This report summarizes the current statistical analysis capability of OVIS and how it works in conjunction with the OVIS data readers and interpolators. It also documents how to extend these capabilities. OVIS is a tool for parallel statistical analysis of sensor data to improve system reliability. Parallelism is achieved using a distributed data model: many sensors on similar components (metaphorically sheep) insert measurements into a series of databases on computers reserved for analyzing the measurements (metaphorically shepherds). Each shepherd node then processes the sheep data stored locally and the results are aggregated across all shepherds. OVIS uses the Visualization Tool Kit (VTK) statistics algorithm class hierarchy to perform analysis of each process's data but avoids VTK's model aggregation stage which uses the Message Passing Interface (MPI); this is because if a single process in an MPI job fails, the entire job will fail. Instead, OVIS uses asynchronous database replication to aggregate statistical models. OVIS has several additional features beyond those present in VTK that, first, accommodate its particular data format and, second, improve the memory and speed of the statistical analyses. First, because many statistical algorithms are multivariate in nature and sensor data is typically univariate, interpolation of data is required to provide simultaneous observations of metrics. Note that in this report, we will refer to a single value obtained from a sensor as a measurement while a collection of multiple sensor values simultaneously present in the system is an observation. A base class for interpolation is provided that abstracts the operation of converting multiple sensor measurements into simultaneous observations. A concrete implementation is provided that performs piecewise constant temporal interpolation of multiple metrics across a single component. Secondly, because calculations may summarize data too large to fit in memory OVIS analyses batches of observations at a time and aggregates these intermediate intra-process models as it goes before storing the final model for inter-process aggregation via database replication. This reduces the memory footprint of the analysis, interpolation, and the database client and server query processing. This also interleaves processing with the disk I/O required to fetch data from the database - also improving speed. This report documents how OVIS performs analyses and how to create additional analysis components that fetch measurements from the database, perform interpolation, or perform operations on streamed observations (such as model updates or assessments). The rest of this section outlines the OVIS analysis algorithm and is followed by sections specific to each subtask. Note that we are limiting our discussion for now to the creation of a model from a set of measurements, and not including the assessment of observations using a model. The same framework can be used for assessment but that use case is not detailed in this report.
The observation and characterization of a single atom system in silicon is a significant landmark in half a century of device miniaturization, and presents an important new laboratory for fundamental quantum and atomic physics. We compare with multi-million atom tight binding (TB) calculations the measurements of the spectrum of a single two-electron (2e) atom system in silicon - a negatively charged (D-) gated Arsenic donor in a FinFET. The TB method captures accurate single electron eigenstates of the device taking into account device geometry, donor potentials, applied fields, interfaces, and the full host bandstructure. In a previous work, the depths and fields of As donors in six device samples were established through excited state spectroscopy of the D0 electron and comparison with TB calculations. Using self-consistent field (SCF) TB, we computed the charging energies of the D- electron for the same six device samples, and found good agreement with the measurements. Although a bulk donor has only a bound singlet ground state and a charging energy of about 40 meV, calculations show that a gated donor near an interface can have a reduced charging energy and bound excited states in the D- spectrum. Measurements indeed reveal reduced charging energies and bound 2e excited states, at least one of which is a triplet. The calculations also show the influence of the host valley physics in the two-electron spectrum of the donor.
This paper proposes a definition of 'IA and IA-enabled products' based on threat, as opposed to 'security services' (i.e., 'confidentiality, authentication, integrity, access control or non-repudiation of data'), as provided by Department of Defense (DoD) Instruction 8500.2, 'Information Assurance (IA) Implementation.' The DoDI 8500.2 definition is too broad, making it difficult to distinguish products that need higher protection from those that do not. As a consequence the products that need higher protection do not receive it, increasing risk. The threat-based definition proposed in this paper solves those problems by focusing attention on threats, thereby moving beyond compliance to risk management. (DoDI 8500.2 provides the definitions and controls that form the basis for IA across the DoD.) Familiarity with 8500.2 is assumed.
