Publications

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To protect drinking water systems, a contamination warning system can use in-line sensors to indicate possible accidental and deliberate contamination. Currently, reporting of an incident occurs when data from a single station detects an anomaly. This paper proposes an approach for combining data from multiple stations to reduce false background alarms. By considering the location and time of individual detections as points resulting from a random space-time point process, Kulldorff's scan test can find statistically significant clusters of detections. Using EPANET to simulate contaminant plumes of varying sizes moving through a water network with varying amounts of sensing nodes, it is shown that the scan test can detect significant clusters of events. Also, these significant clusters can reduce the false alarms resulting from background noise and the clusters can help indicate the time and source location of the contaminant. Fusion of monitoring station results within a moderately sized network show false alarm errors are reduced by three orders of magnitude using the scan test. © 2011 ASCE.

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We consider the problem of placing sensors in a municipal water network when we can choose both the location of sensors and the sensitivity and specificity of the contamination warning system. Sensor stations in a municipal water distribution network continuously send sensor output information to a centralized computing facility, and event detection systems at the control center determine when to signal an anomaly worthy of response. Although most sensor placement research has assumed perfect anomaly detection, signal analysis software has parameters that control the tradeoff between false alarms and false negatives. We describe a nonlinear sensor placement formulation, which we heuristically optimize with a linear approximation that can be solved as a mixed-integer linear program. We report the results of initial experiments on a real network and discuss tradeoffs between early detection of contamination incidents, and control of false alarms.

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While connectivity is an important aspect of heterogeneous media, methods to measure and simulate connectivity are limited. For this study, we use natural aquifer analogs developed through lidar imagery to track the importance of connectivity on dispersion characteristics. A 221.8 cm by 50 cm section of a braided sand and gravel deposit of the Ceja Formation in Bernalillo County, New Mexico is selected for the study. The use of two-point (SISIM) and multipoint (Snesim and Filtersim) stochastic simulation methods are then compared based on their ability to replicate dispersion characteristics using the aquifer analog. Detailed particle tracking simulations are used to explore the streamline-based connectivity that is preserved using each method. Connectivity analysis suggests a strong relationship between the length distribution of sand and gravel facies along streamlines and dispersion characteristics.

This report summarizes the experimental and modeling effort undertaken to understand solute mixing in a water distribution network conducted during the last year of a 3-year project. The experimental effort involves measurement of extent of mixing within different configurations of pipe networks, measurement of dynamic mixing in a single mixing tank, and measurement of dynamic solute mixing in a combined network-tank configuration. High resolution analysis of turbulence mixing is carried out via high speed photography as well as 3D finite-volume based Large Eddy Simulation turbulence models. Macroscopic mixing rules based on flow momentum balance are also explored, and in some cases, implemented in EPANET. A new version EPANET code was developed to yield better mixing predictions. The impact of a storage tank on pipe mixing in a combined pipe-tank network during diurnal fill-and-drain cycles is assessed. Preliminary comparison between dynamic pilot data and EPANET-BAM is also reported.

This paper investigates the use of strip transect sampling to estimate object abundance when the underlying spatial distribution is assumed to be Poisson. A design-rather than model-based approach to estimation is investigated through computer simulation, with both homogeneous and non-homogeneous fields representing individual realizations of spatial point processes being considered. Of particular interest are the effects of changing the number of transects and transect width (or alternatively, coverage percent or fraction) on the quality of the estimate. A specific application to the characterization of unexploded ordnance (UXO) in the subsurface at former military firing ranges is discussed. The results may be extended to the investigation of outcrop characteristics as well as subsurface geological features. © 2008 Springer-Verlag Berlin Heidelberg.

Using a slab of Massillon Sandstone, laboratory-scale solute tracer experiments were carried out to test numerical simulations using the Advection-Dispersion Equation (ADE). While studies of a similar nature exist, our work differs in that we combine: (1) experimentation in naturally complex geologic media, (2) X-ray absorption imaging to visualize and quantify two-dimensional solute transport, (3) high resolution transport property characterization, with (4) numerical simulation. The simulations use permeability, porosity, and solute concentration measured to sub-centimeter resolution. While bulk breakthrough curve characteristics were adequately matched, large discrepancies exist between the experimental and simulated solute concentration fields. Investigation of potential experimental errors suggests that the failure to fit solute concentration fields may lie in loss of intricate connectivity within the cross-bedded sandstone occurring at scales finer than our property characterization measurements (i.e., sub-centimeter). © 2008 Elsevier Ltd. All rights reserved.

To protect drinking water systems, a contamination warning system can use in-line sensors to detect accidental and deliberate contamination. Currently, detection of an incident occurs when data from a single station detects an anomaly. This paper considers the possibility of combining data from multiple locations to reduce false alarms and help determine the contaminant's injection source and time. If we consider the location and time of individual detections as points resulting from a random space-time point process, we can use Kulldorff's scan test to find statistically significant clusters of detections. Using EPANET, we simulate a contaminant moving through a water network and detect significant clusters of events. We show these significant clusters can distinguish true events from random false alarms and the clusters help identify the time and source of the contaminant. Fusion results show reduced errors with only 25% more sensors needed over a nonfusion approach. © 2008 ASCE.

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