Publications

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In the event of contamination in a water distribution network (WDN), source identification (SI) methods that analyze sensor data can be used to identify the source location(s). Knowledge of the source location and characteristics are important to inform contamination control and cleanup operations. Various SI strategies that have been developed by researchers differ in their underlying assumptions and solution techniques. The following manuscript presents a systematic procedure for testing and evaluating SI methods. The performance of these SI methods is affected by various factors including the size of WDN model, measurement error, modeling error, time and number of contaminant injections, and time and number of measurements. This paper includes test cases that vary these factors and evaluates three SI methods on the basis of accuracy and specificity. The tests are used to review and compare these different SI methods, highlighting their strengths in handling various identification scenarios. These SI methods and a testing framework that includes the test cases and analysis tools presented in this paper have been integrated into EPA's Water Security Toolkit (WST), a suite of software tools to help researchers and others in the water industry evaluate and plan various response strategies in case of a contamination incident. Finally, a set of recommendations are made for users to consider when working with different categories of SI methods.

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Our first purpose here is to offer to a general technical and policy audience a perspective on whether the supercomputing community should focus on improving the efficiency of supercomputing systems and their use rather than on building larger and ostensibly more capable systems that are used at low efficiency. After first summarizing our content and defining some necessary terms, we give a concise answer to this question. We then set this in context by characterizing performance of current supercomputing systems on a variety of benchmark problems and actual problems drawn from workloads in the national security, industrial, and scientific context. Along the way we answer some related questions, identify some important technological trends, and offer a perspective on the significance of these trends. Our second purpose is to give a reasonably broad and transparent overview of the related issue space and thereby to better equip the reader to evaluate commentary and controversy concerning supercomputing performance. For example, questions repeatedly arise concerning the Linpack benchmark and its predictive power, so we consider this in moderate depth as an example. We also characterize benchmark and application performance for scientific and engineering use of supercomputers and offer some guidance on how to think about these. Examples here are drawn from traditional scientific computing. Other problem domains, for example, data analytics, have different performance characteristics that are better captured by different benchmark problems or applications, but the story in those domains is similar in character and leads to similar conclusions with regard to the motivating question.

Here, grain boundary-engineered materials are of immense interest for their corrosion resistance, fracture resistance and microstructural stability. This work contributes to a larger goal of understanding both the structure and thermodynamic properties of grain boundaries vicinal (within ±30°) to the Σ3(1 1 1) <1 1¯0> (coherent twin) boundary which is found in grain boundary-engineered materials. The misoriented boundaries vicinal to the twin show structural changes at elevated temperatures. In the case of nickel, this transition temperature is substantially below the melting point and at temperatures commonly reached during processing, making the existence of such boundaries very likely in applications. Thus, the thermodynamic stability of such features is thoroughly investigated in order to predict and fully understand the structure of boundaries vicinal to twins. Low misorientation angle grain boundaries (|θ| ≲ 16°) show distinct ±1/3(1 1 1) disconnections which accommodate misorientation in opposite senses. The two types of disconnection have differing low-temperature structures which show different temperature-dependent behaviours with one type undergoing a structural transition at approximately 600 K. At misorientation angles greater than approximately ±16°, the discrete disconnection nature is lost as the disconnections merge into one another. Free energy calculations demonstrate that these high-angle boundaries, which exhibit a transition from a planar to a faceted structure, are thermodynamically more stable in the faceted configuration.

The electrical conductivity of materials under extremes of temperature and pressure is of crucial importance for a wide variety of phenomena, including planetary modeling, inertial confinement fusion, and pulsed power based dynamic materials experiments. There is a dearth of experimental techniques and data for highly compressed materials, even at known states such as along the principal isentrope and Hugoniot, where many pulsed power experiments occur. We present a method for developing, calibrating, and validating material conductivity models as used in magnetohydrodynamic (MHD) simulations. The difficulty in calibrating a conductivity model is in knowing where the model should be modified. Our method isolates those regions that will have an impact. It also quantitatively prioritizes which regions will have the most beneficial impact. Finally, it tracks the quantitative improvements to the conductivity model during each incremental adjustment. In this paper, we use an experiment on Sandia National Laboratories Z-machine to isentropically launch multiple flyer plates and, with the MHD code ALEGRA and the optimization code DAKOTA, calibrated the conductivity such that we matched an experimental figure of merit to +/-1%.

A minimax estimator has the minimum possible error ("risk") in the worst case. We construct the first minimax estimators for quantum state tomography with relative entropy risk. The minimax risk of nonadaptive tomography scales as O(1/N) - in contrast to that of classical probability estimation, which is O(1/N) - where N is the number of copies of the quantum state used. We trace this deficiency to sampling mismatch: future observations that determine risk may come from a different sample space than the past data that determine the estimate. This makes minimax estimators very biased, and we propose a computationally tractable alternative with similar behavior in the worst case, but superior accuracy on most states.