This paper presents experimental results for two fuel-related topics in a diesel engine: (1) how fuel volatility affects the premixed burn and heat release rate, and (2) how ignition quality influences the soot formation. Fast evaporation of fuel may lead to more intense heat release if a higher percentage of the fuel is mixed with air to form a combustible mixture. However, if the evaporation of fuel is driven by mixing with high-temperature gases from the ambient, a high-volatility fuel will require less oxygen entrainment and mixing for complete vaporization and, consequently, may not have potential for significant heat release simply because it has vaporized. Fuel cetane number changes also cause uncertainty regarding soot formation because variable ignition delay will change levels of fuel-air mixing prior to combustion. To address these questions, experiments are performed using a constant-volume combustion chamber simulating typical low-temperature-combustion (LTC) diesel conditions. We use fuels that have the same ignition delay (and therefore similar time for premixing with air), but different fuel volatility, to assess the heat-release rate and spatial location of combustion. Under this condition, where fuel volatility is decoupled from the ignition delay, results show almost the same heat release rate and spatial location of the premixed burn. The effect of ignition quality on soot formation has also been studied while maintaining similar levels of fuel-ambient mixing prior to combustion. To achieve the same ignition delay, the high-cetane-number fuel is injected into an ambient gas at a lower temperature and vice versa. The total soot mass within the spray is measured and compared for fuels with different cetane numbers but with the same premixing level (e.g. the same ignition delay and lift-off length). Experimental results show that the combination of high cetane number and low ambient gas temperature produces lower soot than the other combination, because the ambient temperature predominantly affects soot formation.
The research described in this report developed the theoretical and conceptual framework for understanding, recognizing, and anticipating the origins, dynamic mechanisms, perceptions, and social structures of Islamic social reform movements in the Muslim homeland and in diaspora communities. This research has revealed valuable insights into the dynamic mechanisms associated with reform movements and, as such, offers the potential to provide indications and warnings of impending violence. This study produced the following significant findings: (1) A framework for understanding Islamic radicalization in the context of Social Movement Theory was developed and implemented. This framework provides a causal structure for the interrelationships among the myriad features of a social movement. (2) The degree to which movement-related activity shows early diffusion across multiple social contexts is a powerful distinguisher of successful and unsuccessful social movements. Indeed, this measurable appears to have significantly more predictive power than volume of such activity and also more power than various system intrinsics. (3) Significant social movements can occur only if both the intra-context 'infectivity' of the movement exceeds a certain threshold and the inter-context interactions associated with the movement occur with a frequency that is larger than another threshold. Note that this is reminiscent of, and significantly extends, well-known results for epidemic thresholds in disease propagation models. (4) More in-depth content analysis of blogs through the lens of Argumentation Theory has the potential to reveal new insights into radicalization in the context of Social Movement Theory. This connection has the potential to be of value from two important perspectives - first, this connection has the potential to provide more in depth insights into the forces underlying the emergence of radical behavior and second, this connection may provide insights into how to use the blogosphere to influence the emergent dialog to effectively impact the resulting actions taken by the potential radicals. The authors of this report recognize that Islamic communities are not the only source of radicalism; indeed many other groups, religious and otherwise, have used and continue to use, radicalism to achieve their ends. Further, the authors also recognize that not all Muslims use, or condone the use of, radical behavior. Indeed, only a very small segment of the Muslim communities throughout the world use and/or support such behavior. Nevertheless, the focus of this research is, indeed, on understanding, recognizing, and anticipating the origins, dynamic mechanisms, perceptions, and social structures of Islamic radicalism.
