CALIBRATION OF WATER DISTRIBUTION NETWORKS

Transkript

1 CALIBRATION OF WATER DISTRIBUTION NETWORKS A THESIS SUBMITTED TO THE GRADUATE SCHOOL OF NATURAL AND APPLIED SCIENCES OF MIDDLE EAST TECHNICAL UNIVERSITY BY KEREM AR IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN CIVIL ENGINEERING DECEMBER 2011

2 Approval of the thesis: CALIBRATION OF WATER DISTRIBUTION NETWORKS submitted by KEREM AR in partial fulfillment of the requirements for the degree of Master of Science in Civil Engineering Department, Middle East Technical University by, Prof. Dr. Canan Özgen Dean, Graduate School of Natural and Applied Sciences Prof. Dr. Güney Özcebe Head of Department, Civil Engineering Assoc. Prof. Dr. Nuri Merzi Supervisor, Civil Engineering Dept., METU Examining Committee Members: Assoc. Prof. Dr. S. Zuhal Akyürek Civil Engineering Dept., METU Assoc. Prof. Dr. Nuri Merzi Civil Engineering Dept., METU Assoc. Prof. Dr. A. Burcu Altan Sakarya Civil Engineering Dept., METU Assoc. Prof. Dr. Elçin Kentel Civil Engineering Dept., METU Gökhan Bağcı (Env.E., M.Sc) ASKİ Date:

3 I hereby declare that all information in this document has been obtained and presented in accordance with academic rules and ethical conduct. I also declare that, as required by these rules and conduct, I have fully cited and referenced all material and results that are not original to this work. Name, Last Name : KEREM AR Signature : iii

4 1. ABSTRACT CALIBRATION OF WATER DISTRIBUTION NETWORKS Ar, Kerem M.Sc., Department of Civil Engineering Supervisor: Assoc. Prof. Dr. Nuri Merzi December 2011, 116 pages Water distribution network models are used for different purposes. In this study, a model, used for daily operational issues is concerned. Models results should be consistent with actual conditions for sound decisions during operational studies. Adjusting model parameters according to site measurements in order to fit the model to obtain realistic results is known as calibration. Researchers have carried out numerous studies on calibration and developed various methods. In this study, an actual network (N8.3 Pressure Zone, Ankara) has been calibrated by two classical methods developed by Walski (1983) and Bhave (1988). The network parameter calibrated in this study is Hazen-Williams roughness coefficient, C-factor, and other parameters have been lumped in the C-factor. Results of the analysis showed that, C- factors have been found in a wide range. Keywords: Water Distribution, Hydraulic Network Model, Calibration, Water Distribution Network of City of Ankara, Case Study iv

5 ÖZ SU DAĞITIM ŞEBEKELERİNİN KALİBRASYONU Ar, Kerem Yüksek Lisans, İnşaat Mühendisliği Bölümü Tez Yöneticisi: Doç. Dr. Nuri Merzi Aralık 2011, 116 sayfa Su dağıtım şebekelerinin modelleri farklı amaçlar için kullanılır. Bu çalışmada günlük işletme amaçlı kullanılan bir model göz önünde bulundurulmuştur. İşletme sırasında doğru kararlar verebilmek için model sonuçlarının gerçek değerlerle tutarlı olması gerekmektedir. Model parametrelerinin, modelin gerçekçi sonuçlar vermesini sağlayacak şekilde ayarlanmasına kalibrasyon denilir. Araştırmacılar kalibrasyon hakkında çok sayıda çalışma yapmışlar ve çeşitli yöntemler geliştirmişlerdir. Bu çalışmada gerçek bir su dağıtım şebekesi (N8.3 Basınç Bölgesi, Ankara) Walski (1983) ve Bhave (1988) tarafından geliştirilen iki klasik yöntem kullanılarak kalibre edilmiştir. Çalışmada kalibre edilen model parametresi Hazen-Williams pürüzlülük katsayısı olan C katsayısıdır ve diğer parametreler de C katsayısının içinde değerlendirilmiştir. Analizler sonucunda C katsayıları geniş bir aralıkta bulunmuştur. Anahtar Kelimeler: Su Dağıtım Şebekesi, Şebeke Hidrolik Modeli, Kalibrasyon, Ankara Şehri Su Dağıtım Şebekesi, Durum Çalışması v

6 To My Family... vi

7 2. ACKNOWLEDGEMENTS I would like to state that I am grateful to my advisor Assoc. Prof. Dr. Nuri Merzi for his contributions, arrangements for the field works and his attention at every stage of the study. I would also express my kind gratitude to Halil Şendil and Onur Bektaş for their contributions and peerless assistance at field works. I am also thankful to my dear friends Onur Arı and Musa Yılmaz who provided great motivation and guidance during this study. vii

8 3. TABLE OF CONTENTS ABSTRACT... iv ÖZ... v ACKNOWLEDGEMENTS... vii TABLE OF CONTENTS... viii LIST OF FIGURES... xi LIST OF TABLES...xiii 1. INTRODUCTION General Scope of the Study LITERATURE REVIEW Definitions of Calibration in the Literature Standards for Calibration Calibration Applications for Real Networks Determination of Test Node Locations Sources of Error in Modeling Nominal vs. Actual Pipe Diameter Internal Pipe Roughness Values Distribution System Demands System Maps Temporal Boundary Conditions Pump Characteristic Curves Data Preparation Skeletonization of Network Head at Source Nodes viii

9 2.6.3 Supply at Source Nodes Consumption at Demand Nodes Pipe Head Loss Coefficients Proposed Methods for Calibration THEORETICAL CONSIDERATIONS General Walski s Method (1983) Development of the Method Calibration of an Example System Bhave s Method (1988) Development of the Method Calibration of the Example System Application of the Method in the Case Study FIELD WORKS Establishment of the Model Construction Works Determination of Valve Locations Installation of Fire Hydrants Construction of Measurement Chambers Isolation of Sub-Zones Utilization of GIS Site Tests Topographic Position Measurements Measurement of Coordinates of Measurement Chambers Measurement of Coordinates of Test Node Taps Pressure Measurements The Setup Measurements Discharge Measurements ix

10 5. CASE STUDY: MODEL CALIBRATION OF N8.3 PRESSURE ZONE, ANKARA General Information About N8.3 Pressure Zone Hydraulic Model Calibration of N Calibration of Yayla Network Model Walski s (1983) Method Bhave s (1988) Method Calibration of North Sancaktepe Network Model Walski s (1983) Method Bhave s (1988) Method Calibration of South Sancaktepe Network Model Walski s (1983) Method Bhave s (1988) Method Calibration of Şehit Kubilay Network Model Walski s (1983) Method Bhave s (1988) Method Calibration of Upper Çiğdemtepe Network Model Walski s (1983) Method Bhave s (1988) Method Calibration of Lower Çiğdemtepe Network Model Walski s (1983) Method Bhave s (1988) Method CONCLUSIONS AND RECOMMENDATIONS BIBLIOGRAPHY APPENDIX A APPENDIX B x

11 4. LIST OF FIGURES FIGURES Figure 2.1 Tuberculated Pipe Section ( 8 Figure 2.2 Distribution of Demands on Pipes... 9 Figure 2.3 Sample Flow Measurement Figure 3.1 Example System for Walski s (1983) Method Figure 3.2 An Illustrative Figure (Bhave, 1988) Figure 3.3 Example System Figure 3.4 Adjustment Zones and Path for Yayla Figure 4.1 Asphalt Covered Measurement Chamber Cover Figure 4.2 A Valve Cover Figure 4.3 Fire Hydrant Figure 4.4 A Measurement Chamber Figure 4.5 GPS Measurement Figure 4.6 Fire Hose Setup Figure 4.7 Strainer and Discharge Pipe Figure 4.8 Digital Manometer Figure 4.9 Ultrasonic Flowmeter Figure 5.1 Satellite View of N8.3 (Google) Figure 5.2 Sub-Zones of N Figure 5.4 Yayla Sub-Zone (2/2) Figure 5.5 North Sancaktepe Sub-Zone (1/2) Figure 5.6 North Sancaktepe Sub-Zone (2/2) Figure 5.7 South Sancaktepe Sub-Zone (1/2) Figure 5.8 South Sancaktepe Sub-Zone (2/2) xi

