diameter increases for a given flow and

pressure head, frictional head losses per unit length decrease, thus

lowering the pump energy requirement. On the other hand, as the

37

diameter decreases the head losses per unit length increases and

hence the pump energy requirement is high.

2.5.1 Hydraulic Friction Factors for Pipe Flow

An over or under estimation of frictional head loss caused by water

flow in pipes will result in the selection of larger or smaller pipe and

pump sizes than actually needed, thus affecting total cost and

pressure distribution in the network. Different friction head-loss

equations used to estimate the energy loss in a certain pipe give

significantly different estimates depending on pipe size and water flow

rate. In this study friction factors were developed to be used with the

Hazen-Williams (H-W) and the Manning (Mn) equations for Cast- iron

and PVC pipes that give similar calculated head losses to that of

Darcy-Weisbach (D-W) equation. The H-W friction factor varied from

134 for 25 mm ID PVC pipes to 150 for 1050 mm ID PVC pipes and

from 111 for 25 mm ID Cast-iron pipes to 135 for 1050 mm ID cast-

iron pipes. Similarly, the Mn friction factor increased with diameter

and varied from 0.0083 for 25 mm ID PVC pipes to 0.0102 for 1050

mm ID PVC pipes and from 0.0099 for 25 mm ID Cast-iron pipes to

0.0113 for1050 mm ID cast-iron pipes. These friction coefficients

should provide satisfactory results (Kamand 1988).

2.5.2 Simple and Accurate Friction Loss Equation for Plastic Pipe

Engineers are continually searching for simpler calculation methods

that do not compromise accuracy. Some simple methods of calculating

pipe friction loss has been found, but the more complex Darcy-

38

Weisbach equation is the most universally accepted. The insertion of

the Blasius friction into the Darcy-Weisbach (D-W) equation results in

a combined equation with the following advantages.

1. It is theoretically sound and dimensionally homogeneous. Both

the Blasius and D-W equations have theoretical bases.

2. It is very accurate for plastic pipe when Reynolds numbers are

less than 1, 00,000. The Reynolds number limit is nonrestrictive

for irrigation-system design using pipes smaller than 80mm.

3. It is conveniently written in readily available terms: flow rate,

length and diameter.

4. It can be easily corrected for viscosity changes directly or by

referring to the included table of correction factors.

(Von Bernuth R.D.,1990)

2.5.3 Effect of Bed-Load Movement on Flow Friction Factor

The presence of bed-load movement increases the flow friction

factor. For pipe flows, the friction factor is larger for flows with bed-

load movement than those without bed-load movement at the same

Reynolds number. For clear-water flow in pipes without bed-load

movement, the measured relationship between the flow friction factor

and the Reynolds number fit the theoretical relationship well. All flows

studied were in the hydraulically smooth region. The increase in

friction factor in flows with bed-load movement is attributed to the

collision of sediment particles in motion, which consumes additional

39

energy in the flow. The increment in the flow friction factor can be

represented with the relative flow friction factor, f/fc. Its value depends

on the volumetric bed-load concentration and the size of the particle.

For a certain sediment size, the larger the concentration, the bigger

the increase. For a given concentration, the bigger the size of the

sediment, the bigger the increase. An empirical equation was fitted for

calculating the friction factor for flows with bed-load movement. If the

bed-load concentration, C, the diameter of the bed-load size, d, and

the flow friction factor of the equivalent flow without bed-load

movement is known , the flow friction factor can be estimated using

the proposed equation.( T.Song, Y.M. Chiew and C.O.Chin,1998)

2.6 FRICTION FACTOR STUDIES ON DOMESTICALLY USED PVC

PIPES

The friction factor in the case of PVC pipes was studied initially by

Von Bernuth and Tanya Wilson in 1989. The study of uniform pipe

flow condition needs two different investigations. (1) The flow velocity

and shear stress distribution in the cross section and (2) the flow-

resistance law i.e., the estimation of the f, friction factor of Darcy-

Weisbach uniform flow equation. In the past these two investigations

were separately developed although they are conceptually linked. If

the flow velocity distribution is known, the mean flow velocity can be

evaluated by integration and as a consequence the flow resistance law

can be deduced (Marchi 1961).

