Alireza Yazdani Post-Doctoral Research Associate Department of
Civil & Environmental Engineering Rice University Presented at:
SAMSI Uncertainty Quantification Transition Workshop May 22 nd,
2012 Slide 2 2 SUSTAINABLE WATER SUPPLY MANAGEMENT SYSTEM
PERFORMANCE EVALUATION UNCERTAINTY QUANTIFICATION NETWORK TOPOLOGY
Slide 3 3 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 Slide 4
The US Water infrastructure is old, fragile and inadequate in
meeting the increasing demand for water. Last years 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 Source: (EPA,
2006 Committee on Public Water Supply Distribution Systems:
Assessing and Reducing Risks, National Research Council, and 2009
Report Card for Americas Infrastructure) Slide 5 5 America's
Infrastructure G.P.A. = D A = Exceptional B = Good C = Mediocre D =
Poor F = Failing 2009 ASCE Report Card for Americas Infrastructure
Slide 6 6 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 elements of the
systems 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. Slide 7 7 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 Slide 8
8 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 Slide 9 9 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 Slide 10 10 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. Slide 11 11 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 Slide 12 12
MetricProxy 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 Slide 13 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 13 Slide 14 14 Image: Albert,
Barabasi and Bonabeau, (2003), Scale-free Networks, Scientific
American, 288, 50-59. Slide 15 15 Colorado Springs (CS), USA
Richmond Yorkshire Water (RYW), UK City of Houston (COH), USA Slide
16 16 Metric Colorado Springs City of Houston Richmond
Nodes17863926872 Links19945801957 Total pipe length
(km)117.013166.1575.61 Average pipe length (m)187.12574.2633.09
Algebraic connectivity2.43 e-42.26 e-46.09 e-5 Average node
degree2.232.962.19 Average path length27.2325.9451.44 Central-point
dominance 0.420.340.56 Critical ratio of random breakdown
0.570.420.32 Graph diameter6972135 Maximum node degree494
Meshedness coefficient0.05860.2390.0495 Node (link) connectivity1
(1) Topological efficiency5.2 %2.4 %3.4 % Slide 17 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. 17 i W1=1 i W1=0.5W2=0.5 i W3=0.6 W3=0.3 i W3=0.2 W3=0.3 * A.
Yazdani, P. Jeffrey (2012), Water Resour. Res.,
doi:10.1029/2012WR011897, in press Slide 18 18 CSRYW Slide 19 19
Colorado Springs top three most important nodes
IDDegreeDAEDNormalized DAED 1444 542.281 12293 354.360.65 13733
192.270.36 Richmonds top three most important nodes IDDegreeDAED
Normalized DAED 1532 94.371 202 75.590.80 2192 64.380.68 CS RYW
Slide 20 20 Slide 21 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. 21 Slide 22 22 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 Slide 23 23 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