Education and training are the foundation for a state's development and maintenance of an indigenous capability to conduct a nuclear energy and research program, from both the regulatory perspective and the licensee or operator perspective. The International Training Course on the Physical Protection of Nuclear Facilities and Materials (ITC) is the original international training program in the area of physical protection of nuclear material, which the United States has been conducting since 1978. This course focuses on a systems engineering performance-based approach to requirements definition, design, and evaluation for physical protection systems. During the first twenty-one presentations of ITC, more than 600 national experts from more than sixty International Atomic Energy Agency member states were trained. This paper describes the content, structure, and process of ITC.
Carbon-manganese steels are candidates for the structural materials in hydrogen gas pipelines, however it is well known that these steels are susceptible to hydrogen embrittlement. Decades of research and industrial experience have established that hydrogen embrittlement compromises the structural integrity of steel components. This experience has also helped identify the failure modes that can operate in hydrogen containment structures. As a result, there are tangible ideas for managing hydrogen embrittement in steels and quantifying safety margins for steel hydrogen containment structures. For example, fatigue crack growth aided by hydrogen embrittlement is a key failure mode for steel hydrogen containment structures subjected to pressure cycling. Applying appropriate structural integrity models coupled with measurement of relevant material properties allows quantification of safety margins against fatigue crack growth in hydrogen containment structures. Furthermore, application of these structural integrity models is aided by the development of micromechanics models, which provide important insights such as the hydrogen distribution near defects in steel structures. The principal objective of this project is to enable application of structural integrity models to steel hydrogen pipelines. The new American Society of Mechanical Engineers (ASME) B31.12 design code for hydrogen pipelines includes a fracture mechanics-based design option, which requires material property inputs such as the threshold for rapid cracking and fatigue crack growth rate under cyclic loading. Thus, one focus of this project is to measure the rapid-cracking thresholds and fatigue crack growth rates of line pipe steels in high-pressure hydrogen gas. These properties must be measured for the base materials but more importantly for the welds, which are likely to be most vulnerable to hydrogen embrittlement. The measured properties can be evaluated by predicting the performance of the pipeline using a relevant structural integrity model, such as that in ASME B31.12. A second objective of this project is to enable development of micromechanics models of hydrogen embrittlement in pipeline steels. The focus of this effort is to establish physical models of hydrogen embrittlement in line pipe steels using evidence from analytical techniques such as electron microscopy. These physical models then serve as the framework for developing sophisticated finite-element models, which can provide quantitative insight into the micromechanical state near defects. Understanding the micromechanics of defects can ensure that structural integrity models are applied accurately and conservatively.
Sandia National Laboratories, California (SNL/CA) is a government-owned/contractor-operated laboratory. Sandia Corporation, a Lockheed Martin Company, operates the laboratory for the Department of Energy's National Nuclear Security Administration (NNSA). The NNSA Sandia Site Office oversees operations at the site, using Sandia Corporation as a management and operating contractor. This Site Environmental Report for 2009 was prepared in accordance with DOE Order 231.1A (DOE 2004a). The report provides a summary of environmental monitoring information and compliance activities that occurred at SNL/CA during calendar year 2009. General site and environmental program information is also included. The Site Environmental Report is divided into ten chapters. Chapter 1, the Executive Summary, highlights compliance and monitoring results obtained in 2009. Chapter 2 provides a brief introduction to SNL/CA and the existing environment found on site. Chapter 3 summarizes SNL/CA's compliance activities with the major environmental requirements applicable to site operations. Chapter 4 presents information on environmental management, performance measures, and environmental programs. Chapter 5 presents the results of monitoring and surveillance activities in 2009. Chapter 6 discusses quality assurance. Chapters 7 through 9 provide supporting information for the report and Chapter 10 is the report distribution list.
