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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Dynamical transitions in a modulated Landau–Zener model with finite driving fields

Highlights: •The rigorous dynamics of a modulated Landau–Zener model is investigated. •Nonadiabatic evolution of the model could realize complete population transfer. •Imperfect effects caused by both the amplitude and phase damping are estimated. -- Abstract: We investigate a special time-dependent quantum model which assumes the Landau–Zener driving form but with an overall modulation of the intensity of the pulsing field. We demonstrate that the dynamics of the system, including the two-level case as well as its multi-level extension, is exactly solvable analytically. Differing from the original Landau–Zener model, the nonadiabatic effect of the evolution in the present driving process does not destroy the desired population transfer. As the sweep protocol employs only the finite driving fields which tend to zero asymptotically, the cutoff error due to the truncation of the driving pulse to the finite time interval turns out to be negligibly small. Furthermore, we investigate the noise effect on the driving protocol due to the dissipation of the surrounding environment. The losses of the fidelity in the protocol caused by both the phase damping process and the random spin flip noise are estimated by solving numerically the corresponding master equations within the Markovian regime.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Wind power curve modeling in complex terrain using statistical models

The simplest power curves model wind power only as a function of the wind speed at the turbine hub height. While the latter is an essential predictor of power output, wind speed information in other parts of the vertical profile, as well as additional atmospheric variables, are also important determinants of power. The goal of this work was to determine the gain in predictive ability afforded by adding wind speed information at other heights, as well as other atmospheric variables, to the power prediction model. Using data from a wind farm with a moderately complex terrain in the Altamont Pass region in California, we trained three statistical models—a neural network, a random forest and a Gaussian process model—to predict power output from various sets of aforementioned predictors. The comparison of these predictions to the observed power data revealed that considerable improvements in prediction accuracy can be achieved both through the addition of predictors other than the hub-height wind speed and the use of statistical models. To our knowledge, the use of the Gaussian process model in this context is new, in contrast to neural networks and random forests. The advantage of this model over the other two models is that it provides a much more natural way to estimate the uncertainty associated with its predictions. In addition, this study presents one of the most comprehensive analyses to date of the relative importance of various wind power curve inputs.

Bulaevskaya, V.↗
NASA NTRS2005

A First and Second Order Moment Approach to Probabilistic Control Synthesis

This paper presents a robust control design methodology based on the estimation of the first two order moments of the random variables and processes that describe the controlled response. Synthesis is performed by solving an multi-objective optimization problem where stability and performance requirements in time- and frequency domains are integrated. The use of the first two order moments allows for the efficient estimation of the cost function thus for a faster synthesis algorithm. While reliability requirements are taken into account by using bounds to failure probabilities, requirements related to undesirable variability are implemented by quantifying the concentration of the random outcome about a deterministic target. The Hammersley Sequence Sampling and the First- and Second-Moment- Second-Order approximations are used to estimate the moments, whose accuracy and associated computational complexity are compared numerically. Examples using output-feedback and full-state feedback with state estimation are used to demonstrate the ideas proposed.

Crespo, Luis G.↗
DOE OSTI2015

SU-E-J-15: Automatically Detect Patient Treatment Position and Orientation in KV Portal Images

Purpose: In the course of radiation therapy, the complex information processing workflow will Result in potential errors, such as incorrect or inaccurate patient setups. With automatic image check and patient identification, such errors could be effectively reduced. For this purpose, we developed a simple and rapid image processing method, to automatically detect the patient position and orientation in 2D portal images, so to allow automatic check of positions and orientations for patient daily RT treatments. Methods: Based on the principle of portal image formation, a set of whole body DRR images were reconstructed from multiple whole body CT volume datasets, and fused together to be used as the matching template. To identify the patient setup position and orientation shown in a 2D portal image, the 2D portal image was preprocessed (contrast enhancement, down-sampling and couch table detection), then matched to the template image so to identify the laterality (left or right), position, orientation and treatment site. Results: Five day’s clinical qualified portal images were gathered randomly, then were processed by the automatic detection and matching method without any additional information. The detection results were visually checked by physicists. 182 images were correct detection in a total of 200kV portal images. The correct rate was 91%. Conclusion: The proposed method can detect patient setup and orientation quickly and automatically. It only requires the image intensity information in KV portal images. This method can be useful in the framework of Electronic Chart Check (ECCK) to reduce the potential errors in workflow of radiation therapy and so to improve patient safety. In addition, the auto-detection results, as the patient treatment site position and patient orientation, could be useful to guide the sequential image processing procedures, e.g. verification of patient daily setup accuracy. This work was partially supported by research grant from Varian Medical System.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Constant-complexity stochastic simulation algorithm with optimal binning