12 Figure 5.9 Şehit Kubilay Sub-Zone (1/3) Figure 5.10 Şehit Kubilay Sub-Zone (2/3) Figure 5.11 Şehit Kubilay Sub-Zone (3/3) Figure 5.12 Upper Çiğdemtepe Sub-Zone Figure 5.13 Lower Çiğdemtepe Sub-Zone (1/2) Figure 5.14 Lower Çiğdemtepe Sub-Zone (2/2) xii

13 5. LIST OF TABLES TABLES Table 3.1 Interpretation of a and b (Walski, 1983) Table 3.2 Pipe Properties of the Example System Table 3.3 Estimated Nodal Demands for Example System Table 3.4 Summary of Iterations of Walski s (1983) Method for the Example System Table 3.5 Pipe Properties of Example System Table 3.6 Estimated Nodal Demands for Example System Table 3.7 Observed Hydraulic Grades in the Field Tests Table 3.8 Summary of Results of Equations for Example System Table 4.1 Coordinates of Test Nodes and Measurement Chambers Table 4.2 Pressure Measurements Table 4.3 Discharge Measurements Table 4.4 Summary of the Measurements (Set-1) Table 4.5 Summary of the Measurements (Set-2) Table 5.1 Test Observations for Yayla Sub-Zone Table 5.2 Summary of Iterations of Walski s (1983) Method for Yayla Sub-Zone for Data Set Table 5.3 Summary of Iterations of Walski s (1983) Method for Yayla Sub-Zone for Data Set Table 5.4 Summary of Iterations of Bhave s (1988) Method for Yayla Sub-Zone Table 5.5 Test Observations for North Sancaktepe Sub-Zone xiii

14 Table 5.6 Summary of Iterations of Walski s (1983) Method for North Sancaktepe Sub-Zone for Data Set Table 5.7 Summary of Iterations of Walski s (1983) Method for North Sancaktepe Sub-Zone for Data Set Table 5.8 Summary of Iterations of Bhave s (1988) Method for North Sancaktepe Sub-Zone Table 5.9 Test Observations for South Sancaktepe Sub-Zone Table 5.10 Summary of Iterations of Walski s (1983) Method for South Sancaktepe Sub-Zone for Data Set Table 5.11 Summary of Iterations of Bhave s (1988) Method for South Sancaktepe Sub-Zone Table 5.12 Test Observations for Şehit Kubilay Sub-Zone Table 5.13 Summary of Iterations of Walski s (1983) Method for Şehit Kubilay Sub-Zone for Data Set Table 5.14 Summary of Iterations of Walski s (1983) Method for Şehit Kubilay Sub-Zone for Data Set Table 5.15 Summary of Iterations of Bhave s (1988) Method for Şehit Kubilay Sub-Zone Table 5.16 Test Observations for Upper Çiğdemtepe Sub-Zone Table 5.17 Summary of Iterations of Walski s (1983) Method for Upper Çiğdemtepe Sub-Zone for Data Set Table 5.18 Summary of Iterations of Walski s (1983) Method for Upper Çiğdemtepe Sub-Zone for Data Set Table 5.19 Summary of Iterations of Bhave s (1988) Method for Upper Çiğdemtepe Sub-Zone Table 5.20 Test Observations for Lower Çiğdemtepe Sub-Zone xiv

15 Table 5.21 Summary of Iterations of Walski s (1983) Method for Lower Çiğdemtepe Sub-Zone for Data Set Table 5.22 Summary of Iterations of Walski s (1983) Method for Lower Çiğdemtepe Sub-Zone for Data Set Table 5.23 Summary of Iterations of Bhave s (1988) Method for Lower Çiğdemtepe Sub-Zone Table A.1 Pipe Properties of Yayla Sub-Zone xv

16 CHAPTER 1 1. INTRODUCTION 1.1 General Water resources are becoming scarce nowadays as a result of increasing population, and contamination in potable water resources. So, effective usage and operation of water resources is gaining importance. In this sense, water distribution networks should be operated efficiently; an adequate operation should be based on a correct model. Different types of models are used according to the scopes of studies. These are long range master planning, rehabilitation, fire protection studies, water quality investigations, energy management, daily operations, and troubleshooting (Walski et al., 2003). Mathematical models are useful for simulation of distribution networks. These models are not only used in the design stage of a project, but also they play a great role in operation of the systems. Water distribution network (WDN) models consist of several elements, such as pipes, pumps, tanks, and valves. So, characteristics of each element have a considerable effect on the behavior of the system. Loading conditions, tank elevations, pump and valve status may change within the day. So that, the operators should forecast how those changes may affect the network. Network models are also studied for extended period simulations (EPS) of WDNs, which are beneficial for predicting the response of the system when unexpected cases occur. 1

17 Although new water distribution systems are designed by means of mathematical models nowadays, a great majority of existing water distribution networks have not been modeled. In order to simulate such networks in operational studies, their models should be built. Such models are established by using city maps and network plans, and sometimes by using verbal knowledge of administration staff. In some situations, actual case may be different from network plan drawings. From the construction of network to modeling stage, there may have been changes in networks. Such changes can occur in two ways; first changes came up with time, like aging of pipes; and second non-recorded (new furnished pipes or consumer connections) or abandoned (asphalt covered and forgotten closed valves) changes applied by water utilities. Thus, some adjustments in the model elements are necessary for accurate representation of actual conditions. The work of adjusting model parameters such as roughness coefficients and nodal demands for adequate representation of existing situation is called calibration. During calibration studies, several steps should be followed in order to obtain realistic results. The first step is determination of pipes and valve locations, and then making the valves available for operation. Second, establishment of isolated sub-zones is necessary in order to reduce the complexity of the system. During this step, necessary facilities like source and test nodes should also be determined. Then, field tests like pressure and discharge measurements should be carried out to obtain necessary information. Finally, a number of analyses are to be carried out with any selected method. In general, two major parameters of network models are calibrated which are roughness coefficients like Hazen-Williams C-factor, and water consumption at demand nodes. On the other hand, if total amount of flow entering the network can be measured accurately, importance of the adjustments in nodal 2

18 demands decreases. Thus, C-factor becomes a lumped parameter including all causes of head losses such as closed valves, minor losses like bends and partially open valves, or nodal demands in addition to pipe friction. 1.2 Scope of the Study The aim of this study is to calibrate an actual WDN and to indicate necessary works which should be carried out for calibration. A brief information about the study has been given in Chapter 1. The literature review relevant to water distribution network calibration has been discussed in Chapter 2. In Chapter 3, theoretical considerations of calibration methods used in this study have been explained with examples. Works done before and during field tests like constructions and measurements have been discussed in Chapter 4. In Chapter 5, a case study has been conducted by calibrating a real water distribution network; six sub-zones of N8.3 pressure zone of WDN of City of Ankara are considered. Finally in Chapter 6, conclusions drawn from this study and recommendations for future studies have been presented. 3