The Advanced Engineering Environment (AEE) project identifies emerging engineering environment tools and assesses their value to Sandia National Laboratories and our partners in the Nuclear Security Enterprise (NSE) by testing them in our design environment. This project accomplished several pilot activities, including: the preliminary definition of an engineering bill of materials (BOM) based product structure in the Windchill PDMLink 9.0 application; an evaluation of Mentor Graphics Data Management System (DMS) application for electrical computer-aided design (ECAD) library administration; and implementation and documentation of a Windchill 9.1 application upgrade. The project also supported the migration of legacy data from existing corporate product lifecycle management systems into new classified and unclassified Windchill PDMLink 9.0 systems. The project included two infrastructure modernization efforts: the replacement of two aging AEE development servers for reliable platforms for ongoing AEE project work; and the replacement of four critical application and license servers that support design and engineering work at the Sandia National Laboratories/California site.
Are your employees unhappy with internal corporate search? Frequent complaints include: too many results to sift through; results are unrelated/outdated; employees aren't sure which terms to search for. One way to improve intranet search is to implement a controlled vocabulary ontology. Employing this takes the guess work out of searching, makes search efficient and precise, educates employees about the lingo used within the corporation, and allows employees to contribute to the corpus of terms. It promotes internal corporate search to rival its superior sibling, internet search. We will cover our experiences, lessons learned, and conclusions from implementing a controlled vocabulary ontology at Sandia National Laboratories. The work focuses on construction of this ontology from the content perspective and the technical perspective. We'll discuss the following: (1) The tool we used to build a polyhierarchical taxonomy; (2) Examples of two methods of indexing the content: traditional 'back of the book' and folksonomy word-mapping; (3) Tips on how to build future search capabilities while building the basic controlled vocabulary; (4) How to implement the controlled vocabulary as an ontology that mimics Google's search suggestions; (5) Making the user experience more interactive and intuitive; and (6) Sorting suggestions based on preferred, alternate and related terms using SPARQL queries. In summary, future improvements will be presented, including permitting end-users to add, edit and remove terms, and filtering on different subject domains.
A sensitivity study was performed utilizing a three dimensional finite element model to assess allowable cavern field sizes in strategic petroleum reserve salt domes. A potential exists for tensile fracturing and dilatancy damage to salt that can compromise the integrity of a cavern field in situations where high extraction ratios exist. The effects of salt creep rate, depth of salt dome top, dome size, caprock thickness, elastic moduli of caprock and surrounding rock, lateral stress ratio of surrounding rock, cavern size, depth of cavern, and number of caverns are examined numerically. As a result, a correlation table between the parameters and the impact on the performance of a storage field was established. In general, slower salt creep rates, deeper depth of salt dome top, larger elastic moduli of caprock and surrounding rock, and a smaller radius of cavern are better for structural performance of the salt dome.
This paper introduces an effective-media toolset that can be used for the design of metamaterial structures based on metallic components such as split-ring resonators and dipoles, as well as dielectric spherical resonators. For demonstration purposes the toolset will be used to generate infrared metamaterial designs, and the predicted performances will be verified with full-wave numerical simulations.
Chemically reacting flow models generally involve inputs and parameters that are determined from empirical measurements, and therefore exhibit a certain degree of uncertainty. Estimating the propagation of this uncertainty into computational model output predictions is crucial for purposes of reacting flow model validation, model exploration, as well as design optimization. Recent years have seen great developments in probabilistic methods and tools for efficient uncertainty quantification (UQ) in computational models. These tools are grounded in the use of Polynomial Chaos (PC) expansions for representation of random variables. The utility and effectiveness of PC methods have been demonstrated in a range of physical models, including structural mechanics, transport in porous media, fluid dynamics, aeronautics, heat transfer, and chemically reacting flow. While high-dimensionality remains nominally an ongoing challenge, great strides have been made in dealing with moderate dimensionality along with non-linearity and oscillatory dynamics. In this talk, I will give an overview of UQ in chemical systems. I will cover both: (1) the estimation of uncertain input parameters from empirical data, and (2) the forward propagation of parametric uncertainty to model outputs. I will cover the basics of forward PC UQ methods with examples of their use. I will also highlight the need for accurate estimation of the joint probability density over the uncertain parameters, in order to arrive at meaningful estimates of model output uncertainties. Finally, I will discuss recent developments on the inference of this density given partial information from legacy experiments, in the absence of raw data.