At the molecular level, biochemical processes are governed by random interactions between reactant molecules, and the dynamics of such systems are inherently stochastic. When the copy numbers of reactants are large, a deterministic description is adequate, but when they are small, such systems are often modeled as continuous-time Markov jump processes that can be described by the chemical master equation. Gillespie’s Stochastic Simulation Algorithm (SSA) generates exact trajectories of these systems, but the amount of computational work required for each step of the original SSA is proportional to the number of reaction channels, leading to computational complexity that scales linearly with the problem size. The original SSA is therefore inefficient for large problems, which has prompted the development of several alternative formulations with improved scaling properties. We describe an exact SSA that uses a table data structure with event time binning to achieve constant computational complexity with respect to the number of reaction channels for weakly coupled reaction networks. We present a novel adaptive binning strategy and discuss optimal algorithm parameters. We compare the computational efficiency of the algorithm to existing methods and demonstrate excellent scaling for large problems. This method is well suited for generating exact trajectories of large weakly coupled models, including those that can be described by the reaction-diffusion master equation that arises from spatially discretized reaction-diffusion processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Synergistic Enzyme Mixtures to Realize Near-Complete Depolymerization in Biodegradable Polymer/Additive Blends

Embedding catalysts inside of plastics affords accelerated chemical modification with programmable latency and pathways. Nanoscopically embedded enzymes can lead to near-complete degradation of polyesters via chain-end mediated processive depolymerization. The overall degradation rate and pathways have a strong dependence on the morphology of semicrystalline polyesters. Yet, most studies to date focus on pristine polymers instead of mixtures that contain additives and other components despite their nearly universal use in plastic production. Here, for this study, additives are introduced to purposely change the morphology of polycaprolactone (PCL) by increasing the bending and twisting of crystalline lamellae. These morphological changes immobilize chain ends preferentially at the crystalline/amorphous interfaces and limit chain-end accessibility by the embedded processive enzyme. This chain-end redistribution reduces the polymer-to-monomer conversion from >95% to less than 50%, causing formation of highly crystalline plastic pieces, including microplastics. By synergizing both random chain scission and processive depolymerization, it is feasible to navigate morphological changes in polymer/additive blends and to achieve near-complete depolymerization. The random scission enzymes in the amorphous domains create new chain ends that are subsequently bound and depolymerized by processive enzymes. Present studies further highlight the importance to consider how the host polymer's morphologies affect the reactions catalyzed by embedded catalytic species.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Quality Research in Radiation Oncology Analysis of Clinical Performance Measures in the Management of Gastric Cancer

Background: The specific aim was to determine national patterns of radiation therapy (RT) practice in patients treated for stage IB-IV (nonmetastatic) gastric cancer (GC). Methods and Materials: A national process survey of randomly selected US RT facilities was conducted which retrospectively assessed demographics, staging, geographic region, practice setting, and treatment by using on-site record review of eligible GC cases treated from 2005 to 2007. Three clinical performance measures (CPMs), (1) use of computed tomography (CT)-based treatment planning; (2) use of dose volume histograms (DVHs) to evaluate RT dose to the kidneys and liver; and (3) completion of RT within the prescribed time frame; and emerging quality indicators, (i) use of intensity modulated RT (IMRT); (ii) use of image-guided tools (IGRT) other than CT for RT target delineation; and (iii) use of preoperative RT, were assessed. Results: CPMs were computed for 250 eligible patients at 45 institutions (median age, 62 years; 66% male; 60% Caucasian). Using 2000 American Joint Committee on Cancer criteria, 13% of patients were stage I, 29% were stage II, 32% were stage IIIA, 10% were stage IIIB, and 12% were stage IV. Most patients (43%) were treated at academic centers, 32% were treated at large nonacademic centers, and 25% were treated at small to medium sized facilities. Almost all patients (99.5%) underwent CT-based planning, and 75% had DVHs to evaluate normal tissue doses to the kidneys and liver. Seventy percent of patients completed RT within the prescribed time frame. IMRT and IGRT were used in 22% and 17% of patients, respectively. IGRT techniques included positron emission tomography (n=20), magnetic resonance imaging (n=1), respiratory gating and 4-dimensional CT (n=22), and on-board imaging (n=10). Nineteen percent of patients received preoperative RT. Conclusions: This analysis of radiation practice patterns for treating nonmetastatic GC indicates widespread adoption of CT-based planning with use of DVH to evaluate normal tissue doses. Most patients completed adjuvant RT in the prescribed time frame. IMRT and IGRT were not routinely incorporated into clinical practice during the 2005-2007 period. These data will be a benchmark for future Quality Research in Radiation Oncology GC surveys.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Microstructure and crystallographic texture of high frequency electric resistance welded X65 pipeline steel