19 CHAPTER 2 2. LITERATURE REVIEW Researchers dealing with water supply and distribution have realized importance of calibrated hydraulic modeling studies considering WDNs since early 80s and numerous calibration techniques have been proposed up to now. Nowadays, methods for model calibration are still being developed. 2.1 Definitions of Calibration in the Literature In the studies, various definitions were discussed about calibration based on similar principles. Shamir and Howard (1977) defined calibration as determining physical and operational characteristics of an existing system and input data which would lead realistic results when put into hydraulic model. Walski (1983) defined calibration as a two step process. The first step is the comparison of predicted pressures and flows with observed pressures and flows for known operating conditions i.e., pump operation tank levels, pressure reducing valve settings. The second step is adjustment of the input data to improve agreement between observed and predicted values. Cesario and Davis (1984) stated that calibration is fine tuning a model until it simulates field conditions accurately. Similarly, Speight and Khanal (2009) stated that model calibration is adjusting parameters accurately in order to represent actual conditions with model simulations. 4

20 Although various definitions have been made by many authors for calibration of water distribution systems, there was an agreement on a common point. That point is the consistency of model results with actual observations and well representing of actual conditions. 2.2 Standards for Calibration Since models are complicated and used for many purposes, accuracy and scope of calibration depends on what extent the studies are carried on (Walski, 1995). So, applying strict rules while calibrating network models may not always be reasonable. Instead of this, degree and scope of calibration should be determined, and following tasks should be carried out with this aspect. The important issue is having a model that decision makers can confidently base their decisions on the results of the model (Walski, 1995). Different scopes of model calibration was described by Walski (1995) such as pipe sizing for master planning, extended period simulations for planning studies, subdivision layout, rehabilitation and energy usage studies, water quality models and flushing problems. For these studies, it was recommended that difference between observed and predicted hydraulic grade level (HGL) should be less than a few feet (under fire flow conditions), except master planning studies in which an error up to 5 ft would be acceptable (Walski, 1995). Walski (1995) also explained the role of head loss on accuracy of calibration and had given the following numerical example. Considering the flow in a 20,000 ft long pipe with 12 in diameter is measured to be 200 gpm and measured head loss is 5 ft with ± 3 ft accuracy, the C-factor can be anywhere between 70 and 148. If the flow is increased to 1,000 gpm, then the head loss becomes 99 ft ± 3 ft. In this case, range for C-factor narrows down to 89 and 92. 5

21 2.3 Calibration Applications for Real Networks Speight and Khanal (2009) have researched the current usage of modeling and calibration in the United States. In this study, current modeling applications, model input data, calibration data and model calibration have been considered. The study was based on ten water utilities with serving a population varying between 70,000 to more than one million. All of the utilities had used network models at least once a month for various applications. Moreover, five of the utilities had used models daily, and this was accepted as a high degree of model utilization. Two thirds of the participants had used Geographic Information Systems (GIS) as data source and had been working to convert old skeletonized models to more detailed models with EPS (Speight and Khanal, 2009). Nearly all of the participants had used manufacturers pump curves which were verified by recent pump tests. Nodal demands had been distributed to nodes by using electronic customer billing data obtained from GIS demand allocation. Although many of the participants in the study of Speight and Khanal (2009) have had varying degrees of SCADA (Supervisory Control and Data Acquisition) system, utilities had mostly used traditional hydrant tests as calibration data. In the research, it was reported that most of the participants had pursued macro level (transmission line) calibration focusing on pumps and tanks by using SCADA measurement rather than field tests. Also, the participants declared that macro level calibration was satisfactory for planning and operational type of applications. On the other hand, detailed micro level calibration had been applied by utilities with more experience on modeling rather than the participants building their first models. Speight and Khanal (2009) finally recommended for academic researchers to develop ways for data collection, model development, and calibration besides advanced techniques for water 6

22 utilities without capabilities to invest in expensive software and skilled modeling expertise. 2.4 Determination of Test Node Locations Determination of optimal number and locations of test nodes for calibration studies has been conducted in some studies in the literature. Meier and Barkdoll (2000) developed a method for optimizing the locations of test hydrants in which genetic algorithms (GA) had been applied. Aim of this method is to maximize the number of pipes carrying flow over a specified velocity. Since velocity of flow is the major factor of head loss, the method helps the user maximize head loss from the source to the test node as discussed before. Thus, the results obtained from calibration studies are expected to be found in a narrow range. de Schaetzen, et al. (2000) applied three methods for sampling design. The first and the second methods depended on shortest path algorithms, whereas third method used genetic algorithms for solving optimization problem based on the Shannon s entropy function. Kapelan et al. (2003) used multi objective genetic algorithms for determining optimal locations of test nodes. Objective functions of the method are minimizing uncertainty of calibrated model and minimizing the cost of sampling design. Such approaches have not been applied in this study, because there is only one hydrant for each sub-zone. 2.5 Sources of Error in Modeling For water distribution networks, making an error-free study is impossible because there are many uncertainties. Walski et al. (2003) pointed out 7

23 possible sources of error in network modeling. Such errors are possible reasons of anomalies such as too high or low C-factors in calibration results Nominal vs. Actual Pipe Diameter Nominal pipe diameters are equal to the actual diameters in the early ages of WDNs. Excessive tuberculation may lead to reduction in diameter which may result in increase of head loss values, so it is not reasonable to express such cases with low C-factors as A highly tuberculated pipe section can be seen in Figure 2.1 (Walski et al., 2003). Figure 2.1 Tuberculated Pipe Section ( However, actual diameter differences can be lumped in C-factor, because it is an indicator of anomalies in a network Internal Pipe Roughness Values A great deal of researches have been conducted in order to estimate pipe roughness coefficient values. Lamont (1981) developed a detailed table for 8

24 estimation of pipe roughness for a variety of pipe materials, sizes and ages. Also, Walski et al. (1989) notified the effect of water quality on aging of pipes. Walski et al. (2003) stated that there are infinite combinations of C-factors in a network resulting the same head loss from source to test node. In addition, it was advised to take head measurements under a range of demand conditions in order to reduce compensating errors in pipe roughness values Distribution System Demands Estimation of nodal demands is a difficult stage of modeling which involves many uncertainties. For the sake of simplicity, nodal demands in a network model are summed up and extracted from junctions of the model. Actually, demands are extracted uniformly by the consumers on the distribution line as shown in Figure 2.2. Generally speaking, combining nodal demands into a junction does not change simulation results significantly. However, very long pipes and facilities consuming large amount of water such as factories, swimming pools should be taken into account. For such facilities, a separate junction can be introduced in the model (Walski et al., 2003). Figure 2.2 Distribution of Demands on Pipes 9

25 2.5.4 System Maps A system map is one of the basic sources of information of a distribution network. Configuration of the network, lengths and diameters of pipes, topographical elevations of junctions, locations of valves can be observed on these maps. However, system maps may contain errors and mismatches even if they are built in digital media. Junction details on the maps are very significant, thus a wrong pipe connection in a model may yield to a completely different result. So, modelers should be aware about possible system map errors. Here, as a remedy, mapping technicians and field staff of the water utilities should work in coordination and any change in the network should be reflected to model (Walski et al., 2003) Temporal Boundary Conditions In water distribution networks, flow and pressure values are variable with time. Thus, synchronization during measurements is important. That means, pressure measurements at the test nodes should be consistent with the corresponding flow and HGL measurements at the source nodes. Although SCADA has been employed, results obtained should be evaluated extensively for excessive oscillations. A sample flow measurement chart have been presented in Figure 2.3 (Walski et al., 2003). 10

