cwmag best practice guide to hydraulic modelling … · 2020. 11. 17. · project title: cwmag best...

CLEAN WATER MODELLING ADVISORY GROUP CWMAG BEST PRACTICE GUIDE TO HYDRAULIC MODELLING

VERSION 4 NOVEMBER 2020

CWMAG BEST PRACTICE GUIDE TO HYDRAULIC MODELLING

NOVEMBER 2020 Version 4 (17/11/2020)



DOCUMENT MANAGEMENT

PROJECT TITLE: CWMAG BEST PRACTICE GUIDE TO HYDRAULIC MODELLING

VERSION DATE: 17 NOVEMBER 2020

DOCUMENT CONTROL

DOCUMENT FILE NAME: CWMAG BEST PRACTICE GUIDE TO HYDRAULIC MODELLING_Nov20_v4

CURRENT VERSION: 4

Section Lead Author(s) Reviewer(s)

INTRODUCTION Neil Croxton, Stantec Duncan Allen, Scottish Water

MODEL BUILD Fiona Page, Yorkshire Water Duncan Allen, Scottish Water James Burke, WSP Clare Keir, Atkins

DEMAND ANALYSIS Clare Keir, Atkins Duncan Allen, Scottish Water Fiona Page, Yorkshire Water

FIELD TESTING Hossein Rezaei, RPS

Duncan Allen, Scottish Water James Burke, WSP Clare Keir, Atkins Tom Nicholls, DCWW Fiona Page, Yorkshire Water

CALIBRATION Simon Croft, Anglian Water Duncan Allen, Scottish Water Tom Nicholls, DCWW Fiona Page, Yorkshire Water

VERSION HISTORY: 1 Draft - Introduction & Demand Analysis Chapter Published

(26/08/2020)

2 Draft – Model Build Chapter Published (22/09/2020)

3 Draft – Field Testing Chapter Published (13/10/2020)

4 Draft – Calibration Chapter Published & Revised Document Control (17/11/2020)

5

6

7

8



TABLE OF CONTENTS

1.0 INTRODUCTION .................................................................................................................... 1

2.0 MODEL BUILD ...................................................................................................................... 3

2.1 INTRODUCTION ..................................................................................................................... 3 2.2 METHODOLOGY .................................................................................................................... 3 2.3 EXTRACT & CONVERSION ..................................................................................................... 4

Nodes .......................................................................................................................... 4 Link/Facilities ............................................................................................................... 5 Network Structures ...................................................................................................... 6 Properties & Property Allocation ................................................................................. 7 Polygons ...................................................................................................................... 7 Data Flags & Labels .................................................................................................... 7

3.0 DEMAND ANALYSIS ............................................................................................................ 8

3.1 INTRODUCTION ..................................................................................................................... 8 3.2 METHODOLOGY .................................................................................................................... 8 3.3 DEMAND PROFILES ............................................................................................................. 10 3.4 STANDARD DEMAND PROFILES ........................................................................................... 11

Domestic Demand – Type 1 & Type 2 ...................................................................... 14 Unaccounted for Water – Type 3 .............................................................................. 14 Non-Domestic Customers – Type 4 to 9 ................................................................... 14 Large Metered Customers & Exports – Type 10 ....................................................... 14

4.0 FIELD TESTING .................................................................................................................. 16

4.1 INTRODUCTION ................................................................................................................... 16 4.2 FLOW RATE MEASUREMENT ............................................................................................... 16 4.3 PRESSURE & DEPTH MEASUREMENT .................................................................................. 17

Coverage & Logger Density ...................................................................................... 17 Operational Assets .................................................................................................... 17

4.4 FIELD TESTING DURATION & TIMING .................................................................................... 18 4.5 ACCURACY & LOGGER SETUP ............................................................................................. 19 4.6 QUALITY CHECKS ............................................................................................................... 19 4.7 PRACTICALITIES ................................................................................................................. 20 4.8 FAILURE RESOLUTIONS ...................................................................................................... 20 4.9 FUTURE DEVELOPMENTS .................................................................................................... 21

5.0 CALIBRATION .................................................................................................................... 22

5.1 INTRODUCTION ................................................................................................................... 22 5.2 FLOWS ............................................................................................................................... 22 5.3 DEPTHS ............................................................................................................................. 23 5.4 PRESSURES ....................................................................................................................... 24 5.5 SYSTEM ANOMALIES ........................................................................................................... 24 5.6 COMPARISON PLOTS .......................................................................................................... 25 5.7 MODEL PERFORMANCE ...................................................................................................... 25

APPENDIX A - DATA FLAG EXAMPLES (INFOWORKS ONLY) APPENDIX B - PIPE DIAMETERS APPENDIX C - FRICTION COEFFICIENTS



- 1 -

1.0 INTRODUCTION

The purpose of this best practice guide is to provide clean water network modelling practitioners with a useful reference document for undertaking and performing effective model build and calibration activities. This document is focussed upon the development of Extended Simulation Period (ESP) or Time Varying models and has been broken down into four main chapters, which provide an overview and guidance on the key steps required during the model build and calibration process. It attempts to capture existing best practice, whilst providing useful signposts for where existing processes can be enhanced. It highlights the pitfalls to avoid when attempting to meet the current need for accurate and calibrated hydraulic network models, as well as attempting to ensure that current model build, and calibration techniques are sustainable and are able to address the water industry’s future requirements for these digital tools. The document has attempted to capture best practice through the detailed survey completed by CwMAG members from various water companies last year and covers;

Model build Demand analysis Field testing Calibration

So what is a network model? A model as defined by the Oxford Dictionary is ‘a simplified mathematical description of a system or process, used to assist calculations and predictions’. The significant technological advancement made in Information Technology over the last 25 years has enabled hydraulic models to be created to replicate the complex interactions that occur across the physical water distribution system. As CPU speed has increased, the ability to model large and complex systems has become easier and much more widespread across the water industry. This increase in computer power, and the development of a number of software modelling packages means that water network models can be used to support a range of key water distribution design and operational activities. These include;

Strategic planning Contingency planning ‘What if’ scenarios Engineering design Pump scheduling Mains rehabilitation Water Quality analysis Pressure optimisation Growth assessment Drain down analysis

For the purposes of this guide, there are two types of clean water network models that are typically built within the UK. These models vary in their level of detail as well as the model’s temporal dimension. These two model types are Steady State and Extended Period Simulation models. Below is a brief overview of each model type.


VERSION 4 NOVEMBER 2020 - 2 -

STEADY STATE MODELS ESP MODELS

Steady state models are a snapshot of the hydraulic conditions at a point in time i.e. where all demand and operations are constant. Although conditions in the real water network change with time, these models are useful when particularly important instantaneous conditions within a network need investigating. An example could be assessing peak demand conditions when sizing pipelines, or minimum demand conditions when investigating the risk of discolouration or poor water age.