Here, microstructure and crystallographic texture and their correlation with impact toughness of high-frequency electric resistance welded (HF-ERW) X65 Grade pipeline steel have been investigated. X65 base metal after HF-ERW has formed a typical hourglass-shaped weld joint with four different regions, including the thermomechanical-affected zone (TMAZ), fine-grained heat affected zone (FGHAZ), coarse-grained heat affected zone (CGHAZ), and bondline (Weld Zone). X65 base metal shows prominent (113) [110] and (112) [110] texture components due to thermomechanical-controlled processing. TMAZ depicted random texture with a minor intensity of Cube component (001)[110] due to the partial recrystallization during welding. In the FGHAZ and CGHAZ, near the bondline, the base metal texture has been converted to rotated Goss (110) [110], Goss (110) [001], and rotated cube (001) [110] due to the recrystallization of the austenite and the shear deformation in the austenite temperature range. A high density of {100} and {110} cleavage planes is observed in the CGHAZ, which is often found to be the fracture location for the impact toughness testing.

36 MATERIALS SCIENCE↗
DOE OSTI2017

A pseudo-thermodynamic description of dispersion for nanocomposites

Dispersion in polymer nanocomposites is determined by the kinetics of mixing and chemical affinity. Compounds like reinforcing filler/elastomer blends display some similarity to colloidal solutions in that the filler particles are close to randomly dispersed through processing. It is attractive to apply a pseudo-thermodynamic approach taking advantage of this analogy between the kinetics of mixing for polymer compounds and thermally driven dispersion for colloids. In order to demonstrate this pseudo-thermodynamic approach, two polybutadienes and one polyisoprene were milled with three carbon blacks and two silicas. These samples were examined using small-angle x-ray scattering as a function of filler concentration to determine a pseudo-second order virial coefficient, A 2 , which is used as an indicator for compatibility of the filler and polymer. It is found that A 2 follows the expected behavior with lower values for smaller primary particles indicating that smaller particles are less compatible and more difficult to mix. The measured values of A 2 can be used to specify repulsive interaction potentials for coarse grain DPD simulations of filler/elastomer systems. In addition, new methods to quantify the filler percolation threshold and filler mesh size as a function of filler concentration are obtained. Moreover, the results represent a new approach to understanding and predicting compatibility in polymer nanocomposites based on a pseudo-thermodynamic approach.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Exploring the potential of using L-Band InSAR for the mapping of flooded vegetation in tropical wetlands

Wetlands play a critical role in global water and carbon cycles, yet monitoring their water extent remains difficult, particularly beneath dense vegetation. SAR-based techniques such as backscatter thresholding are limited by complex scattering mechanisms, while fully polarimetric SAR (PolSAR) data capable of detecting doublebounce scattering remain scarce. To address these challenges, this study evaluates the potential of Interferometric SAR (InSAR) for mapping water surfaces beneath vegetation, termed flooded vegetation, using the Atrato floodplain in Colombia as a case study. We develop an automated workflow combining InSAR fringe detection with local phase homogeneity analysis and random sampling of processing parameters to generate probabilistic flooded vegetation maps. Applied to ALOS PALSAR-1 L-band image pairs from 2007–2011, the workflow captures seasonal fluctuations in flooded extent ranging from 500 to 1,500 km2. Compared to other L-band SAR inundation products, the InSAR-based maps identify broader flooded areas, with ~70% agreement in pairwise comparisons. Around 84% of detections align with existing wetland inventories and seasonal changes correspond with regional hydrological indicators, including terrestrial water storage anomalies and water gauge measurements. PolSAR analysis shows that InSAR complements backscatter-based methods by detecting inundation in areas with weak double-bounce signals. These findings suggest that combining InSAR with backscatter-based methods can improve detection of flooded vegetation, which is especially relevant for the upcoming NISAR mission that will offer frequent global L-band observations.

