Alireza Yazdani Post-Doctoral Research Associate Department of Civil & Environmental Engineering
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Transcript of Alireza Yazdani Post-Doctoral Research Associate Department of Civil & Environmental Engineering
Quantifying Uncertainty to Support Sustainable Planning and Management of
Water Supply Infrastructure
Alireza Yazdani
Post-Doctoral Research AssociateDepartment of Civil & Environmental Engineering
Rice University
Presented at:SAMSI Uncertainty Quantification Transition Workshop
May 22nd, 2012
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SUSTAINABLE WATER SUPPLY MANAGEMENT
SYSTEM PERFORMANCE EVALUATION
UNCERTAINTY QUANTIFICATION
NETWORK TOPOLOGY
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Water Supply Infrastructure
• Water Distribution Systems (WDS) are large complex networks of multiple interdependent nodes (e.g. reservoirs, fittings, fire hydrants) joined by links (e.g. pipes, valves, pumps).
• Main system components:• Source• Treatment• Transmission• Storage• Distribution
A hypothetical network representation
• The US Water infrastructure is old, fragile and inadequate in meeting the increasing demand for water.
• Last year’s Texas drought resulted in a spike in water main breaks (CBS local, Aug 2011).
• Existing centralized networks, suffer from high water age, bio-film growth, pressure loss and high energy consumption.
• There is currently an underinvestment (~ $108.6 Billion).4
The problem
Source: (EPA, 2006 Committee on Public Water Supply Distribution Systems: Assessing and Reducing Risks, National Research Council, and 2009 Report Card for America’s Infrastructure)
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Aviation DBridges C Dams DDrinking Water D-Energy D+Hazardous Waste DInland Waterways D-Levees D-Public Parks & Recreation C-Rail C-Roads D-School DSolid Waste C+Transit DWastewater D-
America's Infrastructure G.P.A. = D
A = Exceptional B = GoodC = MediocreD = Poor F = Failing
2009 ASCE Report Card for America’s Infrastructure
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Sustainability
• A sustainable Water Supply System is one that supplies anticipated demands over a sensible time horizon without degradation of the source of the supply or other element’s of the system’s environment.*
• Criteria:
• Reliability: • adequate flow and pressure, availability of
the physical components
• Water Quality: • Acceptable water age and chemical
contents
• Efficiency: • leakage management, operational efficiency
and environmental impacts
Achieving sustainability requires integrated analysis and optimization of performance criteria while dealing with uncertainties in the data/model/natural environment
* Water Distribution Systems (2011), D. Savic, J. Banyard (Eds.), ICE Press.
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Efficiency: what is the cost/impacts of getting water here?
Adequacy (quality/quantity): How does water taste there? Is the pressure sufficient?
Reliability: what if these pipes break together?!
Reservoir and treatment facilities
A slightly reconfigured EPANET representation of Colorado Springs WDS
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Uncertainty and Decision Making• Reducible ( epistemic) uncertainty: Resulting from a lack of
information in model about the system, typically reduced through inspection, measurement or improving the analogy between the abstract model and real system
• Irreducible (aleatoric) uncertainty: Natural randomness in a process, usually described by probabilistic approaches
Image taken from: S. Fox (2011), Factors in ontological uncertainty related to ICT innovations, I. J. Manag. Proj. Busin, 4 (1), 137-149.
Not to be absolutely certain is, I think, one of the essential things in rationality.
Bertrand Russell
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Examples from Water Supply Engineering
• Model (e): inability to represent true physics of the system and its behaviour
• Data (e): measurement error, inconsistent/inaccurate/inadequate data
• Operation (e): related to the system construction, design, equipments, deterioration, maintenance
• Natural (a): unpredictability of nature and its impacts on the system
• Determining the pipe size, tank diameter, network topology at design stage
• Placement of sensors/control valves to monitor water quality
• Prediction of the physical components failure rates and evaluating failure consequences
• Estimating water weekly/monthly/yearly water demand to support normal/peak consumption
• Assessing the impacts of climate/demographical changes on resources
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Pipe Break/Contaminant
Ingress
Source unavailable
• Reliability: how often the system fails (in quantity or quality terms).
• Vulnerability: how serious the consequences of the failure may be.
• Resiliency: how quickly the system recovers from failure.
Reservoir
Tank
Reservoir
WDS Performance is largely affected by network topology
Uncertainty in system performance due to the unknown/unpredictable parameters may be reduced through studying topology.
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• Centralized treatment/operation
• water quality deterioration
• cost of wastewater collection
• high energy loss
• Decentralized treatment
• shorter pipe lengths
• improved water quality?