40

The flow-resistance law has been theoretically deduced for some

cross sectional shapes (circular and very wide rectangular) and under

specific boundary conditions because in these cases the velocity

profile is known. For example, the Prandtl-Von Karman theory of

turbulence can be applied to deduce the velocity profile, which has a

logarithmic shape; by integrating this logarithmic distribution for a

circular cross section of a smooth pipe, the Prandtl flow-resistance law

is obtained (Nikuradse 1933).

In the past the incomplete knowledge of the flow velocity profile

encouraged the empirical analysis of the friction factor measurements.

Empirical relationships like Blasius, Hazen-Williams and Manning’s

equations (Kamand 1988, Von Bernuth 1989) were developed. Some of

the relationships, characterized by a simple mathematical form,

neglect the variability of the friction factor with the flow conditions

and as a result, they only allow accurate head loss estimates for given

ranges of velocity and pipe diameter.

Blasius (1911) made a critical survey of the then existing

experimental results and arranged them in dimensionless form in

accordance with Reynolds law of similarity. The following empirical

equation called Blasius formula, was established f = c/Rem

where ‘c’ is the Blasius coefficient and ‘m’ is the exponent, which is

valid for the frictional resistance of smooth pipes of circular cross

section. According to his result, the dimensionless coefficient of

41

resistance in a pipe is a function of the Renolds number only. It is

found that the Blasius formula is valid in the range of Reynolds

number ≤ 105

Explicit relationships based on the Colebrook-White formulation

are available (Swamee and Jain 1976) and they are not more

complicated than the Blasius relationship (1913). However for

Reynolds numbers Re less than 105, the simple Blasius equation,

which allows accurate friction factor estimates for small diameter

plastic pipes (Watters and Keller 1978; Von Bernuth and Wilson 1989)

can be used (Schlichting 1979).

Von Bernuth and Wilson (1989), by using the experimental data of

Urbina (1976), Paraquiema (1977) and Norum (1984) assumed a

constant 'm' exponent of the Blasius (1913) equation equal to 0. 25;

the ‘c’ coefficient is variable and according to these experimental

results for PVC pipes, it is in the range of 0.281 to 0.345. The

curvature of the theoretical Prandtl equation (1935) on a graph log f

versus log Re can be filled by using an exponent 'm' of Blasius

equation variable with Re. The variability of 'm' can be explained by

the relationship between the velocity profile and the flow-resistance

law. Schlichting (1979), by using Nikuradse's (1933) data, showed that

by applying a power law velocity distribution with an exponent n=l/7,

and 'm' value equal to 0. 25 are obtained. According to Monin and

Yaglom (1971), because a simple relationship exists between 'm' and

'n', the Blasius equation (1913) has a theoretical basis.

42

A question to be resolved is whether the friction factor estimate for

irrigation design for PVC pipes should account for the deposits

accumulated in pipes which are used domestically for the flow of

ground water, since the variation of friction factor if any, can

significantly affect the head loss estimate. In particular, an

experimental study is needed to verify whether for a given pipe

diameter and discharge, the variation of friction factor with time of

usage can be explained by the formation of deposits and its

roughness.

In the case of domestically used PVC pipes of small diameters,

the Blasius equation is a good predictor for the estimation of

friction factor. In this particular study, the m exponent of Blasius

equation is taken as 0. 25 and the variation of the multiplicative

constant is noted.

Basically in the present study, the friction factor investigations as

well as head loss calculation equations were formulated. The main

aspect of deposition has not been taken into the preview of the study.

As mentioned earlier in this work, the deposition thickness, type of

slats and their characteristics, and rate of development of deposit

thickness mainly influenced by the water quality parameters, time of

running, pressure in the pipes, temperature etc. If a relationship is

established between the age and other parameters the equations

resulted above would be more useful. The same data may be collected

for artificially deposited pipes. Equivalent roughness coating materials

43

can be chosen and applied on the inner walls of the PVC pipes. The

friction data can be obtained for uniformly increasing deposit

thickness and at the same time higher Reynolds numbers can be

attained by using longer diameter pipes.( V.V. Nageswara Rao,2003)

2.6.1 Variation of Blasius Coefficient (c) in PVC pipes

According to V.V.Nageswara Rao (2003), In the case of

domestically used PVC pipes of small diameters, the Blasius

equation is a good predictor for the estimation of friction factor.

In this particular study, the ‘m’ exponent of Blasius equation is

taken as 0. 25 and the variation of the multiplicative constant is

noted.