Extended Simulation Period (ESP) models are built to represent the change in demand and operational conditions, typically over a 24 hour period and at 15-minute timesteps, although increased computational speed means modellers aren’t limited to these temporal constraints. These models are useful for ‘what if’ scenario planning as the network’s response can be predicted considering the real networks true dynamic state i.e. varying demand, pressure, pump status and reservoir levels.

Given the widescale adoption of ESP model builds within the UK, the remaining chapters of this document will focus upon the development this model type. It is worth noting that for more specialised modelling activities, such as Water Quality Analysis, parameters such as time of travel and chlorine decay would need to be added to the data inputs required for the successful model build and calibration.



2.0 MODEL BUILD

2.1 Introduction

Model building is defined as the process of taking information from various sources and using it to create a hydraulic model which is ready for calibration. The data sources used can include:

GIS data – for the network layout, elevations, boundaries, asset locations etc. Topographical data such as Lidar/DTM if not included in GIS Address/Billing data if not included in GIS. Asset Information (company specific) – for how assets such as pumping stations and

service reservoirs operate including information such as reservoir volume per meter depth and pumping station rated speeds and curves. May also include control regime information. Many companies have PRV/Control Valve databases.

Telemetry and other field or survey data. Demand profiles – covered in the Demand Analysis chapter.

While there are a variety of model build ‘types’ such as all-mains, trunk main, strategic or a combination, these notes refer to the individual aspects of the model build. They are built up from a questionnaire that was completed by most water companies in the UK. Further work is needed to refine and develop them.

2.2 Methodology

The aim of the model build is to create a replica of the system in the software that mimics reality as practicably as possible and includes hydraulic devices and points where flow and pressure are altered. The standard model build process commonly includes the following: 1. Define area and extract from GIS. 2. Model data cleansing and adding of initial information where necessary (Service

Reservoir dimensions, DMA boundaries and meters etc…) 3. Demand allocation.

Each water company has their own specific methodology and/or software for all aspects of model building which will provide the finer details of how each aspect of the model should be set up. As part of the model build data cleanse exercise, errors in data and in connectivity inherent in the GIS system will be identified. It is best practice that these errors are fed back and corrected in the base GIS system for future improvement. The model start point should be specified (and agreed between client and consultant where a model is being built externally) and based on available data and ultimate use of the model. This should be a defined point where either flow or head (preferably both) is known (e.g. service reservoir or pumping station). The British National Grid is the standard coordinate system used for models in England/Scotland/Wales (Irish Grid for Northern Ireland). Usually the units used are l/s for flow, m3 or ML for volume and m head for pressure.



2.3 Extract & Conversion

Following model extents agreement, an extract of the modelled area is taken from the water company GIS. The main purpose is to extract the base asset information for the model build which allows it to be geospatially viewed in the modelling software. The extraction and conversion process varies between water companies due to differing modelling software and base data storage. In general, an amount of data processing is required prior to import into the modelling software. For example, most GIS have valves as ‘points’ on the GIS pipes, and these pipes need to be split and a short valve length created prior to (or post) import, depending on modelling software. SynerGI has the purchasable option of a model builder module which converts shapefiles or databases into a model via look up tables or data source definition (DSD) files. A more simplistic version exists which allows for the instant conversion of imported shapefiles into pipes without any attributes. Infoworks can import models from other modelling software models (e.g. Stoner, Wesnet, EPAnet files) or CSV files. Using the Open Data Import Centre, data can be bought in from MapInfo TAB files, csv files, access database files and ESRI shp files amongst others. InfoWorks contains tracing tools that can be used to check the model validity (Boundary Trace, Proximity Trace and Connectivity Trace). There are inbuilt tools for setting elevation of nodes and properties, diameters and roughnesses of pipes, allocating properties to nodes and defining the highest property at each node. There are tables of standard roughness coefficients. An InfoWorks user has the ability to use SQL and Ruby scripts to define/edit further model parameters if required. Infowater can import GIS data from ArcGIS and can import models from EPAnet. Models can be exported to InfoWorks via the InfoWater IWLiveManger (within Add On Extension Manager). The conversion process will create nodes, pipes, and assets in the model.

Nodes

Nodes act as the start and end points of a specific pipe of material/diameter, and also as locations for demand allocation where necessary. They may already exist in the GIS extract, but extra nodes are generally created to represent the following: Pipe ends and junctions. Changes in pipe attributes e.g. size, material, lining etc. Imports or Exports to a zone (e.g. Transfer Node in InfoWorks). At points of significant demand, e.g. a supply to a major consumer (Transfer Node or

Demand node), or where there is a significant cluster of customers. Water storage assets and other hydraulic devices in the network. At other points to correct errors in source data for accurate modelling such as elevation. Model elevation e.g. Air Valves may be identified on the main as a node, or a low/high

point may need to be identified. Hydrants should be included in the model as a hydrant type (InfoWorks only) or noted in

the description box. Within InfoWater, a selection set for hydrants can be created using Domain Manager.



To ensure that models can be merged and/or updated, a unique node numbering methodology is required and must be repeatable for future model maintenance. Usually this will be based on a Grid Reference format or taken from individual GIS node IDs. For node/property elevations a 10m or better DTM is used or LIDAR data where available. Each node should be assigned to a specific area code (usually DMA). Each node needs a Grid Reference (usually 12 point).

Link/Facilities

In InfoWorks/SynerGI/InfoWater linear objects are categorised into different types depending on function e.g. links/pipes, valves, meters, pumps etc.

2.3.2.1 Pipes

Pipes can be described in the model as ‘upstream/from node’ – ‘downstream/to node’ and contain the following information to accurately represent the flow of water moving through the model:

Diameter – internal diameters are specified through ‘look-up tables’ and are dependent on age, wall thickness, and condition. A GIS may contain some diameter information in inches as well as millimetres, and the conversion process will need to account for this to define the internal diameter for the modelling software.

Material. Length – calculated based on geographical length or can be manually overwritten. Roughness Coefficient – usually specified through look up tables dependent on the hydraulic

equation within the software. The equations to calculate friction factor commonly include: o Darcy Weisbach o Colebrook White o Hazen Williams.

Meters – these are represented as symbology in SynerGI and are a separate link type in InfoWorks. Headlosses across them are rarely specified or modelled.