26 11 Flow (m 3 /h) Time (h) Figure 2.3 Sample Flow Measurement 11

27 2.5.6 Pump Characteristic Curves Pumps are usually introduced to models with characteristic curves provided by the manufacturer. However, aging of pumps is a situation that may occur in every system. If a 20 year old pump is modeled with head discharge relationship of the manufactured condition, it would have a higher capacity than the actual case. To compensate this, pump capacity tests should be carried out and actual curves should be obtained; in other words pumps should also be calibrated (Walski et al., 2003). 2.6 Data Preparation Bhave (1988) pointed out data preparation in five items: skeletonization of network, determination of head at source nodes and supply at source nodes, estimation of consumption at demand nodes and pipe head loss coefficients Skeletonization of Network Bhave (1988) defined skeletonization as ignoring small diameter pipes or grouping and replacing by equivalent pipes in order to make the model manageable. If excessive number of pipes are removed in skeletonization, the model may poorly represent the network (Bhave, 1988). Eggener and Polkowski (1976) stated that skeletonized networks would be more accurate if all the small diameter pipes near source and large concentrated demands are included in the model. Jeppson (1982) proposed formulas for equivalent pipes for replacing a set of pipes and recommended lumping length, diameter and head loss coefficients into a single resistance factor. However, network solver software require length, diameter and head loss coefficients of pipes as input 12

28 variables. So, it would be beneficial to use length and diameter of largest pipe as it is and adjusting its head loss coefficient according to removed pipes (Bhave, 1988). In this study, micro level skeletonization has been applied and only consumer connections have been ignored Head at Source Nodes Determination of head at source nodes is relatively easy. HGL elevation in tanks can be determined by staff gauges installed in tanks, and head in the pumps can be measured by pressure gauges (Bhave, 1988) Supply at Source Nodes Average discharge at a tank can be determined by dividing volume of water leaving the tank to time passed. Rate of flow passing through pumps can be determined by using head discharge curves of pumps, if the curves are assumed to be accurate (Bhave, 1988). Nowadays, electromagnetic and ultrasonic flowmeters measure rate of flow in pipes accurately Consumption at Demand Nodes According to Bhave (1988), estimating consumption at demand nodes is rather difficult. Water consumption at a node depends on the area served by that node, type of consumption such as residential, public, or industrial and population density of the area. Since estimated nodal demands may not be accurate, they may need to be adjusted during calibration (Bhave, 1988). 13

29 2.6.5 Pipe Head Loss Coefficients According to Bhave (1988), the most difficult issue in calibration of networks is the selection of head loss coefficients (in this study, C-factor) for pipes. Pipe head loss coefficients can be estimated by using pipe age and material versus head loss coefficient charts. However, it should be noted that these coefficients are lumped parameters including other sources of head loss such as valves, fittings, service connections and pipe alignment deviations (Eggener and Polkowski, 1976). 2.7 Proposed Methods for Calibration There have been various techniques developed in order to calibrate network models in the literature. Walski et al. (2003) prepared a table summarizing calibration methods developed till the publish date of their book. A portion of the methods and more recent studies, their theoretical basis, and scopes have been presented below. Walski (1983) developed an iterative method to adjust both nodal demands and C coefficients. The method is based on HGL differences between source node and test node under low and high flow conditions. It was assumed that adjusted C value is representative for entire network pipes. This method may also change the rate of flow entering the network, even it is measured accurately (Chapter 3.2). Ormsbee and Wood (1986) approached calibration from a different point of view, by solving the problem explicitly. Apart from Walski (1983), in this 14

30 method hydraulic network equations had been solved in order to meet field measurements (pressure and flow). The non-linear energy equations have been linearized by Newton s Method. The calibrated network parameters of Ormsbee and Wood s (1986) Method are pipe roughness coefficients and predefined minor loss coefficients. The method is based on the assumption that demand loading and operation conditions are fairly accurate. This assumption is the weak point of the method, due to the difficulty in determining nodal demands accurately at every junction. Another method was developed by Bhave (1988) which depends on site measurements under different demand loadings, especially fire flow conditions. Similar to Walski s Mehtod (1983), Bhave s (1988) Method fits the model hydraulic grades according to source and test node HGLs obtained from site tests. The major difference of Bhave s (1988) Method from Walski s (1983) Method is the assumption that rate of the flow entering the network can be measured fairly accurate. Thus, nodal demands in one group of junctions are increased, while demands in the other junctions are decreased in the same amount. This method is also iterative like Walski s (1983) Merthod. However, when compared to automated methods, convergence rate is rather slow (Walski et al., 2003) (Chapter 3.3). After development of new optimization based computing techniques since early 90 s, new approaches to calibration have appeared in the literature. Genetic algorithm (GA) is one of these methods employed by researchers dealing with WDNs. Theory of genetic algorithm is based on the evolution theory of Darwin. This theory states that fittest individuals in a population have the highest probability of transferring their genes to next generations. 15

31 GA works in the same manner. A population is generated and members closest to the optimal solution are carried to next generations. There are crossover, mutation, cut and splice operators which constitute members with different codes from previous generation. Vitkovsky et al. (2000) employed GA for network calibration. Then, Wu et al. (2002) used the Darwin Calibrator module of WaterCAD software which bases GA for calibration of networks. Greco and Del Giudice (1999) proposed a new approach for calibration. The calibration problem have been tried to be solved by non-linear optimization with the objective to minimize the sum of the squares of the differences between predicted and calculated pipe roughness. Apart from other studies, Greco and Del Giudice (1999) have not minimized differences of pressure between observed and predicted measurements, but used pressure difference as a constraint. Calibration is also used for leak detection in networks. Borzi et al. (2005) had applied a GA based calibration in Parma, Italy for an extended period simulation with a duration of 24 hours. In this study, areas with large water loss had been detected. Wu and Sage (2006) employed calibration for detecting both location and magnitude of water loss based on GA optimization. After evaluating the available methods, Walski s (1983) and Bhave s (1988) Methods have been proposed to be applied in this study. The reason of selection of these methods is being relatively simple. Moreover, the network to be studied has been expected to have high level of uncertainty, and there 16

32 have been limited amount of data have been collected. Furthermore, size of the problem is relatively small, so that complex methods are not necessary. As stated by Speight and Khanal (2009), simpler methods are more common in recent calibration studies carried out by water utilities. 17

33 CHAPTER 3 3. THEORETICAL CONSIDERATIONS 3.1 General In this study, calibration methods proposed by Walski (1983) and Bhave (1988) have been employed. Both of the methods approach the problem on a deterministic way. In both methods, C-factors of pipes and nodal demands are adjusted until hydraulic grade levels in site observations and model match; both Walski s (1983) and Bhave s (1988) Methods are iterative. Several assumptions have been done for the sake of simplicity. Measurement chambers have been treated as fixed grade nodes (source node) and modelled as reservoirs. Elevations of pipe centre level in the chamber have been summed up with observed pressure in terms of meters. So, that hydraulic grade level at each chamber have been determined. During field tests, pressure readings fluctuated until system was stabilized. After the stabilization, magnitude of fluctuations reduced considerably. Average of readings of stabilized network were used during analysis. It was also assumed that pressure drop in points close to hydrants during fire flow would be the same with the pressure drop at the exact location of fire hydrant. By means of this assumption, pressure measurements could have 18

34 been done at the garden taps of the houses close to the test nodes. On the other hand, while measuring pressure on fire hydrants, minor losses increase significantly because of very high velocity of flow in the hydrant and auxiliary instruments. Fire hoses generally bend up and disturb the flow of water considerably. 3.2 Walski s Method (1983) Walski (1983) developed a method for calibrating network models which adjusts both pipe roughness coefficients and nodal demands of the network simultaneously in order to fit the model to site adjustments Development of the Method Hydraulic grade line levels at a test node (fire hydrant) are h 1 and h 2 corresponding to low flow condition Q and high flow condition Q + Q f, respectively. Q f, denotes the flow extracted from the hydrant during test. H is the hydraulic grade line level near a constant head node. Unknowns of the problem are total water consumption Q corresponding to h 1 and pipe roughness coefficient C-factor for all pipes. In order to develop a simple method, Walski (1983) assumed a single equivalent pipe from source node to test node. The head loss between source node and test node at low and high flow was expressed as: S H1 h1 K1 C (3.1a) 19

