Publications

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We present a methodology for optimally locating disinfectant booster stations for response to contamination events in water distribution systems. A stochastic programming problem considering uncertainty in both the location and time of the contamination event is formulated resulting in an extensive form that is equivalent to the weighted maximum coverage problem. Although the original full-space problem is intractably large, we show a series of reductions that reduce the size of the problem by five orders of magnitude and allow solutions of the optimal placement problem for realistically sized water network models. © 2012 Elsevier B.V.

We investigate Bayesian techniques that can be used to reconstruct field variables from partial observations. In particular, we target fields that exhibit spatial structures with a large spectrum of lengthscales. Contemporary methods typically describe the field on a grid and estimate structures which can be resolved by it. In contrast, we address the reconstruction of grid-resolved structures as well as estimation of statistical summaries of subgrid structures, which are smaller than the grid resolution. We perform this in two different ways (a) via a physical (phenomenological), parameterized subgrid model that summarizes the impact of the unresolved scales at the coarse level and (b) via multiscale finite elements, where specially designed prolongation and restriction operators establish the interscale link between the same problem defined on a coarse and fine mesh. The estimation problem is posed as a Bayesian inverse problem. Dimensionality reduction is performed by projecting the field to be inferred on a suitable orthogonal basis set, viz. the Karhunen-Loeve expansion of a multiGaussian. We first demonstrate our techniques on the reconstruction of a binary medium consisting of a matrix with embedded inclusions, which are too small to be grid-resolved. The reconstruction is performed using an adaptive Markov chain Monte Carlo method. We find that the posterior distributions of the inferred parameters are approximately Gaussian. We exploit this finding to reconstruct a permeability field with long, but narrow embedded fractures (which are too fine to be grid-resolved) using scalable ensemble Kalman filters; this also allows us to address larger grids. Ensemble Kalman filtering is then used to estimate the values of hydraulic conductivity and specific yield in a model of the High Plains Aquifer in Kansas. Strong conditioning of the spatial structure of the parameters and the non-linear aspects of the water table aquifer create difficulty for the ensemble Kalman filter. We conclude with a demonstration of the use of multiscale stochastic finite elements to reconstruct permeability fields. This method, though computationally intensive, is general and can be used for multiscale inference in cases where a subgrid model cannot be constructed.

This document summarizes research performed under the SNL LDRD entitled - Computational Mechanics for Geosystems Management to Support the Energy and Natural Resources Mission. The main accomplishment was development of a foundational SNL capability for computational thermal, chemical, fluid, and solid mechanics analysis of geosystems. The code was developed within the SNL Sierra software system. This report summarizes the capabilities of the simulation code and the supporting research and development conducted under this LDRD. The main goal of this project was the development of a foundational capability for coupled thermal, hydrological, mechanical, chemical (THMC) simulation of heterogeneous geosystems utilizing massively parallel processing. To solve these complex issues, this project integrated research in numerical mathematics and algorithms for chemically reactive multiphase systems with computer science research in adaptive coupled solution control and framework architecture. This report summarizes and demonstrates the capabilities that were developed together with the supporting research underlying the models. Key accomplishments are: (1) General capability for modeling nonisothermal, multiphase, multicomponent flow in heterogeneous porous geologic materials; (2) General capability to model multiphase reactive transport of species in heterogeneous porous media; (3) Constitutive models for describing real, general geomaterials under multiphase conditions utilizing laboratory data; (4) General capability to couple nonisothermal reactive flow with geomechanics (THMC); (5) Phase behavior thermodynamics for the CO2-H2O-NaCl system. General implementation enables modeling of other fluid mixtures. Adaptive look-up tables enable thermodynamic capability to other simulators; (6) Capability for statistical modeling of heterogeneity in geologic materials; and (7) Simulator utilizes unstructured grids on parallel processing computers.

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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.

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