2.3.2.2 Look-up Tables

Look-up Tables for internal diameters and roughness coefficients: These vary considerably across different water companies. While there are some similarities amongst common pipe materials, pressure rating, ages and sizes there is also a significant difference among others (see Appendix B). Coupled with the level of detail that may, or may not, be held within the GIS, it has not been possible to define standard Look-up Tables at this time. The water company will normally provide a set of standard figures to be used with their pipe asset base and models. For instances where unknown pipe information is present, these can be inferred from surrounding pipework, either automatically or manually, or from local knowledge. It is important that the direction of flow in meters and control valves etc is specified ‘from node’ to ‘to node’ so that the flow appears positive when graphed. The software cannot operate some control objects (e.g. PRVs, pumps etc.) if they are not aligned to flow direction.



2.3.2.3 Valves

All valves should be included in the model as a valve type. These often include (but are not limited to): Default sluice valve – acts as a headloss device and can be opened, closed, or throttled

dependent on headloss coefficients (k values). Check or Non-Return Valve – only allows for flow to travel through the valve in the

direction specified by the modeller. Tank Fill Valve – controls the flow into a reservoir based on various parameters (tank

level etc). Control valves such as PRVs/PMVs, FMVs (Flow Modulated Valves), PSVs (Pressure

Sustaining Valves) etc.

For the majority of these valves, a default valve curve is usually specified. Valve curves can be individually created for each valve where necessary based on telemetry or field data. Most companies include boundary valves to other areas in the model (whether that area is included in the model or not).

2.3.2.4 Control Valves

These should be modelled dynamically and frequently consist of the following: Pressure Reducing/Management Valves (PRVs/PMVs) – often modelled with a fixed

downstream pressure outlet. Flow Modulators – these adjust the downstream pressure based on flow travelling

through the valve via a curve. Often used to represent a decrease in pressure overnight to reduce leakage levels during periods of lower demand.

Time Modulated – operate according to time of day Flow Regulator – controls the flow through the valve based on a profile or set value. Loss Element (SynerGI) – controls downstream head based on flow travelling through

the valve.

These valves can be reflective of reality or be used as virtual assets for model calibration (see calibration chapter). It is prudent to note that headloss coefficients/tau values play a significant part in the performance of these valves.

Network Structures

These include Service Reservoirs, Water Towers, Break Pressure Tanks, and Pumping Stations. These should be included in the model as they can represent critical sources of changes in condition within the model. Service Reservoirs, Break Pressure Tanks and Water Towers should be modelled as variable head reservoirs with inlet and outlet flows measured and modelled to provide a dynamic simulation. Reservoirs that comprise connected compartments are often modelled as single cell. Pumps should be modelled individually (including all duty, standby, and assist pumps) with manufacturers’ pump curves used where possible. If these are not available, then it is possible to create a performance curve from existing flow and pressure telemetry data. However, it is prudent to remember that these curves will show current pump performance



including deterioration from its original state, which without the original manufacturer’s information, will not automatically be obvious. Pump tests to calculate pump curves are rarely undertaken but should be included if available.

Properties & Property Allocation

Properties are classified as either domestic or non-domestic and both types can be metered or unmetered (see Demand Analysis Chapter). These are usually present in GIS as ‘points’ and can be imported into the model as property points (InfoWorks). Allocation can be done within the model (InfoWorks), or within GIS, or as part of the model build process. Allocation is assigned to the closest relevant pipe and then to the nearest node along that pipe. Each company will have algorithms that specify/limit mains diameter to be allocated to, distance from the main, and maximum number of properties per node. For example: Pipes >8”/200mm are not allocated to. Distance to pipe can be limited to try and identify properties with private

supplies/erroneous grid references. Maximum properties per node is usually 200, and is a historical setting, as in more

modern model building the number rarely exceeds that.

Polygons

Polygons represent boundaries such as DMAs within the model and are often imported in directly from GIS.

Data Flags & Labels

Data flags act as confidence levels and highlight changes in the model (InfoWorks only). Labels within SynerGI provide a similar function and are used to annotate calibration changes or points of note. Selection sets in InfoWater are used to highlight calibration actions. Callout labels can be used for points of note. Where there is capability to include data flags (currently InfoWorks only) it is recommended that these are used. However, a clear guide for their use should be defined and adhered to. As can be seen from Appendix A, there are a number of flag descriptions common across the examples: Calibration Change. Model Maintenance. Levelled point. GIS import. Assumed data.

These could be collated into a standard list. It must be noted that too many flags can be counter-productive.



3.0 DEMAND ANALYSIS

3.1 Introduction

This chapter covers the Demand Analysis process where customer usage is determined through the creation of demand profiles, with the ultimate aim of having the best representation of both Domestic and Non-Domestic customer demand. For the purposes of this chapter, leakage will be referred to as Unaccounted for Water (UFW) and we recognise that variations in local terminology exist. There will be differences in technique between companies; this chapter is based upon the answers taken from the questionnaire sent out in Spring 2019. This process typically follows Demand Allocation where address points are assigned to the model. Demand Analysis involves the following:

Collection of data to typically include:

o Details of the area to be analysed. This typically constitutes a District Metered Area (DMA) with metered inlet/outlet flows.

o Flow data from relevant DMA inlet/outlet meters. o Details and number of properties/customers within the DMA.

Determining customer/demand type. I.e. Metered Domestic or Unmetered Domestic? Metered Non-Domestic or Unmetered Non-Domestic?

Assigning of known usage demands to standard normalised profiles which typically represent usage and patterns of consumption over 24 hours in 15 minute intervals.

Calculation of unknown volume demands, typically Unmetered Domestic use,

Unmetered Non-Domestic use, and UFW.

Derivation of normalised profiles for allocation of domestic demands.

Once this process is complete, a series of profiles are created which provide a best representation of customer usage for each DMA. These profiles are normalised and do not exactly represent the nuances of differing demand. These are then assigned to the analysis area DMA.

3.2 Methodology

Demand Analysis is carried out separately for each DMA or demand area. A demand area will be considered as a discrete area for which the total daily demand can be determined from field or telemetry measurements (metering). There may be exceptions to this such as, but not limited to:

A broken or faulty DMA inlet/outlet meter.

An open or cracked valve between DMAs for rezones or to improve turnover of mains, thus resulting in inaccurate inlet flow provided by DMA meters.

Where the above is present, then DMAs can be analysed together at a Super DMA level (multiple combined demand areas) where a flow meter(s) is present which records flows into the area. Other options may include using historic or derived data. This may result in a lower level of accuracy and confidence in the final model.