35 S Q f H2 h2 K2 C (3.1b) Here; Q f = difference between low flow and high flow (l/s), H 1 = hydraulic grade line level at the source node at low flow (m), H 2 = hydraulic grade line level at the source node at high flow (m), h 1 = hydraulic grade line level at the test node at low flow (m), h 2 = hydraulic grade line level at the test node at high flow (m), K 1 = K for equivalent pipe at low flow, K 2 = K for equivalent pipe at high flow, S = total water usage at the nodes significantly affecting hydrant test (l/s). Unknowns in the Eqs. 3.1a and 3.1b are K 1, K 2, S, and C. K 1 and K 2 depend on the lengths and diameters of the pipes. For a tank or a pressure reducing valve hydraulic grade levels for low and high flows may be equal as H 1 = H 2. Here, K 1 and K 2 can be estimated by initial estimations of S and C (noted as S e and C e ). After assigning initial values in the model, the modeler can predict the values of h 3 and h 4 for low and high flow, respectively. 20

36 K H h C e Se K H h 1.85 C e Se Q f...(3.2a) (3.2b) In which, h 3 = model prediction of h 1 for S e and C e (m), h 4 = model prediction of h 2 for S e and C e (m). Inserting the values of K 1 and K 2 into Eqs. 3.1 and solving for S and C, resultant equations becomes: S Qf ASe...(3.3a) b Qf 1 1 a Se QC f e C b( S Q ) as e f e BC...(3.3b). e Where, H a H h h (3.3c) and H b H h h (3.3d). 21

37 As a result, and Qf A b ( S Q ) S a B Qf b ( S Q ) as e f e e f e...(3.3e)...(3.3f). Eqs. 3.3 can be used to adjust C and S through calibration. Walski (1983) stated that a and b give information about the source and magnitude of error in initial estimations and also prepared the following table. Table 3.1 Interpretation of a and b (Walski, 1983) Value a b <1 Too much head loss predicted Too much head loss predicted at at low flow high flow =1 Head loss correct at low flow Head loss correct at high flow >1 Too little head loss predicted at low flow Too little head loss predicted at high flow Finally adjustments are done by using A and B such as: S i AS, i = 1,2,..,.m...(3.4) ei C j BC, j = 1,2,...,m...(3.5) ej Walski (1983) recommended a 9-step procedure for the technique: 1. Prepare the model as accurate as possible. 2. Assume initial values for C and S and run the model. 22

38 3. Obtain the values of H 1, H 2, h 1, h 2, and S f from site tests. 4. If the results obtained from the model with initial estimates are close enough to values taken from site observations the system can be said to be calibrated and no adjustment is necessary. 5. Calculate a and b. 6. Calculate C and S. 7. Run the model with corrected values. 8. If the results are close enough to observations calibration is complete, stop iterations. 9. Turn back to Step 5, do the same steps. If desired values are still not reached, turn back to Step 3 and do the site tests again Calibration of an Example System The example network has been shown in Figure 3.1. This network is an imaginary network. All properties are known including nodal demands and C- factors. HGL results obtained from the model with known parameters are treated as site measurements. So, it has been checked that whether the method gives accurate results close to actual values. Pipe properties and estimated C-factor values for the first iteration can be seen in the Table 3.2 and estimated nodal demands can be seen in Table 3.3. In the field tests during low flow conditions, hydraulic grade level during the test node (J-10) has been measured as m. In the same node, hydraulic grade level has been measured as 9.67 m with 20 l/s water extraction from 23

39 the fire hydrant. In both low and high flow conditions water level in the reservoir has been measured as 45 m. Figure 3.1 Example System for Walski s (1983) Method 24

40 Table 3.2 Pipe Properties of the Example System Pipe Label Length (m) Estimated Hazen- Williams C Diameter (mm) P P P P P P P P P P P P P P Table 3.3 Estimated Nodal Demands for Example System Junction Label Demand (l/s) Junction Label Demand (l/s) J J J J J J J J J Predicted hydraulic grade levels at the test node in the first iteration with C = 120 for all pipes are m and m at low and high flow conditions, respectively. According to this information, a and b have been computed as: a

41 b A and B have been found as: 20 A (56 20) B (56 20) At the end of the first iteration, adjusted values of C and S have been found as: C adj = B C est = = 94.7 and, S adj = A S est = = l/s. For the second iteration, estimated values for C and S are 94.7 and 55.81, respectively. Following iterations have been summarized in the Table 3.4, below. At the end of two trials, the values of C converged to 95. The meaning of this result is assumed value (120) for the first iteration was quite high. Value of S did not change significantly. That is, estimated water consumption for junctions were close enough to actual case. In fact, C-factors of all pipes were 95 and total water consumption was 56 l/s. So that the method yielded to accurate results. 26

42 Table 3.4 Summary of Iterations of Walski s (1983) Method for the Example System ITERATION FLOW SOURCE HGL (m) OBSERVED TEST NODE HGL (m) PREDICTED C PREDICTED TEST NODE HGL (m) LOW HIGH LOW HIGH ITERATION S (l/s) F (l/s) a b A B ADJUSTED C Bhave s Method (1988) The method adjusts both C-factors and nodal demands. Major difference between this method and Walski s (1983) Method is the assumption that total water consumption can be measured accurately and it remains constant during adjustments. In addition, the method allows users to divide the network into zones in terms of roughness coefficients and nodal demands. So that the system may not be forced to have a single roughness coefficient. 27

43 3.3.1 Development of the Method The basic equations of flow are nodal flow continuity equations and loop head loss equations. Node flow continuity equation states that the algebraic sum of flows at nodes must be zero. Qi qj 0 for all j...(3.6) i incident on j Q i = flow in pipe incident on node j; q j = external flow as supply or demand at node j. According to head loss equation, algebraic sums of head losses in pipes in a loop must be zero. Li h 0 for all c...(3.7) i c In which h L = head loss in pipe i contained on loop c. Equation 3.7 can include minor head losses and the heads supplied by pumps in the loops (Ormsbee and Wood 1986). In practice, for head loss determination Hazen Williams (H W) formula is used: KLQ hl C D...(3.8). 28

44 K = constant depending on the units of the other terms in the H-W formula (10.67 for metric unit); L = length of pipe; Q = flow in pipe; C = Hazen-Williams roughness coefficient; D = diameter of pipe. The general form of head loss equation is known as: h L = RQ n...(3.9) R = overall resistant constant, n = exponent, (between 1.7 and 2.0). In the method proposed by Bhave (1988), it is assumed that inflow at the source node can be measured or estimated fairly accurately and therefore it is known and remains fixed during calibration. Since both nodal demands and head loss coefficients are adjusted, at least two additional conditions must be available. Necessary data can be provided by measuring heads at two demand nodes under a particular loading condition, or at a demand node under normal and fire flow conditions, or at a demand node under two different loading conditions (Bhave, 1988). Bhave (1988) illustrated an example model in Figure 3.2. In this figure, s is a source node and t 1 and t 2 are test nodes. The network has been divided into two zones, one for each test node, as shown in the Figure 3.2. ΔQ 1 and ΔQ 2 are demand adjustments for Zones 1 and 2, respectively. ΔQ 1 = -ΔQ 2 so that demand adjustment for the entire network is zero (Bhave, 1988). 29

