G. Mahinthakumar

Battle of the Water Networks II

The Battle of the Water Networks II (BWN-II) is the latest of a series of competitions related to the design and operation of water distribution systems (WDSs) undertaken within the Water Distribution Systems Analysis (WDSA) Symposium series. The BWN-II problem specification involved a broadly defined design and operation problem for an existing network that has to be upgraded for increased future demands, and the addition of a new development area. The design decisions involved addition of new and parallel pipes, storage, operational controls for pumps and valves, and sizing of backup power supply. Design criteria involved hydraulic, water quality, reliability, and environmental performance measures. Fourteen teams participated in the Battle and presented their results at the 14th Water Distribution Systems Analysis conference in Adelaide, Australia, September 2012. This paper summarizes the approaches used by the participants and the results they obtained. Given the complexity of the BWN-II problem and the innovative methods required to deal with the multiobjective, high dimensional and computationally demanding nature of the problem, this paper represents a snap-shot of state of the art methods for the design and operation of water distribution systems. A general finding of this paper is that there is benefit in using a combination of heuristic engineering experience and sophisticated optimization algorithms when tackling complex real-world water distribution system design problems

Identification of dominant source of errors in developing streamflow and groundwater projections under near‐term climate change

Uncertainties in projecting the changes in hydroclimatic variables (i.e., temperature and precipitation) under climate change partly arises from the inability of global circulation models (GCMs) in explaining the observed changes in hydrologic variables. Apart from the unexplained changes by GCMs, the process of customizing GCM projections to watershed scale through a model chain—spatial downscaling, temporal disaggregation, and hydrologic model—also introduces errors, thereby limiting the ability to explain the observed changes in hydrologic variability. Toward this, we first propose metrics for quantifying the errors arising from different steps in the model chain in explaining the observed changes in hydrologic variables (streamflow and groundwater). The proposed metrics are then evaluated using a detailed retrospective analyses in projecting the changes in streamflow and groundwater attributes in four target basins that span across a diverse hydroclimatic regimes over the U.S. Sunbelt. Our analyses focused on quantifying the dominant sources of errors in projecting the changes in eight hydrologic variables—mean and variability of seasonal streamflow, mean and variability of 3 day peak seasonal streamflow, mean and variability of 7 day low seasonal streamflow, and mean and standard deviation of groundwater depth—over four target basins using an Penn state Integrated Hydrologic Model (PIHM) between the period 1956–1980 and 1981–2005. Retrospective analyses show that small/humid (large/arid) basins show increased (reduced) uncertainty in projecting the changes in hydrologic attributes. Further, changes in error due to GCMs primarily account for the unexplained changes in mean and variability of seasonal streamflow. On the other hand, the changes in error due to temporal disaggregation and hydrologic model account for the inability to explain the observed changes in mean and variability of seasonal extremes. Thus, the proposed metrics provide insights on how the error in explaining the observed changes being propagated through the model under different hydroclimatic regimes.

Multivariate Downscaling Approach Preserving Cross Correlations across Climate Variables for Projecting Hydrologic Fluxes

AbstractMost of the currently employed procedures for bias correction and statistical downscaling primarily consider a univariate approach by developing a statistical relationship between large-scale precipitation/temperature with the local-scale precipitation/temperature, ignoring the interdependency between the two variables. In this study, a multivariate approach, asynchronous canonical correlation analysis (ACCA), is proposed and applied to global climate model (GCM) historic simulations and hindcasts from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to downscale monthly precipitation and temperature over the conterminous United States. ACCA is first applied to the CNRM-CM5 GCM historical simulations for the period 1950–99 and compared with the bias-corrected dataset based on quantile mapping from the Bureau of Reclamation. ACCA is also applied to CNRM-CM5 hindcasts and compared with univariate asynchronous regression (ASR), which applies regular regression to sorted GCM and observed v...

Parallel Evolutionary Algorithm for Designing Water Distribution Networks to Minimize Background Leakage

AbstractLeaks in water distribution systems waste energy and water resources, increase damage to infrastructure, and may allow contamination of potable water. This research develops an evolutionary algorithm-based approach to minimize the cost of water loss, new infrastructure, and operations that reduce background leakage. A new design approach is introduced that minimizes capital and operational costs, including energy and water loss costs. Design decisions identify a combination of infrastructure improvements, including pipe replacement and valve installment, and operation rules for tanks and pumps. Solution approaches are developed to solve both a single-objective and multiobjective problem formulation. A genetic algorithm and a nondominated sorting genetic algorithm are implemented within a high-performance computing platform to select tank sizes, pump placement and operations, placement of pressure-reducing valves, and pipe diameters for replacing pipes. The evolutionary algorithm approaches identif...

Leak Detection in Water Distribution Systems Using the Dividing Rectangles (DIRECT) Search

Leak detection and management is an important problem in water distribution systems since it has been documented that up to 40% of the water may be lost to leaks in many aging systems. Small gradual leaks, which represent more than half of all leaks, are difficult to locate. Routinely measured pressure, flow, and water quality data in combination with a simulation-optimization inverse modeling approach could be used to characterize leakage. In this approach, the leak locations are found by minimizing the difference between real and simulated measurements for a known sensor configuration. Simulation-optimization approaches are computationally demanding because millions of simulations of a network simulator (e.g., EPANET) may be required to achieve a satisfactory solution. This problem is alleviated using a high performance computing (HPC) framework that enables many parallel simulations of the water system using EPANET. This research is modifying an existing global search algorithm, called the Dividing Rectangles (DIRECT) Search that is traditionally used for continuous functions, to enable parallel simulations and a mix of discrete variables (for leak locations) and continuous variables (for leak magnitudes). The modified algorithm is being tested with traditional continuous test functions, discrete test functions, and test water distribution networks. 1. Motivation Water distribution systems are a vital part of modern infrastructure, yet they are susceptible to leaks and contaminant intrusion. High pressure, freezing water, or aging can cause cracks in the distribution pipes that lead to small, gradual leaks into the ground that are difficult to detect. In some aging systems, up to 40% of water is lost to leaks [1]. Utilities typically monitor locations that are prone to leak, based on a history of previous leaks or the age of the pipes. A leak can be detected, for example, by using acoustic listening devices that pick up on the sound of water escaping from the pipe, among other methods. However, it is expensive and time intensive to manually check the suspected pipes. There are routinely collected measurements of pressure, flow, and water quality at sensor locations. These measurements can carry a signature that will help identify the leak location and

Successive Linear Approximation Methods for Leak Detection in Water Distribution Systems

AbstractIn many modern water networks, an emerging trend is to measure pressure at various points in the network for operational reasons. Because leaks typically induce a signature on pressure, the...

Parallel Multi-Swarm Optimization Framework for Search Problems in Water Distribution Systems

Population based heuristic search methods such as evolutionary algorithms (EA) and particle swarm optimization (PSO) methods are widely used for solving optimization problems especially when classical techniques are inadequate. A parallel optimization framework using multiple concurrent particle swarms is developed and applied to water distribution problems. Details of the enabling framework that couples the optimization methods with a parallel simulator built around EPANET will be discussed. In addition, algorithmic and computational performance results using ORNL’s and ANL’s leadership class parallel architectures will be presented for leakage detection and contaminant source characterization problems for two water distribution networks with 1,834 and 12,457 nodes respectively.