Coastal inundation↗
DOE OSTI2025

Control over Banded Morphologies and Circular Dichroism in Chiral Halide Perovskites

Chiral halide perovskites (c-HPs) merge the chirality of organic cations with the semiconducting properties of metal halide frameworks, creating a family of chiral semiconductors with tunable chiroptoelectronic behavior. Here, we describe the impact of periodic banded morphologies of textured c-HP ( R/S -NEA) 2 PbBr 4 films (NEA = 1- (1-naphthyl)ethylammonium) on their chiroptical behavior. Due to the interplay between the crystalline and glassy phases, the c-HP film growth is driven by rhythmic precipitation, producing a distinctive controllable radial banded pattern with the ( R/S -NEA) 2 PbBr 4 inorganic planes oriented parallel to the substrate. The banded morphology can be controlled, as evidenced by the growth temperature dictating the ridge-to-ridge spacing as well as the density of banded regions. The resulting circular dichroism (CD) spectral shape, intensity, and polarity vary in a seemingly random manner across processing conditions. However, these spectral features can be explained by considering key features of the banded morphology, such as refraction of the incident light due to surface morphology, birefringence, and stacked, rotated crystallites. These effects cannot be canceled by averaging front and back CD spectra of c-HP films, and our model incorporating these effects reproduces all observed CD spectra remarkably well. The control over the c-HP morphology and prediction capabilities of our CD modeling leads to further understanding of this class of semiconductors and the possibility of exploiting structural features for light polarization control akin to enhanced metamaterials.

14 SOLAR ENERGY↗
DOE OSTI2018

Recombination activity of grain boundaries in high-performance multicrystalline Si during solar cell processing

Here in this work, we applied internal quantum efficiency mapping to study the recombination activity of grain boundaries in High Performance Multicrystalline Silicon under different processing conditions. Wafers were divided into groups and underwent different thermal processing, consisting of phosphorus diffusion gettering and surface passivation with hydrogen rich layers. After these thermal treatments, wafers were processed into heterojunction with intrinsic thin layer solar cells. Light Beam Induced Current and Electron Backscatter Diffraction were applied to analyse the influence of thermal treatment during standard solar cell processing on different types of grain boundaries. The results show that after cell processing, most random-angle grain boundaries in the material are well passivated, but small-angle grain boundaries are not well passivated. Special cases of coincidence site lattice grain boundaries with high recombination activity are also found. Based on micro-X-ray fluorescence measurements, a change in the contamination level is suggested as the reason behind their increased activity. Published by AIP Publishing.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Nonequilibrium formulation of varying-temperature bit erasure

Landauer's principle states that erasing a bit of information at fixed temperature T costs at least $k$ B $T$ ln 2 units of work. Here we investigate erasure at varying temperature, to which Landauer's result does not apply. Here we formulate bit erasure as a stochastic nonequilibrium process involving a compression of configuration space, with physical and logical states associated in a symmetric way. Erasure starts and ends at temperature T, but temperature can otherwise vary with time in an arbitrary way. Defined in this way, erasure is governed by a set of nonequilibrium fluctuation relations that show that varying-temperature erasure can done with less work than $k$ B $T$ ln 2. As a result, erasure and the complementary process of bit randomization can be combined to form a work-producing engine cycle.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Machine learning for seismic low-frequency extrapolation

The cycle-skipping problem that plagues full waveform inversion (FWI) can be at least partially mitigated if low frequencies (which encode the kinematics of wave propagation in seismic data) are recorded. However, seismic sources and receivers are band-limited, so seismic data does not generally include signals down to 0 Hz. To improve our ability to solve the seismic inverse problem, one can synthesize this missing low-frequency (LF) content from the recorded high-frequency (HF) data using machine learning (ML) models. Deep learning models such as convolutional neural networks (CNNs) demonstrate impressive ability to perform low frequency extrapolation. However, such models require powerful hardware (GPU machines) and careful training. We assess the extrapolation capabilities of three different ML models that do not require GPU machines, namely, random forest, Gaussian process regression and gradient boosting, on both synthetic and real data. Experimental results on two synthetic data sets (generated from a low velocity lens embedded in a homogeneous medium, and the Marmousi model) demonstrate that FWI applied to the extrapolated data consistently improves inversion accuracy relative to FWI applied to the original data sets that do not contain low frequencies. Application of low-frequency extrapolation to real data from the Northwest Shelf of Australia demonstrates that tree-based ML models such as gradient boosting can outperform CNNs in terms of both accuracy and computational cost on non-GPU architectures.