(A) Variation of 'c' with the diameter

It can be clearly observed that when the area of cross section

of the flow reduces the head loss vis-a-vis the friction factor

increases. The variation of ‘c’ value which is used to calculate the

friction factor has been follows a 4th degree polynomial equation

given as follows:

c = 3. 4896D4 - 30.404D3 + 98.21 D2 - 139.31 D + 73.528

in which D is the diameter of the pipe in centimeters.

(B) Variation of 'c' with deposit thickness

It is very important to find the friction factor when the deposit

thickness is increased, because as discussed in the previous

section, the friction factor as well as the 'c' value increases with

44

the thickness of the deposit in the PVC pipe. The deposit thickness

has been observed to be increased to 1 mm in the case of 26 mm

pipe when they are used for 11 years. This deposition is gradual

and the relation has been given as follows:

c = 24.547 t2 - 2.031 t + 0.3195

in which’t’ is the thickness of the deposit formation in cm.

The deposit thickness is increased to 0. 49 mm after 3 years of

usage in the case of 20 mm pipes. 16mm pipe for a total period of

8 years was considered with two pipes in between this period with

3 years and 5 years also has been taken. In all these three cases

the thickness of the deposition has been measured. The total

accumulation is observed to be 0. 4 mm during this 8 year period

and followed a trend line relating ‘c’ and ‘t’ in the following form:

c =164.3 t2- 3.28 t+ 0.4

in which ’t’ is the thickness of the deposit formation in cm.

(C) Variation of 'c' with age of the pipe

When the pipe is domestically used depending on the quality

of ground water the deposits are formed inside the pipes. When

the head loss calculations are required it is necessary to have an

idea of the value of c for different conditions because this value is

varying with respect to diameter, thickness of the deposits etc.,

Instead, if a direct relation exist between the age of the pipe and 'c'

it can directly be calculated by knowing simply the age of the pipe.

45

Even the same thing can be substituted in the head loss equation

and the head loss can directly be calculated with the known age of

PVC pipe. The Blasius constant c is given by following equation:

c = 0. 0075 A + 0. 3067

in which A is the age of the pipe in years.

In an area where the similar conditions of pressure diameters

and the quality of water prevails this equation gives very accurate

results since the correlation coefficient is very high (0. 991). In the

case of 20 mm diameter PVC pipes the relationship between the

age(A) and coefficient 'c' is given by the equation:

c = 0. 0047 A +0. 326

In 16 mm diameter pipes the relationship between the age (A)

and the value of 'c' coefficient is obtained to be slightly complex

and follows a 3 degree polynomial equation which can be written

as:

c =0.0004 A3- 0.0026A2+0. 166 A +0.342

In all the cases, the friction factor calculated by the above Blasius

coefficients and the friction factor measured in the experiments are to

be nearly close to the line of agreement.

2.6.2 Variation of Friction Factor in PVC pipes

The data obtained for various diameter pipes and time of usages

has been analyzed for the calculation of friction factor by using

Darcy-Weisbach equation. These friction factor values are drawn to

the respective values of Reynolds numbers and relationship is

46

obtained in the form of a power law by taking the exponent 'm' as 0.

25 in the Blasius equation.

In the case of new pipes, it is clearly evident from the literature that

the data follows the Blasius equation in the calculation of friction factor.

By fixing the value of 'm' as 0. 25 the value of constant 'c' has been

analyzed for various diameter pipes in new condition. In the present

study it has been observed that the constant 'c' is slightly different from

that of the values suggested by Von Bernuth R.D, and Tanya

Wilson(1989) and going well with the value suggested by

Bagarellow et al.,(1995).

It has been observed from the study that the friction factor is

increasing with the time of usage for each diameter of the pipe. It is

obvious that when the deposits are formed the diameter decreases and

hence the head loss increases. More over the deposition roughness

also influences the friction factor. In the case of 26 mm pipes because

of the deposits the diameter of the pipe is gradually decreasing from

26mm to 24mm of internal diameter in a total period of usage of 11

years. The variation of' ‘c' for calculating the friction factor in

deposited pipes varying from 0. 304 for new pipe to 0. 39 for 11 years

used pipes with values in between.

For 20 mm pipes the value of 'c' varies from 0. 326 for new pipes to

0.345 for 3 years used pipe. Similarly the 'c' values in the case of 16

mm pipes changes from 0.342 to a value of 0. 524 for 8 years used

pipe. It is clearly evident that the friction factor increases steeply in

47

the case of smaller diameter pipes as the time of usage is increasing.

