Water quality processes in the drinking water distribution network are strongly influenced by the flow velocity and residence time of the water in the network. In order to understand how the water quality changes in the drinking water distribution network, a good understanding of hydraulics is required. Specifically in the periphery of the network, where customers are connected, the hydraulics can change rapidly. During the night time the water is almost stagnant and the residence time increases. In the morning, when everybody gets up and flushes the toilet and takes a shower, high flow velocities can occur. During the remainder of the day flow velocities are low. The stochastic endues model SIMDEUM was developed to simulate water use on a small time scale (1 s) and small spatial scale (per fixture). SIMDEUM enables a good model of flow velocities, residence times and the connected water quality processes in the water distribution network.
Stochastic Water Demand Modelling: Hydraulics in Water Distribution Networks describes the requirements of hydraulics in water quality modelling and provides insight into the development of detailed residential and non-residential water demand models. The book illustrates the use of detailed demand models in water quality models with respect to the variation in residence times and the relation with particle accumulation and resuspension. The models are compared to measurements in several real drinking water distributi
Chapter
2.3 WATER QUALITY MODELLING - PARTICULATE MATTER
2.4 DEMANDS IN HYDRAULIC NETWORK MODELS
3.2 METHODS AND MATERIALS - STATISTICAL ANALYSIS
3.3 METHODS AND MATERIALS - MODEL DEVELOPMENT
3.3.2 Justification of input sources
3.3.5 The frequency of use (F)
3.3.6 The pulse intensity (I)
3.3.7 The pulse duration (D)
3.3.8 The diurnal pattern, time of water use (t)
3.4 METHODS AND MATERIALS - THE SIMULATION
3.5 METHODS AND MATERIALS - MODEL VALIDATION PARAMETERS
3.6 METHODS AND MATERIALS - MODEL VALIDATION
4.2 METHODS AND MATERIALS - MODEL DEVELOPMENT
4.2.2 The functional rooms (h)
4.2.5 The frequency of use (f)
4.2.6 The pulse intensity (I) and pulse duration (D)
4.2.7 The diurnal pattern, time of water use (t)
4.3 METHODS AND MATERIALS - THE SIMULATION
4.4 METHODS AND MATERIALS - MODEL VALIDATION
4.5 METHODS AND MATERIALS - SENSITIVITY ANALYSIS
4.6 RESULTS - MODEL VALIDATION
4.7 RESULTS - SENSITIVITY ANALYSIS
5.2 MATERIALS AND METHODS
5.2.1 The Poisson Rectangular Pulse (PRP) model
5.2.2 The End-Use Model SIMDEUM
5.2.3 The flow measurements of Milford, Ohio
5.2.4 Comparing the two models
5.2.5 Parameters to compare measurements and simulation results
5.3 RESULTS: COMPARING THE TWO MODELS
5.3.1 Comparing the two models on underlying principles
5.3.2 Comparing the black box models
5.3.3 Comparing the input data
5.3.4 Comparing the output data: results for single homes
5.3.5 Comparing the output data: results for sum of 20 homes
6.2 METHODS AND MATERIALS - BENTHUIZEN AREA
6.2.1 Generic methodology
6.2.3 Measurement setup for the tracer study
6.2.4 Hydraulic model and demand allocation
6.2.5 Water demand pattern generation
6.2.6 Water quality model
6.2.7 Sensitivity analysis and model validation
6.3 RESULTS AND DISCUSSION - BENTHUIZEN AREA
6.3.1 Demand multiplier pattern
6.3.2 Residence time - sensitivity analysis
6.3.3 Residence time - model validation
6.4 INTERMEDIATE CONCLUSIONS FROM BENTHUIZEN STUDY
6.5 METHODS AND MATERIALS - ZANDVOORT AREA
6.5.2 Measurement setup for the tracer study
6.5.3 Hydraulic model and demand allocation
6.5.4 Water demand pattern generation
6.6 RESULTS AND DISCUSSION - ZANDVOORT AREA
6.6.1 Diurnal flow pattern
6.7 INTERMEDIATE CONCLUSIONS FROM ZANDVOORT STUDY
7.2 METHODS AND MATERIALS
7.2.3 Flushing the network
7.2.4 Water demand pattern generation
7.2.5 The hydraulic network model and demand allocation
7.2.6 Determining the relationship between hydraulics and settled sediment
7.3 RESULTS AND DISCUSSION
7.3.1 Interpretation of results
7.3.2 Theory of self-cleaning
7.3.3 Practical implications
7.3.4 Design implications for self-cleaning networks
8.2 CONSTRUCTING THE END-USE MODEL SIMDEUM
8.2.1 The approach of modelling the end user
8.2.2 Advantage of end-use modelling
8.2.3 An undemanding model
8.2.4 Pressure driven demand
8.3 CASE STUDIES - MEASUREMENT ISSUES
8.4 CASE STUDIES - NETWORK SOLVER CONSIDERATIONS
8.5 EVALUATION OF ADDED VALUE OF SIMDEUM
8.5.1 Effects of detailed demand model in DWDS network model
8.6 APPLICATIONS OF SIMDEUM IN RESEARCH ON WATER QUALITY IN THE DWDS
8.6.1 Fouling prediction tool
8.6.2 Design principles for self-cleaning networks
8.6.3 Maximum travel time
8.7 PRACTICAL APPLICATIONS OF SIMDEUM
8.7.1 Water demand management
8.7.3 Risk analysis of contamination through ingestion
8.7.4 Dimensioning plumbing and water heaters
8.7.5 Water-related energy use
8.7.6 Practical application of SIMDEUM in network modelling
9.2 DEVELOPING AND VALIDATING SIMDEUM: A SIMULATION MODEL FOR DRINKING WATER DEMAND
9.3 APPLYING SIMDEUM IN CASE STUDIES: THE STUDY OF RESIDENCE TIMES AND PARTICLES IN THE DISTRIBUTION NETWORK
9.4 EVALUATING ADDED VALUE OF SIMDEUM: COMPARING SIMDEUM TO EXISTING MODELS
9.5 EVALUATING ADDED VALUE OF SIMDEUM: NEW WAY OF DEMAND MODELLING IS REQUIRED FOR MODELLING WATER QUALITY IN THE DISTRIBUTION N
A.1 AUTO- AND CROSS-CORRELATION
A.3 PROBABILITY DISTRIBUTION FUNCTIONS
A.4 STATISTICAL TEST FOR NORMAL DISTRIBUTION
Stochastic Water Demand Modelling
( KWR Watercycle Research Institute Series )
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