45 Figure 3.2 An Illustrative Figure (Bhave, 1988) Considering a path from s to t 1 as shown by thick lines in Figure 3.2, and labeling the pipes on this path as 1,3,...,i,..., the following equation can be written: R 1 (Q 1p ) n + R 3 (Q 3p ) n R i (Q ip ) n +... = H s H t1p...(3.10). R 1, R 3,..., R i = overall resistance coefficients of pipes 1,3,...,i,...; Q 1p, Q 3p,..., Q ip,... = discharge in pipes 1,3,...,i,... for the estimated nodal demands and 30

46 pipe resistant constants; H s = head at source s; H t1p = predicted head at node t 1. Assuming the entire correction factor for Zone-1 as ΔQ 1, which is applied to pipe 1 on path s-t 1 in proportion to the discharge carried by a pipe; the adjustment for pipe i is ΔQ i = Q ip /ΔQ 1p. In addition, B is the global adjustment factor for pipe resistance constants for path s-t 1. n n n Q3 p Qip 1 1p p 1... i ip 1... s t1 1p 1p BR Q Q BR Q Q BR Q Q H H Q Q...(3.11) Expanding Eq. 3.11, neglecting higher order terms of ΔQ 1 and combining with Eq and simplifying the following equation yields to: nb Hs Ht1 p B Hs Ht1 p Q1 Hs Ht1...(3.12). Q1 p Since B 1, Bhave (1988) took B = 1 in Eq for simplicity for the second term. n Hs Ht1 p B Hs Ht1 p Q1 Hs Ht1...(3.13) Q1 p 31

47 Similarly, considering path s-t 2, and taking ΔQ 2 = - ΔQ 1 n Hs Ht 2 p B Hs Ht 2 p Q1 Hs Ht 2...(3.14) Q2 p in which Q 2 is the flow in the first pipe on path s-t 2. When Eqs and 3.14 are solved simultaneously, values of B and ΔQ 1 can be calculated. Then, adjusted C-factor is: C ia 1 Cip...(3.15) B Correction factor for each nodal demand can be computed according to the following equation: q ja q jp 1 Q z q...(3.16) jp j Nz in which q jp = predicted demand at node j; ΔQ z = total nodal flow adjustment for zone z; and N z = set of demand nodes in zone z. 32

48 Bhave (1988) proposed the following ten-step calibration procedure: 1. Preparation of the model data as accurate as possible, making the total outflow equal to the total inflow for the entire network. 2. Measuring the heads at the source node and at the test nodes for one or more loading conditions. Also measuring the fire flow if fire flow test are to be carried out. 3. Making the runs of the model for the predicted values for the selected conditions. 4. Obtaining the heads at the test nodes, determining the head losses from the source to the test nodes, and calculating the ratios of the head losses of the predicted to observed conditions. 5. If the ratios of observed and predicted HGL levels are acceptable, the calibration is complete. Otherwise, continue. 6. Determination of the number of additional equations available for calibration. It should be noted that, in a multisource network, as the heads and supplies at all source nodes are treated as known, the head at only one source node serves as a reference node and the head at each of the remaining source nodes provides additional equations. 7. Divide the network into zones of both C-factor and demand adjustments, and decide the number of B s and ΔQ s so that their sums exceeds the number of equations by one. This is because the one equation comes from the sum of ΔQ s are zero. Also following three items should be taken into account that all the paths: (1) together cover all the zones; (2) lie in the general direction of flow; and (3) contain pipes that carry majority of flow 33

49 towards the test nodes as seen from the runs of the model for predicted condition (step 3). The same principle can be used for selection of paths for multisource networks. This step is done only for the first iteration. 8. Framing appropriate adjustment equations for the selected paths solving and determination of the values of B s and ΔQ s. 9. Adjusting the pipe resistance coefficients and nodal demands. 10. Treating the adjusted values as predicted values and return to step Calibration of the Example System A calibration problem has been created with a similar approach in the example network in the Section Actually, all network parameters were known, and HGL values obtained from network analysis had been treated as site observations. Then it was tried to obtain the actual roughness coefficients by calibration. Forgetting the actual value of roughness coefficients, a C-factor had been assigned to entire network and Bhave s (1988) method was applied. The network, which can be seen in the Figure 3.3, has been divided into two sub networks starting from the source node (east and west parts). Thus, one test node has been specified for each part which are, J-4 and J-13. Fire flow extraction from each test node were 5 m 3 /min (83.33 l/s) during site tests. Pipe properties of the network and estimated nodal demands are shown in Tables 3.5 and 3.6, respectively. 34

50 Figure 3.3 Example System 35

51 Table 3.5 Pipe Properties of Example System Pipe Length Diameter Estimated Hazen- Pipe Length Diameter Estimated Hazen- (m) (mm) Williams C (m) (mm) Williams C Table 3.6 Estimated Nodal Demands for Example System Junction Demand (m 3 /min) Junction Demand (m 3 /min) J J J J J J J J J J J J J J Flow paths related to each test node have been selected as: J-4: Pipes J-13: Pipes

52 Hydraulic grade values observed in field observations are given in the Table 3.7, below. Water level in tank T-2 was measured as m during site tests. Table 3.7 Observed Hydraulic Grades in the Field Tests Observed HGL (m) Junction Low Flow High Flow J J Four equations are available from site observations. Thus, four adjustment coefficients have been proposed which are B 1, B 2, B 3, for roughness coefficient adjustments; and F for nodal demand corrections. Pipes included in each zone have been listed below: Equations according to test nodes, path selection and zoning have been written as: h h F B h h h for low flow, T 2 j 4, p 1 f 20,21,22 T 2 j 4, o Q h h F B h h h for high flow, T 2 j 4, p 1 f 20,21,22 T 2 j 4, o Q20 37

53 3, 4 12,19 T 2 j 13, p 2 f 3 f T 2 j 13, o Q h h F B h B h h h for low flow, 3, 4 12,19 T 2 j 13, p 2 f 3 f T 2 j 13, o Q h h F B h B h h h for high flow, Following equations have been generated after inserting the predicted values of head loss in pipes into the above equations for the first iteration: 4.31B 0.94 F 4.31, B F 49.90, B 6.03B 0.32 F 16.30, B 47.54B F Results of the abovementioned equations and following iterations have been listed in Table 3.8. Table 3.8 Summary of Results of Equations for Example System Iteration B 1 B 2 B 3 ΔF C 1,adj C 2,adj C 3,adj Since ΔF was very small, no corrections have been done in nodal demands. Final adjusted values of C 1, C 2 and C 3 have been found as 120, 140 and 109, 38

54 respectively. The method gave precise results. Actual values of C-factors were 120, 140, and 109 for Zones 1,2,and 3, respectively Application of the Method in the Case Study There is only one hydrant for each sub-zone. Thus, number of the equations reduces to 2 for each sub-zone. The method has been modified and simplified according to some assumptions. First of all, the nodal demands extracted from junctions are assumed to be constant during the site tests. This means that ΔQ becomes zero and regarding parts in the equations have been eliminated and reduced to the following form: B Hs Htip Hs Hti...(6.12) At the early stages of the study, two adjustment coefficients were proposed for C-factors because there were two measurements for two loading conditions. However, during the analysis it was observed that head losses during low flow and high flow conditions were not compatible with each other. This incompatibility was realized when a negative B obtained from head loss equations. An example of this situation has been given in below for Yayla the sub-zone. So, only the test results at high flow are used for the calibration of the sub-zones. Pipes belonging to each path and head losses have been shown in Table B7, in Appendix B. Adjustment zones for C-factor and the path for Yayla sub-zone have been shown in Figure

55 Figure 3.4 Adjustment Zones and Path for Yayla Calibration equations according to Data Set-1 have been shown below: 0.09B 0.02B B 4.42B B 1 and B 2 have been found as and -3.00, respectively. The same problem was faced while studying other zones. Thus, one loading condition and one C-factor adjustment coefficient have been used for entire sub-zone. 40