Following the above and the download of relevant data for the DMA, the next step is to determine a representative 24 hour period. This is completed by choosing 24 hours which provide the best representation of network operation availability of telemetry/field data, etc, ideally containing a good depiction of peaks, lows, and overall volume. Typically, data from mid-week is used (Tuesday, Wednesday, Thursday). Once the representative 24 hour period is chosen, demand profiles are derived through Flow Balancing for the 24-hour simulation period for the unmetered customers. Standard demand profiles are commonly used on network models to describe the variation in demand associated with Non-Domestic customers and UFW. To assist with determining daily consumption for each customer, billing data is collated alongside SIC codes (Standard Industrial Classification). The SIC code system classifies industries by a five-digit code. Established in the United States in 1937, it is used by government agencies and large companies to classify commercial types and industry. The SIC code is usually held by water companies on their customer billing system and is a convenient way to classify businesses into the different demand types. When demand is known (typically Non-Domestic customers) standard profiles are used to simulate the demand over a 24-hour period (see section 4.0 Standard Demand Profiles). For demands that are unknown (typically UFW and Domestic customers) the demand profiles are derived/calculated from actual recorded data. The four categories of demand generally include the following:

Metered Domestic – typically represents household usage with potentially slightly lower usage than unmetered Domestic, often based on daily volume taken from meter readings. This category is sometimes considered within the unmetered Domestic category as a Domestic standard profile.

Unmetered Domestic – represented as typical household usage.

Metered Non-Domestic – daily volume based on meter readings with an associated

profile based on SIC Codes. Any Large Metered Consumers (LMCs: typical usage more than 10,000m3/year) may be specifically meter read during field tests, or temporarily/permanently logged.

Unmetered Non-Domestic – typically small commercial (e.g. units) and usually combined within unmetered Domestic. Number of these is generally low.

The process of Demand Analysis or Flow Balancing is where the metered inlet flow is apportioned up into its constituent parts. Demands such as UFW, Non-Domestic, and any exports are typically subtracted from the inlet flow, resulting in total unmetered Domestic use being left over. The following is provided as an indication of how Demand Analysis can be calculated: UNMETERED DOMESTIC FLOW

= INLET FLOW – EXPORTS & LMCS (IF PRESENT) – UFW – NON DOMESTICS (IF PRESENT)

Individual property consumption can be calculated by dividing the remaining Total Unmetered Domestic Flow by the number of properties within the demand area being flow balanced.



Total Domestic flow (both metered and unmetered) can also be used to inform the Per Capita Consumption (typical range between 120 and 160L/Head/Day) of a DMA through the following steps: 1. Total Domestic Flow converted into a volume, then totalled. 2. Total Volume divided by average number of people per property (2.4 based on ONS

2019 data) and divided by number of Domestic properties within the DMA. The calculated property consumption is multiplied by the normalised profile at each timestep to calculate network flows. The calculation of demand for each time step may therefore be summarised as the calculation below and in Figure 3.21

Figure 3.21 – Modelled Demand

3.3 Demand Profiles

Demand profiles are used in network models to describe the variation in demand throughout a simulation period. Their purpose is to give a representation of different types of usage over a 24 hour period, with different usages categorised into normalised profiles. The derivation of these profiles for each demand area is typically carried out as follows: Known demand types are considered first and are typically not normalised profiles. A visual image of how demand is separated into its constituent parts is illustrated below in Figure 3.32. The minimum night line is also included. 1. The total average daily UFW level is typically determined by the minimum metered

night flow and legitimate night use allowances (illustrated by black vertical line in Figure 3.32).

2. Where UFW (red line in Figure 2) is not considered as a separate demand category

and is to be combined with the Domestic demand, stage 1 would not be required.



3. For metered customers (known demand and green line in Figure 2) the total average daily demand for each metered demand type (both Domestic and Non-Domestic) is determined or taken from billing data.

4. Large metered customers or exports are usually determined from field test results or

telemetry data and inserted directly into the model as a flow profile. 5. The variation in Domestic demand is determined by subtracting the demand

(unknown demand in below figure) associated with UFW (where considered as a separate category), metered customers, and LMCs from the total diurnal demand profile. This is normally calculated in 15 minute intervals for a 24 hour period.

Figure 3.32 – Demand Build up

3.4 Standard Demand Profiles

The Standard Demand Profiles are typically used for metered Non-Domestic customers. The profiles are normalised to give a good indication of usage and are used by the majority of Water Companies (usually detailed within Specifications) and are generally industry standard with some variation . The majority of models will contain standard demand profiles representing:

Metered Domestic.

Metered Non-Domestic

UFW.



For study areas with a significant and varied metered demand component, it may be necessary to sub divide the Non-Domestic categories. Oftentimes, billing data is used to derive usage and therefore it is prudent to use a representative sample (for example 3 years) to mitigate against anomalies. The commonly used Demand Categories and Type are defined below in Table 3.41, with a visualisation of the profiles illustrated in Figure 3.42: Table 3.41 – Demand Categories

DEMAND CATEGORY DEMAND TYPE

Unmetered Domestic 1

Metered Domestic 2

Unaccounted for Water (UFW) 3

Metered 10 Hour 4

Metered 16 Hour 5

Metered 24 Hour 6

Agricultural 7

Hotels 8

Hospitals 9

LMCs 10



- 13 -

Figure 3.42- Industry Standard Demand Profiles



The standard demand categories are defined below. It should be noted that not all water companies follow these profiles exactly, with some using fewer.

Domestic Demand – Type 1 & Type 2

Type 1 Unmetered Domestic Demand is determined by the calculation stated previously in this chapter and is generally, but not necessarily normalised. Some differences exist where individual water companies calculate the profiles based upon consumption within individual DMAs. Type 2 Metered Domestic Demand is typically assigned to Category 2 and is often a normalised diurnal profile. The metered Domestic demand is determined from field test or telemetry data and allocated to the model nodes based on the Demand Allocation process.

Unaccounted for Water – Type 3

Where UFW is included as a separate demand category, levels are generally determined from field test or telemetry data. UFW is often determined based on the Minimum Night Flow (MNF) deducted from the total flow for an area, but other company specific methods are often developed and followed. UFW can be allocated to model nodes based on a variety of methods e.g. location of customers or length of mains. Levels of UFW determined from the MNF will be based on the estimated Domestic and Non-Domestic legitimate night use. The Legitimate Non-Domestic night use will be based on the standard Non-Domestic profiles. For the Legitimate Domestic Night Use (LDNU) an allowance will be required to determine this value for each Domestic property that is connected to the network; most water companies will have figures available to apply this allowance.

Non-Domestic Customers – Type 4 to 9

Metered Non-Domestic customers can be sub divided into the most appropriate demand type by reference to the SIC codes. These customers can usually be subdivided into the following categories:

Type 4 – 10 Hour Usage. Typically, offices, schools, and smaller shops.

Type 5 – 16 Hour Usage. Can include leisure facilities such as sports centres, restaurants, and also some shops.

Type 6 – 24 Hour Usage. Large industry operating on shift patterns.

Type 7 – Agricultural Usage.

Type 8 – Hotels.