SIRHEN (Sandia InfraRed HEtrodyne aNalysis) is a program for reducing data from photonic Doppler velocimetry (PDV) measurements. SIRHEN uses the short-time Fourier transform method to extract velocity information. The program can be run in MATLAB (2008b or later) or as a Windows executable. This report describes the new Sandia InfraRed HEtrodyne aNalysis program (SIRHEN; pronounced 'siren') that has been developed for efficient and robust analysis of PDV data. The program was designed for easy use within Sandia's dynamic compression community.
To maintain effective containment surveillance (CS) system capabilities, the requirements for such systems must continue to evolve outpacing diversion capabilities, reduce costs, and meet the needs of the looming nuclear renaissance. What are the future sensor-based capabilities that must be available to support growing CS requirements and what are the technologies needed to provide the underlying capabilities? This presentation is intended to discuss the present gaps in sensor-based containment and surveillance relevant technologies, and future development trends which may address these gaps. Consumer driven technology development will represent a component rich source of technologies and devices that can find application in containment and surveillance tools helping to minimize the technology gaps. Recognizing and utilizing these sources is paramount to cost effective solutions. Where these gaps cannot be addressed by consumer based development, custom, CS specific approaches are the only solution.
Discontinuity detection is an important component in many fields: Image recognition, Digital signal processing, and Climate change research. Current methods shortcomings are: Restricted to one- or two-dimensional setting, Require uniformly spaced and/or dense input data, and Give deterministic answers without quantifying the uncertainty. Spectral methods for Uncertainty Quantification with global, smooth bases are challenged by discontinuities in model simulation results. Domain decomposition reduces the impact of nonlinearities and discontinuities. However, while gaining more smoothness in each subdomain, the current domain refinement methods require prohibitively many simulations. Therefore, detecting discontinuities up front and refining accordingly provides huge improvement to the current methodologies.
It is known that, in general, the correlation structure in the joint distribution of model parameters is critical to the uncertainty analysis of that model. Very often, however, studies in the literature only report nominal values for parameters inferred from data, along with confidence intervals for these parameters, but no details on the correlation or full joint distribution of these parameters. When neither posterior nor data are available, but only summary statistics such as nominal values and confidence intervals, a joint PDF must be chosen. Given the summary statistics it may not be reasonable nor necessary to assume the parameters are independent random variables. We demonstrate, using a Bayesian inference procedure, how to construct a posterior density for the parameters exhibiting self consistent correlations, in the absence of data, given (1) the fit-model, (2) nominal parameter values, (3) bounds on the parameters, and (4) a postulated statistical model, around the fit-model, for the missing data. Our approach ensures external Bayesian updating while marginalizing over possible data realizations. We then address the matching of given parameter bounds through the choice of hyperparameters, which are introduced in postulating the statistical model, but are not given nominal values. We discuss some possible approaches, including (1) inferring them in a separate Bayesian inference loop and (2) optimization. We also perform an empirical evaluation of the algorithm showing the posterior obtained with this data free inference compares well with the true posterior obtained from inference against the full data set.
The U.S. Department of Energy's (DOE) National Nuclear Security Administration (NNSA) established the Global Threat Reduction Initiative's (GTRI) mission to reduce and protect nuclear and radiological materials located at civilian sites worldwide. Internationally, over 80 countries are cooperating with GTRI to enhance security of facilities with these materials. In 2004, a GTRI delegation began working with the Tanzania Atomic Energy Commission, (TAEC). The team conducted site assessments for the physical protection of radiological materials in Tanzania. Today, GTRI and the Government of Tanzania continue cooperative efforts to enhance physical security at several radiological sites, including a central sealed-source storage facility, and sites in the cities of Arusha, Dar Es Salaam, and Tanga. This paper describes the scope of physical protection work, lessons learned, and plans for future cooperation between the GTRI program and the TAEC. Additionally the paper will review the cooperative efforts between TAEC and the International Atomic Energy Agency (IAEA) with regards to a remote monitoring system at a storage facility and to the repackaging of radioactive sources.