This is mainly because of the reduction in the diameter of the pipe.

Even there is a slight change in the reduction of diameter, the friction

factor changes rapidly (V.V. Nageswara Rao, 2003).

2.6.3 Hazen-Williams coefficient for PVC pipes

Trevor C. Hughes and Roland W. Jeppson (1978), reported the

field measured friction losses in three one-mile sections of small

diameter PVC pipe which had been in service for 10 years. Hazen—

Williams and Darcy-Weisbach equations were examined to

provide a framework for comparing Hazen-Williams Coefficients

recommended by pipe manufacturers to those obtained by the field

measurements. The conclusion is that the Hazen-Williams

Coefficient of 150 recommended by most PVC pipe manufactures is

too high for the diameter-velocity combinations encountered in

rural dead-end small diameter lines. The measured coefficients

averaged 133 which is close to that predicted by superimposing

H-W coefficients on the Moody Diagram from which the friction

factor for the Darcy-Weisbach equation is obtained. In this

study head loss measurements were taken along three

approximately one mile sections of pipe which consisted of 12m

(40 feet) lengths of PVC with glued joints. The three sections

included 1617m (5385 feet) of 60mm (2.5 inch) nominal diameter,

1333m (4440 feet) of 50mm (2 inch) and 1465m (4880 feet) of

40mm (1.5 inch) pipe all had been in service for 10 years. The

48

actual inside diameters were 67.4mm (2. 655 inches), 54. 6mm (2.

149 inches) and 43. 7mm (1.720 inches) respectively. The tests

were conducted in the ranges of Reynolds numbers between

14,350 to 41,100 and the estimated Darcy f values were between

0.0213 to 0.0280. Pipe sections approximately 0.6m (2 feet) long

were cut from the three PVC pipe diameters during installation of

master meters. The visual inspection of pipe sections cut from

these three lines appeared to be identical to new pipe in terms of

smoothness and total absence of deposition or corrosion. This had

been one of the very earlier studies on friction of PVC pipes after several

years of usage. But in this study it had been observed that the

deposition was absent inside the pipe.

2.7 MODELING STUDIES ON PIPE FLOW

There has been an extensive effort in the last decade to develop

theoretical models for fluid flow problems in pipes. In recent years

there has been success in the prediction of output results in the pipe

flow. In this section some modeling studies on flow through pipes have

been reviewed.

B.Bros (1999) presented a numerical model for the description of

fluid flow, and suspended and bed-load sediment transport. Density

effects are included in the momentum (Reynolds) equations and in the

turbulence (k and €) equations. Changes in bed levels are calculated

from sediment continuity, and the finite-element grid is adapted to the

geometry. The flow at a surface mounted cylinder in a steady flow is

49

predicted in good agreement with experiments. Scour calculations are

performed for a cylinder in a steady flow with its underside placed at

the level of the original flat bed. Predicted scour at a pipeline in steady

flow is in good agreement with laboratory measurements reported in

the literature. The routine use of numerical models in solving scour

problems for simple as well as complicated flow and bed conditions

will require physical insight and more modeling effort as well as

computer power.

M.Rashidul Islam and M.Hanif Chaudhry (1998), developed a

dynamic computer model to predict the constituent concentration at

various points and nodes in a pipe network under slowly varying flow

conditions. Unlike presently available steady-state or extended-period

water quality models, this model uses the slowly varying flow

conditions and is more appropriate for real-life applications to typical

distribution systems. In this model, a steady-state analysis of the pipe

network is performed first to determine the initial conditions. Then,

the dynamic governing equations, including the inertial effects, are

solved subject to the proper boundary conditions. The model is

applied to two typical pipe networks to simulate the transport and

decay of chlorine, and the results are compared with another model

which uses the standard extended-period simulation technique. The

results are found to be in good agreement at the beginning of the

simulation. The model may be used to analyze the propagation and

decay of any substance with a first-order reaction rate.

50

C.J.Kerr et al., (1999), predicted the relationship between the pipe

material and biofilm formation in a laboratory model system. The aim

of this study was to compare biofilm accumulation and heterotrophic

bacterial diversity on three pipe materials- cast iron, Medium Density

Poly Ethylene (MDPE) and unplasticised Poly Vinyl Chloride (uPVC) -

using a laboratory model system run over a short period (21days) and

a longer period (7months). Number of biofilm heterotrops accumulated

more slowly on uPVC and MDPE than on cast iron at the low flow rate

used in this study. The suggestion that the biofilm on uPVC is less

stable than on MDPE or cast iron requires further investigation.