Type 9 – Hospitals.

Large Metered Customers & Exports – Type 10

Demands associated with large metered customers and exports are determined from field test results and the resulting information applied to the network model demands. LMCs are defined as those consumers whose usage of water has a significant effect on the pressures and flows within a distribution system. They are designated as LMCs due to either a high associated water use, or their location within the distribution system



(sometimes fed directly from trunk mains). The majority of these customers are typically logged. The flow consumption value above which the LMCs are logged varies by Water Company with each adopting a slightly different approach. However, the majority log LMCs with consumption over 10,000m3/year. The likelihood is that these customers fall under OfWAT’s Open Water agreement (in Wales and England) where certain businesses and retailers can choose their water supplier. Open Water eligible businesses need to use more than 50ML/year and as such, are likely to be individually logged by water companies. The treatment of LMCs as part of the model building process is usually undertaken as 1. The flows and daily demand from LMCs are recorded on telemetry and typically

used within the demand analysis process to determine any unmetered usage. 2. If an LMC is logged via telemetry or temporarily during a field test, this data will

be inserted directly into the model and not normalised against a standard profile.

3. The resulting individual profile is often included in the model as a direct demand

and located as close as geographically possible to the customer to replicate any notable instances in resulting local flows and pressures.



4.0 FIELD TESTING

4.1 Introduction

The purpose of field testing is to obtain required data and information used to assess the performance of the network hydraulic model predictions in comparison to the real system, and, if required, update the model (i.e. Model Calibration). As such, reliability and quality of data and information obtained from the field test are important to ensure a successful model calibration. Primarily, the main characteristics (data) of the systems captured in the field test include: Flow, Pressure and Depth. Additional information and data might also be gathered to help with understanding of the system’s hydraulic and operational setup and management, to feed into the model calibration (and anomaly investigation, if required). Various sources of information might be used to plan a field test, including GIS, system schematics, site schematic and operational documents, billing data, previous models, various corporate databases, etc. It is important to ensure timely access to these are provided and appropriate quality checks (e.g. using the latest version of information packs) are undertaken. When designing and delivering a field test, the following factors should be considered, as discussed in the subsequent sections:

Flow, pressure, and depth logging

Telemetry and existing data

Operational sites

Field test duration and timing

Coverage and logger density

Accuracy and logger setup

Practicalities and failures

4.2 Flow Rate Measurement

These days, most water utilities have a considerable number of permanently logged flows used for model calibration on their telemetry. It is recommended that, as a minimum, flows though the following locations should be available (through telemetry and if not, logged during the field test):

Flows in and out of the system under study (i.e. transfer nodes)

Flows in and out of reservoirs and towers

Flows delivered by all pumps

Flows through hydraulically-defined zones and DMA meters

Flows to large metered customers

Flow telemetry data will be used in priority to data gathered through field testing. It is advisable to review the telemetry in advance of field testing, to determine whether data of suitable quality is available. It is important to understand the accuracy of different



types of flow meter data, particularly for important locations across the network (above list).

4.3 Pressure & Depth Measurement

Pressure measurements are undertaken within DMAs, on trunk mains, and on the operational assets, including operational sites and regulating assets/valves. Depth measurements sometimes are done for tanks, reservoirs, water towers and other water storages, where telemetry data is not available. Similar to flow telemetry data, where available, pressure and depth telemetry data might be used in place of data gathered through field testing. This is project/case specific and should be agreed by all stakeholders. Appropriate data checks are required to confirm quality and reliability of such telemetry data.

Coverage & Logger Density

For pressure logging, a minimum logging density should be defined, depending on the area type and characteristics of the system under review. For DMAs, this will vary company by company, but typically, will be in the region of 1 logger per approximately 200 properties for urban areas, and 1 logger per approximately 5km length of pipe for rural areas, with a minimum of 3 loggers per DMA. For a single-feed DMA, it is recommended to log the DMA’s inlet, a point near the end of the DMA and/or any critical point(s). More populated (properties or mains length), hydraulically complex, multi-fed, or DMAs with special characteristics (e.g. topography, poor condition mains or a specific user), and/or local operational knowledge, may require (significantly) more logging points to guarantee a good calibration. Trunk main logging requirements will vary company by company but is often driven by availability of existing logging points, as enabling works required to setup new tappings are costly and often challenging to deliver. Where possible, it is recommended to have 1 logger for every 3-5km of trunk main length and/or where there is a significant change in trunk main characteristics (network or hydraulic). Often, (upstream of) regulating valves/points feeding DMAs off trunk mains can be logged and used for trunk main calibration.

Operational Assets

Gathering quality data from operational assets is critical for a good model calibration. Many of the operational assets (particularly larger pump stations and reservoirs) may already be on telemetry/SCADA systems. Depending on the quality and reliability of data from these systems, additional data collection may be required, specifically if network and operational arrangements are complex and/or there are unconfirmed anomalies to be investigated. Reservoirs It is recommended that all depths in tanks (reservoirs and water towers) should be logged or recorded, if not available on telemetry. Where the tank consists of more than one cell that may operate independently, each cell should be logged separately. Site specific checks should confirm operation and logging requirements.



Pump stations Depending on the size and setup of the pumps in each pumping station, it is advisable to log individual pumps, rather than the upstream and downstream of pump station. Enabling works might be required to be undertaken in advance of field testing, to ensure correct tappings are available and working, although this might not always be practical. In this case, it might be possible to consider alternative locations upstream and downstream of the pump station (e.g. nearby fire hydrants) to be logged. For accuracy of pump calibration, such alternative locations should not be a long distance from the pumps, nor should there be any assets or restrictions between these that could be altering the system’s hydraulics. Regulating valves It is advisable to log both upstream and downstream of each (significant e.g. based on size, number of properties supplied, operation, etc.) regulating valve, particularly the ones feeding a DMA or other hydraulic areas. If fed directly off a trunk main, logging upstream of these valves provide valuable data for calibrating the trunk main. As with the pump stations, alternative hydrants or other loggable locations can be used where these valves cannot be directly logged. Additional information It is advisable to utilise the logger deployment site visits to gather any additional information about operational and regulating assets, i.e. pumps, reservoirs, regulating valves, etc. These may include asset information, make and model, pump curve, operational regime, standby/duty arrangements, asset and network arrangements, connectivity and operations. This includes normal operation as well as operation (if any different) within the field testing period. Furthermore, photographic evidence (with permission), site/asset schematics, copies of local/site paperwork (which might not be available digitally), information on isolating valves (e.g. asset tag ID), etc. might provide useful information when undertaking calibration. If anomalies had been reported previously (e.g. previous model reports), gathering information about the anomaly and related assets can help with investigation.