Automated processing, modeling, and analysis of unstructured text (news documents, web content, journal articles, etc.) is a key task in many data analysis and decision making applications. As data sizes grow, scalability is essential for deep analysis. In many cases, documents are modeled as term or feature vectors and latent semantic analysis (LSA) is used to model latent, or hidden, relationships between documents and terms appearing in those documents. LSA supplies conceptual organization and analysis of document collections by modeling high-dimension feature vectors in many fewer dimensions. While past work on the scalability of LSA modeling has focused on the SVD, the goal of our work is to investigate the use of distributed memory architectures for the entire text analysis process, from data ingestion to semantic modeling and analysis. ParaText is a set of software components for distributed processing, modeling, and analysis of unstructured text. The ParaText source code is available under a BSD license, as an integral part of the Titan toolkit. ParaText components are chained-together into data-parallel pipelines that are replicated across processes on distributed-memory architectures. Individual components can be replaced or rewired to explore different computational strategies and implement new functionality. ParaText functionality can be embedded in applications on any platform using the native C++ API, Python, or Java. The ParaText MPI Process provides a 'generic' text analysis pipeline in a command-line executable that can be used for many serial and parallel analysis tasks. ParaText can also be deployed as a web service accessible via a RESTful (HTTP) API. In the web service configuration, any client can access the functionality provided by ParaText using commodity protocols ... from standard web browsers to custom clients written in any language.
The Integrated Modeling, Mapping, and Simulation (IMMS) program is designing and prototyping a simulation and collaboration environment for linking together existing and future modeling and simulation tools to enable analysts, emergency planners, and incident managers to more effectively, economically, and rapidly prepare, analyze, train, and respond to real or potential incidents. When complete, the IMMS program will demonstrate an integrated modeling and simulation capability that supports emergency managers and responders with (1) conducting 'what-if' analyses and exercises to address preparedness, analysis, training, operations, and lessons learned, and (2) effectively, economically, and rapidly verifying response tactics, plans and procedures.
Researchers at Sandia National Laboratories in Livermore, California are creating what is in effect a vast digital petridish able to hold one million operating systems at once in an effort to study the behavior of rogue programs known as botnets. Botnets are used extensively by malicious computer hackers to steal computing power fron Internet-connected computers. The hackers harness the stolen resources into a scattered but powerful computer that can be used to send spam, execute phishing, scams or steal digital information. These remote-controlled 'distributed computers' are difficult to observe and track. Botnets may take over parts of tens of thousands or in some cases even millions of computers, making them among the world's most powerful computers for some applications.
While RAID is the prevailing method of creating reliable secondary storage infrastructure, many users desire more flexibility than offered by current implementations. To attain needed performance, customers have often sought after hardware-based RAID solutions. This talk describes a RAID system that offloads erasure correction coding calculations to GPUs, allowing increased reliability by supporting new RAID levels while maintaining high performance.
Materials with switchable states are desirable in many areas of science and technology. The ability to thermally transform a dielectric material to a conductive state should allow for the creation of electronics with built-in safety features. Specifically, the non-desirable build-up and discharge of electricity in the event of a fire or over-heating would be averted by utilizing thermo-switchable dielectrics in the capacitors of electrical devices (preventing the capacitors from charging at elevated temperatures). We have designed a series of polymers that effectively switch from a non-conductive to a conductive state. The thermal transition is governed by the stability of the leaving group after it leaves as a free entity. Here, we present the synthesis and characterization of a series of precursor polymers that eliminate to form poly(p-phenylene vinylene) (PPV's).