• more efficient?
Image from D. Kang, K. Lansey, Scenario-based Robust Optimization of Regional Water/Wastewater Infrastructure,doi:10.1061/(ASCE)WR.1943-5452.0000236
The Need and Practicality
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Network measurements
Metric Proxy for
Spectral Graph Theory •Fault-tolerance (design)•Rate of contaminant spread
Centrality measures•Component criticality analysis•Network vulnerability to random failures/targeted attacks
Path length/distances
•Friction losses•Design/Operation Cost•Access between source and nodes•Water residence time
Loops •Redundancy • Reliability
• Random networks:• Random degree distribution (equal connectivity
likelihood) • Network equally vulnerable to failures/attacks
(typical nodes)• Examples: spatial networks (no hubs, large diameter)
• Small worlds:• Gaussian or exponential degree distribution• Large networks with low path lengths and high
clustering
• Scale free networks:• Scale-free networks/power law degree distribution• Many low degree nodes with very few highly
connected hubs• Robust against random component failures yet fragile
under targeted attacks on the hubs
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Network topology models
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Image: Albert, Barabasi and Bonabeau, (2003), Scale-free Networks, Scientific American, 288, 50-59.
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Case studiesColorado Springs (CS), USA
Richmond Yorkshire Water (RYW), UK
City of Houston (COH), USA
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Case studies (cont.)Metric Colorado
SpringsCity of
HoustonRichmon
d
Nodes 1786 3926 872Links 1994 5801 957Total pipe length (km) 117.01 3166.15 75.61
Average pipe length (m) 187.12 574.2 633.09
Algebraic connectivity 2.43 e-4 2.26 e-4 6.09 e-5
Average node degree 2.23 2.96 2.19
Average path length 27.23 25.94 51.44
Central-point dominance 0.42 0.34 0.56
Critical ratio of random breakdown 0.57 0.42 0.32
Graph diameter 69 72 135
Maximum node degree 4 9 4
Meshedness coefficient 0.0586 0.239 0.0495
Node (link) connectivity 1 (1) 1 (1) 1 (1)
Topological efficiency 5.2 % 2.4 % 3.4 %
d=0.4
Demand-adjusted entropic degree (DAED)* combines topology and physics by incorporating the number of links attached to a node, the capacity of the link connections and the way they are distributed while taking into account the demand for water at each node.
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Generalized connectivity(an example of reducing model uncertainty at the fine scales)
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W1=1
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W1=0.5 W2=0.5
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W3=0.6
W3=0.3
W3=0.3
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W3=0.2
W3=0.3 W3=0.3
* A. Yazdani, P. Jeffrey (2012), Water Resour. Res., doi:10.1029/2012WR011897, in press
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Generalized connectivity (cont.)
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Colorado Springs’ top three most important nodes
ID Degree DAED Normalized DAED
144 4 542.28 1
1229 3 354.36 0.65
1373 3 192.27 0.36
Richmond’s top three most important nodes
ID Degree DAED Normalized DAED
153 2 94.37 1
20 2 75.59 0.80
219 2 64.38 0.68
Generalized connectivity (cont.)
CS RYW
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Generalized connectivity (a WDS specific alternative for degree distribution)
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Normalized DAED
Pr {
> f
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• The analysis of WDS topology:
Reduces model uncertainty and offers a computationally inexpensive and less data-dependent simplified approach
Helps quantifying vaguely understood qualities such as redundancy, optimal-connectivity and fault-tolerance
Supports development and comparison of the alternative design and operation (e.g. Decentralized) scenarios
• The UQ via studying interactions of system topology and performance (hydraulic reliability, energy use, water quality) provides theoretical support for finding sustainable solutions for water infrastructure systems planning and management (rehabilitation/design/expansion problems).
• Due to the WDS specifications, data and model uncertainties, and hydraulic complexities, advanced UQ techniques (e.g. spectral methods, multiple regression and survival analysis and non-parametric statistics) have a special place in the realistic analysis of WDS vulnerability/sustainability.
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Summary and Conclusions
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Ongoing and future work
• Performance analysis and comparison of the centralized, decentralized and hybrid layouts in terms of water quantity and quality
• Analysis of historical failure data to develop component/system failure rate models serving reliability analysis
• Investigating the role of network topology (in the presence or absence of shut off valves) in facilitating mass transport/preventing the spread of contaminants within the system validated by the EPANET models
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Acknowledgements
• Rice University Shell Centre for Sustainability
• SAMSI for the travel support
• Dr. Leonardo Duenas-Osorio and Dr. Qilin Li of Rice University Civil and Environmental Engineering