4.4 Field Testing Duration & Timing

As detailed in the Calibration chapter, a ‘calibration day’ is selected following assessment of the field test data for model calibration. Availability of quality data is a key consideration in selecting the calibration day. It is advisable to consider a potential calibration day (and alternatives) when planning a field test. It is generally advised to avoid weekends (and where possible Mondays and Fridays) aiming to get a typical representation of the system’s hydraulics. A minimum duration of full coverage logging (when all locations are logged) is advised to be at least one full week to provide a choice of calibration day. It is advisable to communicate the planned dates of field test(s) with various stakeholders (and wider operation and management teams) to ensure that the field test period does not coincide with major planned activities that might alter the system’s hydraulic characteristics. In the case there has been a significant event occurring during field test period, it is advisable to review options to continue logging for an extended period. It is generally considered best practice to undertake a single field test for the whole area under review. In the event this is not practical, multiple field tests will be undertaken ideally using suitable hydraulic splits, e.g. reservoirs, to separate the field tests to more manageable size.



While there are various practicality concerns in undertaking the field testing, it is advisable to undertake field testing when there are higher demands and headlosses across the system.

4.5 Accuracy & Logger Setup

Resolution of data collected depends on specific requirements and types of modelling undertaken. For generic modelling work, this is typically 15-minute time interval data. If data is collected at higher resolution, and converted to 15-minute time interval data, conversion methodology should be reviewed to ensure suitability when compared/combined with data sets with other recording resolution/setups. A specific time stamp should be agreed upon and programmed in, typically GMT (used widely across the industry). Where required, and depending on the modelling software used, logger offsets might be applied within the model, (and not to the raw data). Logging device level of accuracy should be specified for all data logging equipment, typically set at 0.1%. All loggers used should be suitably calibrated with logger calibration certificates available. It is recommended that all pressure and depth locations are levelled. This will vary company by company, but typically will have a tolerance within ±25mm to ±50mm. Where GPS levelling is used, signal coverage and quality might not be suitable in all locations (e.g. under trees, built-up areas, inside buildings). In these instances, alternative methodologies may need to be considered.

4.6 Quality Checks

Various checks can be undertaken prior, during and post field test to help with quality and reliability of data collected. It is recommended that interim field test checks are undertaken on key logged locations, which may lead to a re-test in case of issues with data being recorded. Where possible, it is recommended that spot check/manual readings are taken when loggers are installed to ensure the logger is correctly installed and a spot reading of the asset data is available. It is recommended that logger data is checked prior and post deployment for any offsets. Post deployment offsets are generally deemed to be the representative offsets. Data should be checked for consistency and reliability, soon after collection. For instance, plotting all data from a DMA can highlight potential issues with data (e.g. where a logger shows distinctly different pressure profile, suggesting deployment on a hydraulically different asset). Early detection of issues may grant opportunities to find solutions to data issues/gaps. As an example, shown in figure 4.6, pressure data collected for a DMA is plotted together. In this graph, point A is from a trunk main passing through the DMA, points B and C are logging on upstream and downstream of the DMA’s inlet PRV respectively, point D is data from a hydraulically different asset (outside of a DMA boundary valve), point E represent a point inside DMA (different elevation), with point F and G showing no data (failed points).



Figure 4.6 - Example of plotting pressure data collected for a DMA, as a quick initial data reliability check

4.7 Practicalities

Various practical considerations should be taken into account to ensure the original points planned for the field test have a high chance of being logged. This may include ensuring intended assets are safely accessible (e.g. not in the middle of busy roads), they are in working condition, and where applicable, correct tappings are available. It is, however, likely that alternative locations will be required if the original point is not loggable. A close working relationship between the modeller (designing original field testing) and the field team is recommended to help with identifying the correct alternative logging locations, in a timely manner. Where practical, particularly for important assets, a site visit prior to the field test can help identify any potential issues; for instance, if any enabling works are required. Whether the field test is planned and undertaken using electronic forms and online GIS/network data or traditional paper-packs, checks should be in place to ensure correct points are logged and associated data/information is recorded, so the risk of erroneous data collection (e.g. coordinates of location logged) is mitigated. All field works must be carried out following industry and company health and safety regulations, such as water hygiene, calm network, and other requirements (Risk Assessment and Method Statements, Personal Protective Equipment, Safe Systems of Work, other good practices, etc.), to ensure a safe work environment for all concerned, and with minimum adverse impact on the network and personnel. It is recommended that an agreed (with all stakeholders) naming convention for logging points from the field test planning stage is in place to avoid confusion and the need for any consequential alterations. It is recommended that as a minimum the model name or reference and field test number or ID are included in naming each logged location.

4.8 Failure Resolutions

An acceptable rate of failure may be agreed between all stakeholders, potentially based on a system’s characteristics, equipment used, type of modelling, etc. Resolution for logger failures might be case specific, depending on the location of failed logger(s), overall coverage, significance of asset logged, etc. Where the adverse impact of failed logger(s) is deemed significant for a successful model calibration, a re-test might be required.



4.9 Future Developments

With advancement in technologies, data logging instrumentations and data analytics, it is acknowledged that this best practice may change to adopt new practices. With more high resolution pressure testing, increased density of permanent logging and enhanced pressure monitoring, advances in ‘Internet of Things’ technologies, near real-time modelling, smart metering, etc., practices may be adjusted to benefit from such progresses. For instance, with increased remote access to data being recorded, a set of alarms might need to be set up to undertake the data checks.



5.0 CALIBRATION

5.1 Introduction

The modeller should calibrate the model over the 24 hour simulation (as per field testing section) period by adjusting the model network data and settings until a good fit is obtained between the model predicted and the field test results from the chosen day. The model predictions should reflect the behaviour of the system over a range of flows between peak and minimum night flow conditions. It is recommended that the model run settings are specified by the model owner but these can be software specific, and an example of typical settings used is 15 minute time steps for 24 hours with a minimum computational accuracy of 0.1 litres per second. Before spending a significant amount of time calibrating a model the data, network and field test data should be validated and checked for any erroneous data.

5.2 Flows

At the start of the calibration process, the model predicted flows must be compared to those recorded on the field test. Where there is one input to a discrete area, the model predicted flows must match the field test results at all times over the 24 hour simulation. For demand areas with multiple inputs at each timestep the combined totals must match even if the balance between the multiple inputs is initially incorrect, see figure 5.1. While carrying out calibration all export flows should be fixed to provide a known before been made dynamic. Flow calibration tolerances should be specified by the model owner prior to calibration work beginning. It is recommended that 85% of all measured values (Including all sources) shall be within ±5%. 95% of all values shall be within ±10%.