58 GEOSCIENCES↗
DOE OSTI2019

The ASAS-SN catalogue of variable stars – IV. Periodic variables in the APOGEE survey

ABSTRACT We explore the synergy between photometric and spectroscopic surveys by searching for periodic variable stars among the targets observed by the Apache Point Observatory Galactic Evolution Experiment (APOGEE) using photometry from the All-Sky Automated Survey for Supernovae (ASAS-SN). We identified 1924 periodic variables among more than $258\, 000$ APOGEE targets; 465 are new discoveries. We homogeneously classified 430 eclipsing and ellipsoidal binaries, 139 classical pulsators (Cepheids, RR Lyrae, and δ Scuti), 719 long-period variables (pulsating red giants), and 636 rotational variables. The search was performed using both visual inspection and machine learning techniques. The light curves were also modelled with the damped random walk stochastic process. We find that the median [Fe/H] of variable objects is lower by 0.3 dex than that of the overall APOGEE sample. Eclipsing binaries and ellipsoidal variables are shifted to a lower median [Fe/H] by 0.2 dex. Eclipsing binaries and rotational variables exhibit significantly broader spectral lines than the rest of the sample. We make ASAS-SN light curves for all the APOGEE stars publicly available and provide parameters for the variable objects.

Pawlak, Michał↗
DOE OSTI2024

Cylinders’ percolation: Decoupling and applications

In this paper we establish a strong decoupling inequality for the cylinder’s percolation process introduced by Tykesson and Windisch (Probab. Theory Related Fields 154 (2012) 165–191). This model features a very strong dependency structure, making it difficult to study, and this is why such decoupling inequalities are desirable. It is important to notice that the type of dependencies featured by cylinder’s percolation is particularly intricate, given that the cylinders have infinite range (unlike some models like Boolean percolation) while at the same time being rigid bodies (unlike processes such as random interlacements). Here our work introduces a new notion of fast decoupling, proves that it holds for the model in question and finishes with an application. More precisely, we prove that for a small enough density of cylinders, a random walk on a connected component of the vacant set is transient for all dimensions d≥3.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Hierarchical effects facilitate spreading processes on synthetic and empirical multilayer networks

In this paper we consider the effects of corporate hierarchies on innovation spread across multilayer networks, modeled by an elaborated SIR framework. We show that the addition of management layers can significantly improve spreading processes on both random geometric graphs and empirical corporate networks. Additionally, we show that utilizing a more centralized working relationship network rather than a strict administrative network further increases overall innovation reach. In fact, this more centralized structure in conjunction with management layers is essential to both reaching a plurality of nodes and creating a stable adopted community in the long time horizon. Further, we show that the selection of seed nodes affects the final stability of the adopted community, and while the most influential nodes often produce the highest peak adoption, this is not always the case. In some circumstances, seeding nodes near but not in the highest positions in the graph produces larger peak adoption and more stable long-time adoption.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Water Security Toolkit User Manual Version 1.2.

