Publications

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The detection of anomalous water quality events has become an increased priority for distribution systems, both for quality of service and security reasons. Because of the high cost associated with false detections, both missed events and false alarms, algorithms which aim to provide event detection aid need to be evaluated and configured properly. CANARY has been developed to provide both real-time, and off-line analysis tools to aid in the development of these algorithms, allowing algorithm developers to focus on the algorithms themselves, rather than on how to read in data and drive the algorithms. Among the features to be discussed and demonstrated are: 1) use of a standard data exchange format for input and output of water quality and operations data streams; 2) the ability to "plug in" various water quality change detection algorithms, both in MATLAB® and compiled library formats for testing and evaluation by using a well defined interface; 3) an "operations mode" to simulate what a utility operator will receive; 4) side-by-side comparison tools for different evaluation metrics, including ROC curves, time to detect, and false alarm rates. Results will be shown using three algorithms previously developed (Klise and McKenna, 2006; McKenna, et al., 2006) using test and real-life data sets. © 2007 ASCE.

A small-scale 3×3 pipe network was simulated to evaluate the validity of complete-mixing and incomplete-mixing models for water distribution systems under different flow rates and boundary conditions. CFD simulations showed that accurate predictions of spatially variable tracer concentrations throughout the network could be attained when compared to experimental data. In contrast, an EPANET model that assumed complete mixing within the junctions yielded uniform concentrations throughout the network, which was significantly different than the spatially variable concentrations observed in the experimental network. The EPANET model was also modified to include mixing correlations derived from previous single-joint experiments. The results from the modified model correctly reflected the incomplete mixing at the pipe junctions and matched the trend in the experimental data. Additional CFD simulations showed that networks comprised of T-junctions separated by at least several pipe diameters could be adequately modeled with complete-mixing models. © 2007 ASCE.

This paper presents computational simulations and experiments of water flow and contaminant transport through pipes with incomplete mixing at pipe joints. The hydraulics and contaminant transport were modeled using computational fluid dynamics software that solves the continuity, momentum, energy, and species equations (laminar and turbulent) using finite-element methods. Simulations were performed of experiments consisting of individual and multiple pipe joints where tracer and clean water were separately introduced into the pipe junction. Results showed that the incoming flow streams generally remained separated within the junction, leading to incomplete mixing of the tracer. Simulations of the mixing matched the experimental results when appropriate scaling of the tracer diffusivity (via the turbulent Schmidt number) was calibrated based on results of single-joint experiments using cross and double-T configurations. Results showed that a turbulent Schmidt number between ∼0.001-0.01 was able to account for enhanced mixing caused by instabilities along the interface of impinging flows. Unequal flow rates within the network were also shown to affect the outlet concentration at each pipe junction, with "enhanced" or "reduced" mixing possible depending on the relative flow rates entering the junction. Copyright ASCE 2006.

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Solute plumes are believed to disperse in a non-Fickian manner due to small-scale heterogeneity and variable velocities that create preferential pathways. In order to accurately predict dispersion in naturally complex geologic media, the connection between heterogeneity and dispersion must be better understood. Since aquifer properties can not be measured at every location, it is common to simulate small-scale heterogeneity with random field generators based on a two-point covariance (e.g., through use of sequential simulation algorithms). While these random fields can produce preferential flow pathways, it is unknown how well the results simulate solute dispersion through natural heterogeneous media. To evaluate the influence that complex heterogeneity has on dispersion, we utilize high-resolution terrestrial lidar to identify and model lithofacies from outcrop for application in particle tracking solute transport simulations using RWHet. The lidar scan data are used to produce a lab (meter) scale two-dimensional model that captures 2-8 mm scale natural heterogeneity. Numerical simulations utilize various methods to populate the outcrop structure captured by the lidar-based image with reasonable hydraulic conductivity values. The particle tracking simulations result in residence time distributions used to evaluate the nature of dispersion through complex media. Particle tracking simulations through conductivity fields produced from the lidar images are then compared to particle tracking simulations through hydraulic conductivity fields produced from sequential simulation algorithms. Based on this comparison, the study aims to quantify the difference in dispersion when using realistic and simplified representations of aquifer heterogeneity.

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Non-equilibrium sorption of contaminants in ground water systems is examined from the perspective of sorption rate estimation. A previously developed Markov transition probability model for solute transport is used in conjunction with a new conditional probability-based model of the sorption and desorption rates based on breakthrough curve data. Two models for prediction of spatially varying sorption and desorption rates along a one-dimensional streamline are developed. These models are a Markov model that utilizes conditional probabilities to determine the rates and an ensemble Kalman filter (EnKF) applied to the conditional probability method. Both approaches rely on a previously developed Markov-model of mass transfer, and both models assimilate the observed concentration data into the rate estimation at each observation time. Initial values of the rates are perturbed from the true values to form ensembles of rates and the ability of both estimation approaches to recover the true rates is examined over three different sets of perturbations. The models accurately estimate the rates when the mean of the perturbations are zero, the unbiased case. For the cases containing some bias, addition of the ensemble Kalman filter is shown to improve accuracy of the rate estimation by as much as an order of magnitude.