Figure 5.1



5.3 Depths

Depth calibration of storage tanks should be specified by the model owner prior to calibration work beginning. The type and elevation of the inlet to the storage tank should be considered, see figure 5.3. Typical tolerances that are used in the water industry is between ± 0.05 and ± 0.1 for 85% to 95% of values. If depth data is taken from telemetry this should be validated and confirmed if on the system 100% full does actually equal top water level of the tank. The shape of the storage tank should be considered as this will affect the volume curve, see figure 5.2.

Figure 5.2



Figure 5.3

5.4 Pressures

The modeller is to consider the future use of the model and the impact of modelling suspected network anomalies such as throttled or closed valves. As higher demand scenarios will be significantly affected by throttle and closed valves which may not be representative of the actual network. Where pressures are known at pumps and PRV outlets these should be fixed during the calibration of the model and then made dynamic for the final version of the model. The modeller is to ensure that calibration is achieved with the minimum adjustment required to the model in order to achieve the desired tolerances. Pressure calibration tolerances should be specified by the model owner prior to calibration work beginning. Typical ranges of values that are used in the water industry are 85% to 100% of monitored data points shall be within ±1.0m to ±2.0m and or frictional headloss may be considered.

5.5 System Anomalies

The preference for which calibration actions to use should be specified by the owner of the model prior to calibration work starting. From a general point of view:

Pipe diameter changes are allowed but justification is required and further on-site investigations maybe required.



The re-allocation of demands is allowed but justification is required. The re-allocation of demand categories (e.g. 10 hour to 24 hour) are allowed but justification is required.

The re-allocation of leakage is allowed but justification is required. The addition of local losses of loss co-efficient on certain objects are allowed

but justification is required. It is recommended that the roughness co-efficient of plastic material pipes

should not be increased beyond their normal starting values, but it is a valid calibration action to increase the roughness coefficient of iron material pipes.

It is recommended that increasing the roughness co-efficient of Asbestos Cement material pipes should be limited as these normally have a very low level of tuberculation.

The pressure rating and any pipe linings should be considered for the impact on the pipe nominal bore roughness coefficient.

During calibration closed and throttle valves can be used but it is recommended that these should be investigated further with on-site checks.

When investigating model anomalies the correct setup of any PRVS in the model should be considered and guidance on how to do this can be found here link to PRV guidance.

Any anomalies found during the calibration stage of the study should be recorded and included in the model calibration report. It also recommended that any anomalies are investigated further with on-site checks.

5.6 Comparison Plots

On completion of the calibration, graphs should be reviewed to show the model predicted and field test results for all recorded flows, pressures and reservoir levels. Calibration plots associated with site facilities such as pumps, tanks, trunk main meters etc. should be reviewed from the final calibrated model. Calibration comparisons can also be provided in tabulated form.

5.7 Model Performance

The model should be calibrated for a period of 24 hours and the model run uninterrupted for an extended period of time e.g. 96 hours. This will produce a calibration model that should be recorded and issued separately. This model should form the basis of other model versions such as average day and peak day models, but how dynamic the model controls and demands are between the versions should be considered.



APPENDICES



Appendix A - Data Flag Examples (InfoWorks Only).

Example 1:

Example 2:

Data Flag

Description Comment

AO Asset data from Other source

Asset data from Other Source – e.g. PRV settings from Database, Details from SR cleaning reports, verbal from Operations etc. Details of information source should be provided in the Notes tab of the object.

AV Assumed Value Any assumed values such as a PRV control setting, if the PRV was not logged (e.g. on a SR bypass) or estimated pipe diameters.

CC Calibration Change All general changes made for calibration purposes e.g. alterations to the default k values, diameters, valve settings (including PRV. THV settings), local losses etc.

RC Recalibration As per CC flag but based on additional data since the original model calibration e.g. as part of model maintenance or anomaly resolution.

EC Enforced Change Any change known to be incorrect but necessary for ensuring the model run works properly such as reducing a PRV diameter for model stability or enforced changes to the pipe diameter. Details should be added to the notes tab of what change has been done and why.

GU GIS Data Unavailable Any data added which is unknown on GIS but populated in the model – e.g. unknown asset IDs because pipe is not on GIS etc.

IG Imported from GIS Any information imported from GIS – e.g. Asset IDs, diameters (prior to any changes made (e.g. PE internal)), pipe materials, ages, user texts etc.

LO Level from Other source (LiDAR, DTM etc.

All elevations which have been obtained from some other system rather than GPS and therefore have lower accuracy (e.g.DTM, LiDAR etc.).

LS Levelled Survey Point All elevations which have been obtained using GPS (or similar) and are therefore at an accuracy of +25mm.

NB Notes tab has info This should be used to flag the area code field if any information has been populated in the notes tab of the object.

MM Model Maintenance All model updates carried out as part of the model maintenance programme but not confirmed with a field test such as new PRV settings from e-mails, new mains, asset IDs, k values etc.



Data Flag

Description Comment

SC Scenario Changes Any changes specifically made to the model for a scenario assessment but are not in the ground – e.g. pipe upsizing, additional PRV or PS etc. Additional information should be populated on to the notes tab as to what change has been made and why.

SD Standard Default All fields where typical default values used such as PE internal diameters, initial default k values etc.

SR Spot Read Any setting input to the model from a single spot read (gauge reading or similar) e.g. a PRV outlet setting (typically only used for PRVs covering only a few properties etc.

VA Virtual Asset Any asset added as a calibration change not on GIS e.g. THV used immediately d/s of a PRV, a closed valve on a pump bypass that is not on GIS etc. The Asset ID should be “see notes” and flagged VA and the notes tab should be used to put brief description of what has been added and why.



Appendix B - Pipe Diameters

There are many types of pipe material in use within the UK water industry, and within those materials, a variety of lining types and methodologies. This leads to huge variation of potential pipe diameters that need to be modelled, and each company has developed tables of these based on their known asset data. Some have similarities and could easily lend themselves to a UK standard, others have such a wide variety that they do not. The following example overleaf shows Cast Iron Pipe material:

Unlined Cast Iron:

Data from 3 companies

Abreviation Class Nom D " Model D mm Variation 1 Class Model D mm Variation 1 Class Model D mm Variation 1 Class Model D mm Variation 1 Class Model D mm Variation 1