R. Banki et al., (2007), presented a rigorous mathematical model

for the prediction of wax deposition in pipelines for laminar flow. In

the last 15 years, there have been a number of studies in modeling of

the deposition processes in flow lines. There was an extensive effort in

the last decade to develop theoretical models for wax deposition

calculations. There are two main processes that affect the deposition

of wax in flow lines: 1) heat transfer and 2) species flow. The transient

deposition of each component is calculated from the solution of the

coupled momentum, energy and species balance equations, and a

thermodynamic wax precipitation model at the local level. A detailed

numerical algorithm to solve the mathematical model is also provided.

51

2.8 APPLICATIONS OF ANN TECHNIQUE IN HYDRAULIC AND

HYDROLOGIC ENGINEERING MODELS

C.S.V.Subrahmanya Kumar (2009), applied an Artificial Neural

Networks technique for the performance evaluation and comparison of

different types of hydrological models. The study focuses on the

application of Artificial Neural Networks (ANNs) to formulate various

hydrologic models. The results of the study reveal that ANN technique

has the capability to capture the non-linear dynamics of a hydrologic

system. It can be used as a versatile tool to solve diverse problems.

The predictions of hydrological variables in the study are to the

reasonable accuracy. The study revealed that the error convergence

was quicker in the models.

Artificial Intelligence (AI) techniques are widely used in solving

various problems of Civil Engineering such as water management and

modeling. One of such AI techniques is an Artificial Neural Networks.

The Artificial Neural Network (ANN) is an information processing

system that roughly emulates the behavior of a human brain by

replicating the operations and connectivity of biological neurons.

ANNs have an ability to capture a relationship from given patterns and

hence this makes them suitable for employment in the solution of

large-scale complex problems such as pattern recognition, nonlinear

modeling, classification, association and control.

52

The ANN training is fundamentally a problem of nonlinear

optimization, which minimizes the error between the network output

and the target output by repeatedly changing the values of ANN’s

connection weights according to a predetermined algorithm. Error

back propagation is by far the most widely used algorithm for

optimizing feed forward ANNs.

For the application of ANN one may refer to Murry (1995). Most of

the ANNs applications related to the fields of water engineering were

presented in Negm et al. (2001). Therefore, only few related studies

were briefly reviewed here for the benefit of the reader.

The ASCE Task Committee (2000) investigated the role of artificial

neural networks (ANNs) in hydrology. According to the basic review

paper prepared by the committee, ANNs are gaining popularity, as is

evidenced by the increasing number of papers on this topic appearing

in hydrology journals, especially over the last decade. In terms of

hydrologic applications, this modeling tool is still in its nascent stages.

The practicing hydrologic community is just becoming aware of the

potential of ANNs as an alternative modeling tool. The paper is

intended to serve as an introduction to ANNs for hydrologists. Apart

from descriptions of various aspects of ANNs and some guidelines on

their usage, this paper offers a brief comparison of the nature of ANNs

and other modeling philosophies in hydrology. A discussion on the

strengths and limitations of ANNs brings out the similarities they have

with other modeling approaches, such as the physical model.

53

The role of ANNs in various branches of hydrology has been

examined here. It is found that ANNs are robust tools for modeling

many of the non-linear hydrologic processes such as rainfall-runoff,

stream flow, ground-water management, water quality simulation, and

precipitation. After appropriate training, they are able to generate

satisfactory results for many prediction problems in hydrology. A good

physical understanding of the hydrologic process being modeled can

help in selecting the input vector and designing a more efficient

network. However, artificial neural networks tend to be very data

intensive, and there appears to be no established methodology for

design and successful implementation. For this emerging technique to

find application in engineering practice, there are still some questions

about this technique that must be further studied, and important

aspects such as physical interpretation of ANN architecture, optimal

training data set, adaptive learning, and extrapolation must be

explored further. The merits and limitations of ANN applications have

been discussed, and potential research avenues have been explored

briefly.