The Water Security Toolkit (WST) is a suite of open source software tools that can be used by water utilities to create response strategies to reduce the impact of contamination in a water distribution network . WST includes hydraulic and water quality modeling software , optimizati on methodologies , and visualization tools to identify: (1) sensor locations to detect contamination, (2) locations in the network in which the contamination was introduced, (3) hydrants to remove contaminated water from the distribution system, (4) locations in the network to inject decontamination agents to inactivate, remove, or destroy contaminants, (5) locations in the network to take grab sample s to help identify the source of contamination and (6) valves to close in order to isolate contaminate d areas of the network. This user manual describes the different components of WST , along w ith examples and case studies. License Notice The Water Security Toolkit (WST) v.1.2 Copyright c 2012 Sandia Corporation. Under the terms of Contract DE-AC04-94AL85000, there is a non-exclusive license for use of this work by or on behalf of the U.S. government. This software is distributed under the Revised BSD License (see below). In addition, WST leverages a variety of third-party software packages, which have separate licensing policies: Acro Revised BSD License argparse Python Software Foundation License Boost Boost Software License Coopr Revised BSD License Coverage BSD License Distribute Python Software Foundation License / Zope Public License EPANET Public Domain EPANET-ERD Revised BSD License EPANET-MSX GNU Lesser General Public License (LGPL) v.3 gcovr Revised BSD License GRASP AT&T Commercial License for noncommercial use; includes randomsample and sideconstraints executable files LZMA SDK Public Domain nose GNU Lesser General Public License (LGPL) v.2.1 ordereddict MIT License pip MIT License PLY BSD License PyEPANET Revised BSD License Pyro MIT License PyUtilib Revised BSD License PyYAML MIT License runpy2 Python Software Foundation License setuptools Python Software Foundation License / Zope Public License six MIT License TinyXML zlib License unittest2 BSD License Utilib Revised BSD License virtualenv MIT License Vol Common Public License vpykit Revised BSD License Additionally, some precompiled WST binary distributions might bundle other third-party executables files: Coliny Revised BSD License (part of Acro project) Dakota GNU Lesser General Public License (LGPL) v.2.1 PICO Revised BSD License (part of Acro project) i Revised BSD License Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of Sandia National Laboratories nor Sandia Corporation nor the names of its con- tributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IM- PLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL SANDIA CORPORATION BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUD- ING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ii Acknowledgements This work was supported by the U.S. Environmental Protection Agency through its Office of Research and Development (Interagency Agreement # DW8992192801). The material in this document has been subject to technical and policy review by the U.S. EPA, and approved for publication. The views expressed by individual authors, however, are their own, and do not necessarily reflect those of the U.S. Environmental Protection Agency. Mention of trade names, products, or services does not convey official U.S. EPA approval, endorsement, or recommendation. The Water Security Toolkit is an extension of the Threat Ensemble Vulnerability Assessment-Sensor Place- ment Optimization Tool (TEVA-SPOT), which was also developed with funding from the U.S. Environ- mental Protection Agency through its Office of Research and Development (Interagency Agreement # DW8992192801). The authors acknowledge the following individuals for their contributions to the devel- opment of TEVA-SPOT: Jonathan Berry (Sandia National Laboratories), Erik Boman (Sandia National Laboratories), Lee Ann Riesen (Sandia National Laboratories), James Uber (University of Cincinnati), and Jean-Paul Watson (Sandia National Laboratories). iii Acronyms ATUS American Time-Use Survey BLAS Basic linear algebra sub-routines CFU Colony-forming unit CVAR Conditional value at risk CWS Contamination warning system EA Evolutionary algorithm EDS Event detection system EPA U.S. Environmental Protection Agency EC Extent of Contamination ERD EPANET results database file GLPK GNU Linear Programming Kit GRASP Greedy randomized adaptive sampling process HEX Hexadecimal HTML HyperText markup language INP EPANET input file LP Linear program MC Mass consumed MILP Mixed integer linear program MIP Mixed integer program MSX Multi-species extension for EPANET NFD Number of failed detections NS Number of sensors NZD Non-zero demand PD Population dosed PE Population exposed PK Population killed TAI Threat assessment input file TCE Tailed-conditioned expectation TD Time to detection TEC Timed extent of contamination TEVA Threat ensemble vulnerability assessment TSB Tryptic soy broth TSG Threat scenario generation file TSI Threat simulation input file VAR Value at risk VC Volume consumed WST Water Security Toolkit YML YAML configuration file format for WST iv Symbols Notation Definition Example { , } set brackets { 1,2,3 } means a set containing the values 1,2, and 3. [?] is an element of s [?] S means that s is an element of the set S . [?] for all s = 1 [?] s [?] S means that the statement s = 1 is true for all s in set S . P summation P n i =1 s i means s 1 + s 2 + * * * + s n . \ set minus S \ T means the set that contains all those elements of S that are not in set T . %7C given %7C is used to define conditional probability. P ( s %7C t ) means the prob- ability of s occurring given that t occurs. %7C ... %7C cardinality Cardinality of a set is the number of elements of the set. If set S = { 2,4,6 } , then %7C S %7C = 3. v

Klise, Katherine A.↗