CI A/B/C/D/U 1.5 38

A/B/C/D/U 2 51

A/B/C/D/U 2.5 63

A/B/C/D/U 3 76

A 4 102 B 102 C 102 D 102 99 U 101 102

A 5 127 129 B 127 129 C 127 D 127 123 U 127

A 6 152 155 B 152 155 C 152 D 152 148 U 152

A 7 178 182 B 178 182 C 178 D 178 173 U 178

A 8 203 208 B 203 208 C 203 D 203 199 U 203

A 9 229 234 B 229 213 C 229 D 229 224 U 229

A 10 254 260 B 254 260 C 254 D 254 249 U 254

A 12 305 306 B 305 C 305 310 D 305 U 305

A 14 356 358 B 356 C 356 361 D 356 U 356

A 15 381 383 B 381 C 381 387 D 381 U 381

A 16 406 409 B 406 C 406 413 D 406 U 406

A 18 457 460 B 457 C 457 464 D 457 U 457

A 20 508 512 B 508 C 508 515 D 508 U 508

A 21 533 538 B 533 C 533 541 D 533 U 533

A 22 559 563 B 559 C 559 566 D 559

A 24 610 614 B 610 C 610 617 D 610 U 610

A 26 660 665 B 660 C 660 669 D 660

A 27 686 691 B 686 C 686 694 D 686 U 686

A 28 711 716 B 711 C 711 719 D 711

A 30 762 767 B 762 C 762 771 D 762 U 762

A 32 813 818 B 813 C 813 822 D 813

A 33 838 844 B 838 C 838 847 D 838 U 838

A 36 914 920 B 914 C 914 924 D 914 U 914

A 38 965 971 B 965 C 965 975 D 965

A 39 991 996 B 991 C 991 1000 D 991

A 40 1016 1022 B 1016 C 1016 1026 D 1016 U 1016

A 42 1067 1072 B 1067 C 1067 1077 D 1067 U 1067

A 44 1118 1123 B 1118 D 1118

A 45 1143 1148 B 1143 C 1143 1153 D 1143 U 1143

A 46 1168 1174 B 1168 C 1168 1179 D 1168

A/B/C/D/U 48 1219



Appendix C – Friction Coefficients

As can be seen from the tables below, a wide variety of values are used across different companies, making developing a standard look up table difficult.

Material Age

Company 1

Company 2

Company 3

Company 4

Company 5

Company 6

AC Asbestos Cement

0 0.03 0.03 0.03 0.05 20 0.06 0.06 0.03 0.3 0.05 30 0.08 0.08 0.03 0.3 0.05 40 0.1 0.1 0.03 0.3 0.05 50 0.2 0.2 0.03 0.3 0.05 60 0.3 0.3 0.03 0.3 0.05 70 0.4 0.4 0.03 0.3 0.05

CI Cast Iron

0 0.03 0.03 0.03 0.03 0.03 2 15 0.05 0.05 0.05 0.05 0.05 2 20 0.88 0.88 0.88 0.88 0.88 2 25 1 1 1 1 1 2 30 1.5 1.5 1.5 1.5 1.5 2 40 2 2 2 2 2 2 50 2.5 2.5 2.5 2.5 2.5 3 60 3 3 3 3 3 3 70 3.5 3.5 3.5 3.5 3.5 4 80 4 4 4 4 4 4 90 5 5 5 5 5 5

100 6 6 6 6 6 5 110 7.5 7.5 7.5 7.5 7.5 5 120 7.5 7.5 7.5

CIL Cast Iron Lined

0 0.03 0.03 0.05

Epoxy

1.5

Bitumen

1.5

Cement

15 0.05 0.05 0.05 1.5 1.520 0.88 0.88 0.05 1.5 1.525 1 1 0.05 1.5 1.530 1.5 1.5 0.05 1.5 1.540 2 2 0.05 1.5 1.550 2.5 2.5 0.05 1.5 1.560 3 3 0.05 1.5 1.570 3.5 3.5 0.05 1.5 1.5

CONC Concrete

0 0.06 0.06 30 0.15 0.15 40 0.3 0.3 50 0.5 0.5



Material Age

Company 1

Company 2

Company 3

Company 4

Company 5

Company 6

CU Copper 0 0.01 0.01 0.05

DI Ductile Iron

0 0.15 0.15 0.15 0.15 1.5 20 0.3 0.3 0.3 0.3 1.5 30 0.6 0.6 0.6 0.6 1.5 40 0.9 0.9 0.6 0.6 1.5

DIL Ductile Iron Lined

0 0.15 0.15 0.05

Epoxy

1.5

Bitumen

1.5

Cement 20 0.3 0.3 0.05 1.5 1.530 0.6 0.6 0.05 1.5 1.540 0.9 0.9 0.05 1.5 1.5

FC Fire Clay

0 0.03 20 0.06 30 0.08 40 0.1 50 0.2 60 0.3 70 0.4

GI Galvanised Iron

0 0.6 0.6 20 1 1 30 1.5 1.5 40 2 2 50 2.5 2.5 60 3 3 70 3.5 3.5

GS Galvanised Steel

0 0.6 0.6 0.05 20 1 1 0.05 30 1.5 1.5 0.05 40 2 2 0.05 50 2.5 2.5 0.05 60 3 3 0.05

HDPE High Density Polyethylene 0 0.01 0.01 0.01 0.03 0.01

HPPE High Performance Polyethylene 0 0.01 0.01 0.01 0.03 0.01

MDPE Medium Density Polyethylene 0 0.01 0.01 0.01 0.03 0.01



Material Age

Company 1

Company 2

Company 3

Company 4

Company 5

Company 6

PE Polyethylene 0 0.01 0.01 0.01 0.03 0.01

PSC Pre Stressed Concrete

0 0.03 0.03 0.03 0.01 20 0.06 0.06 0.06 0.01 30 0.09 0.09 0.08 0.01 40 0.12 0.12 0.1 0.01

PVC Polyvinyl Chloride

50 0.2 60 0.3 70 0.3 0 0.03 0.03 0.03 0.01

20 0.06 0.06 0.06 0.01 30 0.09 0.09 0.08 0.01 40 0.12 0.12 0.1 0.01

RC Reinforced Concrete

0 0.06 0.06 30 0.15 0.15 40 0.3 0.3 50 0.5 0.5

SI Spun Iron

0 0.6 0.6 0.6 20 1 1 1 30 1.5 1.5 1.5 40 2 2 2 50 2.5 2.5 2 60 3 3 2.5 70 3.5 3.5 3

ST Steel

0 0.3 0.3 0.3 0.3 0.05 20 1 1 1 1 0.05 40 2 2 2 2 0.05 60 3 3 3 3 0.05 80 5 5 5 5 0.05 90 7.5 7.5 5 5 0.05

TPL Thermo Pipe Liner 0 0.03 0.03

uPVC Un-plasticised

Poly Vinyl Chloride

0 0.03 0.03 0.03 0.03 0.01 20 0.06 0.06 0.06 0.06 0.01 30 0.09 0.09 0.09 0.09 0.01 40 0.12 0.12 0.09 0.09 0.01

cwmag best practice guide to hydraulic modelling … · 2020. 11. 17. · project title: cwmag best...

Documents