56 CHAPTER 4 4. FIELD WORKS For adequate calibation of the N8.3 Pressure Zone, mathematical model of the network has been established, necessary preparations for data collection have been conducted, and field tests were carried out. 4.1 Establishment of the Model Hydraulic model of N8.3 had already been built by ASKİ staff before calibration study. Lengths and diameters of pipes in the model have been assigned according to project drawings. Measured nodal demands have been distributed to junctions by using semi pipe length approach. In this approach, total demand of junctions are distributed according to ratio of total of half lengths of pipes connecting to a junction to total pipe length in the network. The mathematical model was provided by ASKİ. 4.2 Construction Works Since the network was in a poor situation before the calibration study, rehabilitation works were carried out in order to improve the reliability of the field tests. 41

57 4.2.1 Determination of Valve Locations While constructing networks, isolation valves are installed on pipes at the points deemed to be necessary. However, as years pass, valves are buried under asphalt cover because of careless works on the streets. Once a valve is covered, it is difficult to find it, because valve locations are not always clearly defined in drawings. Furthermore, staff of the water authorities can have no prediction about the status of lost valves. Such situations make that portion of the network unable to be managed. An example of covering network components with careless work is shown in Figure 4.1. Even the cover of a measurement chamber was covered by asphalt. Figure 4.1 Asphalt Covered Measurement Chamber Cover In N8.3 there were many lost valves. ASKİ found the places of valves by using special instruments scanning underground which are sensitive to metal. Then covers of those valves have been carried over ground. After this work the network became operable in terms of valves. In Figure 4.2 cover of a valve can be seen. 42

58 Figure 4.2 A Valve Cover Installation of Fire Hydrants Fire hydrants are elements that are used for fire extinguishing. Hydrants also have important role in network management like flushing and draining the networks. In this study hydrants are one of the most important instruments because large amount of flow could have only been extracted from hydrants. Generally, number of the hydrants on streets are less than the necessary number. N8.3 Pressure Zone was also in a similar situation. In addition, most of the hydrants were underground type which are not practical to use. For the experiments, at least one surface type hydrant have been installed to each sub-zone by ASKİ. A surface type fire hydrant is shown in Figure

59 Figure 4.3 Fire Hydrant Construction of Measurement Chambers In this study, measurement chambers are source nodes with known HGL. They are rooms constructed under roads where the main pipes entering sub-zones can be accessed. Entire flow entering the sub-zones passes through the pipe in measurement chamber. Measurements regarding source nodes are carried out in these chambers. Flowmeters are mounted on the pipe. For pressure measurements, there is a sleeve on pipe for mounting pressure gauges. Inside view of a measurement chamber is shown in Figure

60 Figure 4.4 A Measurement Chamber 4.3 Isolation of Sub-Zones At the early stages of the study, pressure measurements were done at all hydrants simultaneously. After the test it was observed that drop in pressure during the test was not sufficient for analysis. In addition, synchronization between experimenters measuring flow and pressure at hydrants became a problem. Another difficulty of dealing with the whole network is the number of necessary staff for the site test. At least two people are required per one hydrant. So, for N8.3, nearly 15 attendants would be required. Furthermore, the most significant advantage of isolation of sub-zones is the reduction in the 45

61 complexity of the problem. Each sub-zone is treated as a separate network. So, classical methods became applicable. By means of the works mentioned in Section 4.2, N8.3 have been prepared for isolation. The isolated sub-zones are Yayla, North Sancaktepe, South Sancaktepe, Şehit Kubilay, Upper Çiğdemtepe, Lower Çiğdemtepe. Apparent valves on the boundaries of the sub-zones have been closed, so no flow between sub-zones is permitted. However, it should be noted that there may also be missing valves that could not be found and brought out. 4.4 Utilization of GIS Geographic Information System (GIS) is a powerful tool for modeling distribution networks. All of the components of the system such as pipes, valves, reservoirs can be visualized in digital media. Another useful feature of GIS programs is their common databases with other software. By this feature, operators can transfer data to other programs. For example, elevations of junctions in the network can be transferred to WaterCAD as the elevation property of junctions. Network model of N8.3 had been built by means of GIS. Topographic map had been combined with cadastral information of the zone. After that, pipes and other elements were drawn according to existing drawings. Finally, digital drawing of the network was transferred to WaterCAD. 46

62 4.5 Site Tests For the study, three types of data were collected: (i) topographic position of source and test nodes, (ii)pressure at source and test nodes, and (iii) the flow entering the network Topographic Position Measurements Calibration methods have been utilized in this study use hydraulic grade level between source and test nodes for adjustments. In order to obtain hydraulic grade level of a node, both pressure and topographical elevation at that point should be known. Since head loss in the network during normal flow conditions is very low, accurate measurement of elevations of both source and test nodes is important. That means, Z-coordinates should have been measured from pipe center level at source nodes, and from tap level for test nodes as accurate as possible. Because of this fact, the most accurate way of measuring topographic data have been proposed as GPS (Global Positioning System). GPS device available at ASKİ could measure Z-coordinate with centimeter accuracy. In Figure 4.5, a photo captured while the technician was measuring coordinates of a garden tap can be seen. 47

63 Figure 4.5 GPS Measurement Measurement of Coordinates of Measurement Chambers As stated before, topographic elevations should have been measured from pipe center level. However, GPS device can only measure ground elevation since it cannot be inserted in the measurement chamber. So, for determining exact elevation of pipe center level, distance between ground and center level has been measured by a tape measure and subtracted from ground elevation Measurement of Coordinates of Test Node Taps Pressure heads have been measured at a close garden tap near the fire hydrants as discussed in the Chapter 3. X, Y, and Z coordinates obtained from measurements have been shown in Table

64 Table 4.1 Coordinates of Test Nodes and Measurement Chambers Node Coordinate X (m) Y (m) Z (m) Yayla Test Node 485, ,428, , Yayla Measurement Chamber 484, ,429, , N. Sancaktepe Test Node 483, ,429, , N. Sancaktepe Measurement 483, ,429, , Chamber S. Sancaktepe Test Node 484, ,428, , S. Sancaktepe Measurement Chamber 483, ,429, , Ş. Kubilay Test Node 483, ,429, , Ş. Kubilay Measurement Chamber 483, ,429, , U. Çiğdemtepe Test Node 482, ,429, , U.Çiğdemtepe Measurement Chamber 482, ,429, , L. Çiğdemtepe Test Node 482, ,429, , L.Çiğdemtepe Measurement Chamber 482, ,429, , Pressure Measurements The Setup The setup used in the experiments was prepared by ASKİ staff. Two hoses connected to hydrant are coincided with a pant. Then, a hose leaving the pant is connected to a strainer which provides weight to prevent warping of the setup under the force of flow. Finally, water is discharged to the nearest sewerage manhole by a steel pipe connected to strainer. Pictures of the setup can be seen in Figures 4.6 and

65 Figure 4.6 Fire Hose Setup Figure 4.7 Strainer and Discharge Pipe Measurements Two field tests have been carried out for pressure measurements. The first test was done by using analogue manometers. After the test, inaccuracies 50

66 have been observed in the test results. For example, HGL calculated in the measurement chamber of North Sancaktepe was lower than test node HGL. Such a case is an indicator of error in measurement. It was predicted that manometer inaccuracy is the reason of this error. So, digital manometers were provided by ASKİ and a second site test was carried out. A picture of digital manometer taken during test have been shown in Figure 4.8. Results obtained from the second test have been used for analysis. Figure 4.8 Digital Manometer Pressure values read on the screen of manometer are in terms of bars. In order to convert the unit to meters of water column, the measured value have been multiplied by a conversion factor which is An important issue during site tests was waiting after opening hydrants until the network had been balanced. Here, balanced network stands for no significant change 51