H. M. Nagy et al., (2002), used an Artificial Neural Model to

estimate the natural sediment discharge in rivers in terms of sediment

concentration. This is achieved by training the network to extrapolate

several natural streams data collected from reliable sources. The

selection of water and sediment variables used in the model is based

on the prior knowledge of the conventional analyses, based on the

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dynamic laws of flow and sediment. Choosing an appropriate neural

network structure and providing field data to that network for training

purpose are addressed by using a constructive back-propagation

algorithm. The model parameters, as well as fluvial variables, are

extensively investigated in order to get the most accurate results. In

verification, the estimated sediment concentration values agree well

with the measured ones. The model is evaluated by applying it to

other groups of data from different rivers. In general, the new

approach gives better results compared to several commonly used

formulas of sediment discharge.

Murat Alp and H.Kerem Cigizoglu (2007), provided two simulations

for suspended sediment load by applying ANN methods using hydro

meteorological data. Estimates of sediment load are required in a wide

spectrum of water resources engineering problems. The nonlinear

nature of suspended sediment load series necessitates the utilization

of non-linear methods for simulating the suspended sediment load. In

this study ANNs are employed to estimate the daily total suspended

sediment load on rivers. Two different ANN algorithms, the Feed-

Forward Back-Propagation (FFBP) method and the Radial Basis

Functions (RBF), were used for this purpose. The neural networks are

trained using rainfall flow and suspended sediment load data from the

Juniata Catchment, USA. The simulations provided satisfactory

simulations in terms of the selected performance criteria comparing

well with conventional multi-linear regression. Similarly, the

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simulated sediment load hydrographs obtained by two ANN methods

are found closer to the observed ones again compared with multi-

linear regression.

Gokmen Tayfur and Vijay P. Singh (2006), developed an ANN and

Fuzzy Logic (FL) models for predicting event based rainfall runoff and

tested these models against the Kinematic Wave Approximation

(KWA). A three-layer feed-forward ANN was developed using the

sigmoid function and the back propagation algorithm. The FL model

was developed employing the triangular fuzzy membership functions

for the input and output variables. The fuzzy rules were inferred from

the measured data. The measured event based rainfall-runoff peak

discharge data from laboratory flume and experimental plots were

satisfactorily predicted by the ANN, FL, and KWA models. Similarly, all

the three models satisfactorily simulated event-based rainfall-runoff

hydrographs from experimental plots with comparable error

measures. ANN and FL models also satisfactorily simulated a

measured hydrograph from a small watershed 8.44 km2 in area. The

results provide insights into the adequacy of ANN and FL methods as

well as their competitiveness against the KWA for simulating event-

based rainfall-runoff processes.

Song Songbai and Cai Huanjie (2006), used an ANN model to

assess sustainable utilization of regional water resources. First,

stochastic method was used to form enough assessment indexes

sequence. Second, by using back-propagation networks, assessment

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indexes sequences and their assessment grade values were considered

as input layer units and output layer units to develop ANN

assessment model. Finally, the model was applied to assess

sustainable utilization of water resources in Hanzhong basin and

Huaihe watershed in China. The assessment results showed that the

model was practical and convenient to use. It is feasible to use the

model to assess the sustainable utilization of regional water resources.

M. Nasseri et al., (2008), developed neural networks to simulate

the rainfall field and Back Propagation (BP) algorithm coupled with

Genetic Algorithm (GA) used to train and optimize the networks. The

technique will be implemented to forecast rainfall for a number of

times using rainfall hyetograph of recording rain gauges in the Upper

Parramatta catchment in the western suburbs of Sydney, Australia.

Results of the study showed the structuring of ANN network with the

input parameter selection, when coupled with GA, performed better

compared to similar work of using ANN alone.

The friction factor of an open channel flow is generally affected by

the Reynolds number and the roughness conditions, and can be

decided by laboratory or field measurements. During practical

applications, researchers often find that a correct choice of the friction

factor can be crucial to make a sound prediction of hydraulic

problems. In this paper, a three-layer ANN was set up to predict the

friction factors of an open channel flow, with the Reynolds number

and the relative roughness as two input parameters.

57

The Levenberg–Marquardt (LM) learning algorithm was employed to

train the model by using laboratory experimental data, and the

trained network was tested by a single set separated from the rest of

the data and a good correlation between the experimental and

predicted results has been obtained. Finally, the ANN simulated

results were compared with the calculated results obtained by the

empirical formula and both comparisons showed that the ANN model

can be used to predict the non-linear relationship between the friction

factor and its influencing factors correctly, once enough samples are

provided. The successful application proved that ANN model can be

used in engineering practice as a convenient and effective method,

and those traditional hydraulic problems which are mostly based on

laboratory tests can be analyzed by ANN modelling.