67 occurs at pressure and discharge values read during fire flow. In order to balance the network, pressure measurements lasted for 3 5 minutes; and the average value of observations was taken as final measurement for each test node. Pressure measurements obtained in the site tests are shown in Table 4.2. Table 4.2 Pressure Measurements Node High Flow Pressure Low Flow bar m water column bar m water column Yayla Test Node Yayla Measurement Chamber N. Sancaktepe Test Node N. Sancaktepe Measurement Chamber S. Sancaktepe Test Node S. Sancaktepe Measurement Chamber Ş. Kubilay Test Node Ş. Kubilay Measurement Chamber U. Çiğdemtepe Test Node U. Çiğdemtepe Measurement Chamber L. Çiğdemtepe Test Node L. Çiğdemtepe Measurement Chamber Discharge Measurements Discharges were measured by ultrasonic flowmeters mounted on inlet pipes passing through measurement chamber. In Figure 4.9, the part of device used for screening and adjusting settings is shown. Input parameters for 52

68 measurement are pipe material, diameter and wall thickness of the pipe. Fire flow extracted have been calculated by subtracting low flow discharge from high flow discharge. Unit of the measurements was m 3 /min and measurements have been converted to l/s. Discharge measurements can be seen in Table 4.3. Figure 4.9 Ultrasonic Flowmeter 53

69 Table 4.3 Discharge Measurements Node Low Flow High Flow Hydrant Flow m 3 /min l/s m 3 /min l/s l/s Yayla Measurement Chamber N. Sancaktepe Measurement Chamber S. Sancaktepe Measurement Chamber Ş. Kubilay Measurement Chamber U. Çiğdemtepe Measurement Chamber L. Çiğdemtepe Measurement Chamber The measurements have been summarized in Table 4.4. After the preliminary studies carried out by using Data Set-1, another field test has been carried out and results of this test have been shown in Table

70 55 Table 4.4 Summary of the Measurements (Set-1) Yayla Test Node Elevation (m) Test Node Pressure (m) Test Node Hydraulic Grade Line Level (m) Inlet Elevation (m) Inlet Pressure (m) Inlet Hydraulic Grade Line Level (m) Inlet Discharge (l/s) Hydrant Flow (l/s) Low Flow High Flow Test Node Elevation (m) Test Node Pressure (m) Test Node Hydraulic Grade Line Level (m) North Sancaktepe Inlet Elevation (m) Inlet Pressure (m) Inlet Hydraulic Grade Line Level (m) Inlet Discharge (l/s) Hydrant Flow (l/s) Low Flow High Flow Test Node Elevation (m) Test Node Pressure (m) Test Node Hydraulic Grade Line Level (m) South Sancaktepe Inlet Elevation (m) 55 Inlet Pressure (m) Inlet Hydraulic Grade Line Level (m) Inlet Discharge (l/s) Low Flow High Flow Hydrant Flow (l/s)

71 56 Table 4.4 Summary of the Measurements (Set-1) Continued Şehit Kubilay Test Node Elevation (m) Test Node Pressure (m) Test Node Hydraulic Grade Line Level (m) Inlet Elevation (m) Inlet Pressure (m) Inlet Hydraulic Grade Line Level (m) Inlet Discharge (l/s) Hydrant Flow (l/s) Low Flow High Flow Test Node Elevation (m) Test Node Pressure (m) Test Node Hydraulic Grade Line Level (m) Upper Çiğdemtepe Inlet Elevation (m) Inlet Pressure (m) Inlet Hydraulic Grade Line Level (m) Inlet Discharge (l/s) Hydrant Flow (l/s) Low Flow High Flow Test Node Elevation (m) Test Node Pressure (m) Test Node Hydraulic Grade Line Level (m) Lower Çiğdemtepe Inlet Elevation (m) 56 Inlet Pressure (m) Inlet Hydraulic Grade Line Level (m) Inlet Discharge (l/s) Low Flow High Flow Hydrant Flow (l/s)

72 57 Table 4.5 Summary of the Measurements (Set-2) Yayla Test Node Elevation (m) Test Node Pressure (m) Test Node Hydraulic Grade Line Level (m) Inlet Elevation (m) Inlet Pressure (m) Inlet Hydraulic Grade Line Level (m) Inlet Discharge (l/s) Hydrant Flow (l/s) High Flow Mod. Flow Test Node Elevation (m) Test Node Pressure (m) Test Node Hydraulic Grade Line Level (m) North Sancaktepe Inlet Elevation (m) Inlet Pressure (m) Inlet Hydraulic Grade Line Level (m) Inlet Discharge (l/s) Hydrant Flow (l/s) High Flow Mod. Flow Test Node Elevation (m) Test Node Pressure (m) Test Node Hydraulic Grade Line Level (m) South Sancaktepe Inlet Elevation (m) 57 Inlet Pressure (m) Inlet Hydraulic Grade Line Level (m) Inlet Discharge (l/s) High Flow Mod. Flow Hydrant Flow (l/s)

73 58 Table 4.5 Summary of the Measurements (Set-2) Continued Şehit Kubilay Test Node Elevation (m) Test Node Pressure (m) Test Node Hydraulic Grade Line Level (m) Inlet Elevation (m) Inlet Pressure (m) Inlet Hydraulic Grade Line Level (m) Inlet Discharge (l/s) Hydrant Flow (l/s) High Flow Mod. Flow Test Node Elevation (m) Test Node Pressure (m) Test Node Hydraulic Grade Line Level (m) Upper Çiğdemtepe Inlet Elevation (m) Inlet Pressure (m) Inlet Hydraulic Grade Line Level (m) Inlet Discharge (l/s) Hydrant Flow (l/s) High Flow Mod. Flow Test Node Elevation (m) Test Node Pressure (m) Test Node Hydraulic Grade Line Level (m) Lower Çiğdemtepe Inlet Elevation (m) 58 Inlet Pressure (m) Inlet Hydraulic Grade Line Level (m) Inlet Discharge (l/s) High Flow Mod. Flow Hydrant Flow (l/s)

74 CHAPTER 5 5. CASE STUDY: MODEL CALIBRATION OF N8.3 PRESSURE ZONE, ANKARA 5.1 General Information About N8.3 Pressure Zone N8.3 pressure zone is located in Keçiören and Yenimahalle districts, in the northern part of City of Ankara. A satellite view of the zone can be seen in Figure 5.1. Schematic illustration of the sub-zones developed in GIS media of N8.3 can be seen in Figure 5.2. Green lines in the drawing imply network pipes, blues lines represent main transmission lines, and black dots represent isolation valves in the network. Geographically, the zone can be defined as hilly. Minimum and maximum elevations of the zone are 1075 m and 1120 m above sea level, respectively. Water usage characteristic of the zone can generally be described as residential. There are no industrial facilities in the zone; furthermore, there exist schools and green areas. Population density of the zone differs from region to region. In early years, many of the houses were single floored. Till then, some of these houses have been reconstructed and multi-storey residential buildings have been built. Reconstructed regions can be distinguished from single floored houses in Figure 5.1. These reconstruction activities will cause an increase in the population of the zone, so in water consumption. Economic level of the people living in the zone can be defined as middle-low and low. Water consumption reaches peak value about p.m. There is no exact data available about population of the zone. 59

75 M.C.: Measurement Chamber T.N.: Test Node Figure 5.1 Satellite View of N8.3 (Google) 60

76 Distribution Pipe Transmission Line Valve LEGEND North Sancaktepe Lower Çiğdemtepe T-53 Reservoir P-23 Pumping Station Şehit Kubilay Yayla South Sancaktepe Upper Çiğdemtepe Figure 5.2 Sub-Zones of N8.3 61