According to A.H. Lobbrecht and D.P. Solomatine (1999), ANN and

Fuzzy Adaptive Systems (FAS) appeared to be efficient alternatives for

using optimal control algorithms in real-time tasks. The results

obtained for the control of water levels show that ANN and FAS are

able to replicate the behaviour of the Aquarius control component at

one-two time steps (1 hour) ahead with the accuracy in the range 90-

97%. This gives the possibility to replace the slow computational

components by the fast-running trained intelligent controllers and

thus to simplify the use of Aquarius in the real time control tasks.

Abdeen, M. A. M.(2001), introduced the use of ANN technique to

model and predict the hydraulic characteristics of the water surface

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profile in natural open channels. Synthetically generated data was

used in the study to show the applicability of using ANN technique for

modeling natural open channel behavior. The study implemented ANN

technique to predict flow depths and average flow velocities along the

channel reach when the geometrical properties of the channel cross

sections were measured or vice versa. The results of this study show

that ANN technique is capable, with small computational effort and

high accuracy, of predicting the different hydraulic characteristics of

irregular open channels.

Abdel-Azim M. Negm and Mohamed A. Shouman used ANNs to

model the characteristics of the submerged hydraulic jump formed

over roughened bed with regular staggered roughness elements.

Multilayer feed forward neural network with back propagation

learning algorithm is used to model the characteristics of such

submerged hydraulic jump. A network of size of 5-5-4 is found

suitable for this purpose with 3300 iterations and sigmoidal (tansh)

activation function. The results of the trained, verified and tested ANN

model are compared to the experimental measurements. Also, results

from previously developed models based on statistical methods are

compared to results of ANN model. The training data set is used to

develop multiple linear regression models for the jump characteristics

in terms of the input variable to the network. The MLR models are

tested using the validation and test data sets are then used to

compare the results of the ANN model. The results indicated that the

59

ANNs are powerful tools for modelling of submerged hydraulic jumps.

Through the sensitivity analysis that conducted using the ANN model,

the most contributing variables to characteristics of the submerged

jump over rough bed are specified.

The work reported by C.S.V.Subrahmanya Kumar and

G.K.Viswanadh (2008), is aimed to predict the reservoir stage using an

Artificial Neural Network Approach. The potential of ANN model which

belongs to the class of data-driven approaches for predicting daily

reservoir stage in Osmansagar reservoir, Hyderabad has been

presented in this study. An ANN using Levenberg-Marquardt

algorithm with back propagation is adopted for the study. The ANN

stage prediction model was developed using stage data for a period of

76 years. The model was trained using the data for a period of 50

years. The trained ANN was then tested for 26 years and the results

were compared with the observed values of the corresponding period.

The results suggests that a three layer feed forward ANN having single

hidden layer with two neurons can effectively be used to predict the

reservoir stage. The Nash coefficients of efficiency of the ANN model

were found to be 0.971 and 0.987 during training and testing. The

correlation coefficient between the observed and computed stage

series is 0.986 during training and 0.994 during validation. The study

revealed that the ANN model developed gives the best prediction of

reservoir stage.

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2.9 NEED FOR PRESENT STUDY

In the present scenario of water distribution systems, the PVC

pipes are in abundant usage for water supply, in wide range of variable

conditions. Because of the presence of salts in the water that they carry,

the inner walls of the PVC pipe is getting deposited with the salts that

are present in the water, in due course of time. The extent of deposition

has been observed to be high when the water is having more amount of

dissolved solids (salts). The thickness of the deposits inside the walls

of the pipe may vary depending on various factors such as water

quality, time of service of the pipes, flow pressure in the pipe,

temperature of water etc. Because of this deposition the friction inside

the pipe will also be changing since initially the PVC pipe is observed

to be smooth inside when compared to the surface of the salt coat

which is being formed with usage.

Though, considerable amount of research work is reported

regarding experimental studies on flow-resistance law and friction

factor studies in PVC pipes, the causes for the deposit formation and

the amount of thickness of deposits have not been reported in

literature. Also the reasons for deposit formation have not been

mentioned. If the probable reasons are investigated and modeled, the

criterion to minimize the formation of these deposits can be suggested.

In this context it becomes necessary to investigate the reasons for this

deposit formation.