This paper analyzes the effect of wind turbine integration (WT) on the inter-area oscillation mode of a test two-area power system. The paper uses a root-locus based design method to propose a pair of controllers to provide damping to the inter-area mode of the system. The controllers are selected from the best combination of feedback signal and WT control action. One of the controllers uses the active power control part of the WT while the other uses the reactive power part. The paper analyzes the impact that increases on the transmission line connecting the WT to the system have on the controllers' performance. Time domain simulations are provided to evaluate the effectiveness of the controllers under different conditions.
Montmorillonite with an empirical formula of Na0.2Ca0.1Al2Si4O10(OH)2(H2O)10 is a di-octahedral smectite. Montmorillonite-rich bentonite is a primary buffer candidate for high level nuclear waste (HLW) and used nuclear fuel to be disposed in mild environments. In such environments, temperatures are expected to be ≤ 90oC, the solutions are of low ionic strengths, and pH is close to neutral. Under the conditions outside the above parameters, the performance of montmorillonite-rich bentonite is deteriorated because of collapse of swelling particles as a result of illitization, and dissolution of the swelling clay minerals followed by precipitation of non-swelling minerals. It has been well known that tri-octahedral smectites such as saponite, with an ideal formula of Mg3(Si, Al)4O10(OH)2•4H2O for an Mg-end member (saponite-15A), are less susceptible to alteration under harsh conditions. Recently, Mg-bearing saponite has been favorably considered as a preferable engineered buffer material for the Swedish very deep holes (VDH) disposal concept in crystalline rock formations. In the VDH, HLW is disposed in deep holes at depth between 2,000 m and 4,000 m. At such deployment depths, the temperatures are expected to be between 100oC and 150oC, and the groundwater is of high ionic strength. The harsh chemical conditions of high pH are also introduced by the repository designs in which concretes and cements are used as plugs and buffers. In addition, harsh chemical conditions introduced by high ionic strength solutions are also present in repository designs in salt formations and sedimentary basins. For instance, the two brines associated with the salt formations for the Waste Isolation Pilot Plant (WIPP) in USA have ionic strengths of 5.82 mol•kg-1 (ERDA-6) and 8.26 mol•kg-1 (GWB). In the Asse site proposed for a geological repository in salt formations in Germany, the Q-brine has an ionic strength of ~13 mol•kg-1. In this work, we present our investigations regarding the stability of saponite under hydrothermal conditions in harsh environments.
This is the official user guide for MUELU multigrid library in Trilinos version 12.13 (Dev). This guide provides an overview of MUELU, its capabilities, and instructions for new users who want to start using MUELU with a minimum of effort. Detailed information is given on how to drive MUELU through its XML interface. Links to more advanced use cases are given. This guide gives information on how to achieve good parallel performance, as well as how to introduce new algorithms Finally, readers will find a comprehensive listing of available MUELU options. Any options not documented in this manual should be considered strictly experimental.
This report describes the 2015-2017 fiscal year research efforts to evaluate high temperature plastics as replacement materials for ceramics in electrical contact assemblies. The main objective of this work was to assess the feasibility of replacing existing high-price ceramic inserts with a polymeric material. Current ceramic parts are expensive due to machining costs and can suffer brittle failure. Therefore, replacing the ceramic with a more cost-effective material — in this case a plastic — is highly desirable. Not only are plastics easier to process, but they can also eliminate final tooling and are less brittle than ceramics. This effort used a three-phase approach: selection of appropriate materials determined by a comprehensive literature review, performance of an initial thermal stability screening, understanding of aging behavior under normal and off-normal conditions, and evaluation of performance at elevated temperatures. Two polymers were determined to meet the desired criteria: polybenzimidazole, and Vespel® SP-1 polyimide. Polymer derived ceramics may also be useful but will require further development of molding capabilities that were beyond the scope of this program.
Two surrogate models are under development to rapidly emulate the effects of the Fuel Matrix Degradation (FMD) model in GDSA Framework. One is a polynomial regression surrogate with linear and quadratic fits, and the other is a k-Nearest Neighbors regressor (kNNr) method that operates on a lookup table. Direct coupling of the FMD model to GDSA Framework is too computationally expensive. Preliminary results indicate these surrogate models will enable GDSA Framework to rapidly simulate spent fuel dissolution for each individual breached spent fuel waste package in a probabilistic repository simulation. This capability will allow uncertainties in spent fuel dissolution to be propagated and sensitivities in FMD inputs to be quantified and ranked against other inputs.
Appropriate waste-forms for radioactive materials must isolate the radionuclides from the environment for long time periods. To accomplish this typically requires low waste-form solubility, to minimize radionuclide release to the environment. However, radiation eventually damages most waste-forms, leading to expansion, crumbling, increased exposed surface area, and faster dissolution. We have evaluated the use of a novel class of materials-ZrW2O8, Zr2P2WO12 and related compounds-that contract upon amorphization. The proposed ceramic waste-forms would consist of zoned grains, or sintered ceramics with center-loaded radionuclides and barren shells. Radiation-induced amorphization would result in core shrinkage but would not fracture the shells or overgrowths, maintaining isolation of the radionuclide. We have synthesized these phases and have evaluated their leach rates. Tungsten forms stable aqueous species at neutral to basic conditions, making it a reliable indicator of phase dissolution. ZrW2O8 leaches rapidly, releasing tungstate while Zr is retained as a solid oxide or hydroxide. Tungsten release rates remain elevated over time and are highly sensitive to contact times, suggesting that this material will not be an effective waste-form. Conversely, tungsten release rates from Zr2P2WO12 rapidly drop and are tied to P release rates; we speculate that a low-solubility protective Zr-phosphate leach layer forms, slowing further dissolution.
American Fuzzy Lop (AFL) is an evolutionary fuzzer that is strategically implemented as a tool for discovering bugs in software during vulnerability research. This work seeks to understand how to best implement AFL on the High-Performance Computing resources available on the unclassified network at Sandia National Laboratories. We investigate various methods of executing AFL, requesting varying numbers of tasks on single compute nodes with 36 physical cores and 72 total threads. A Python script called Blue Claw is presented as an automated testbed generator tool to assist in the tedious process of creating and executing experiments of any scale and duration.
Security at nuclear power plants (NPPs) in the United States is currently based on vital area identification (VAI)-a procedure to determine locations within a nuclear facility that need to be defended from adversaries in order to avoid damage to the facility and/or release of radionuclides to the environment. This procedure heavily leverages a Level 1 probabilistic risk assessment (PRA) which identifies combinations of events that can lead to core damage. Current approaches to VAI for NPPs, however, are determined on a “snapshot-in-time,” and therefore unable to include the time-dependent effects of safety systems within a NPP A novel “leading simulator (LS) / trailing simulator (TS)” methodology is proposed to integrate the thermal hydraulic-based safety analysis of a NPP with a physical security analytical tool to model vital area boundaries and related potential consequences. The methodology will use dynamic event trees to systematically explore the uncertainties in an adversary attack scenario at a hypothetical NPP while incorporating the timing and repair effects that are not captured using the available modeling approaches to physical security practices. Ultimately, the LS/TS methodology will enable NPPs to incorporate the full complement of safety systems and procedures when performing security analyses.
Geotechnical concerns arise due to the close proximity of the some of the caverns to each other (e.g., Caverns 15 and 17) or to the edge of the salt dome (e.g., Cavern 20). There are nine abandoned caverns, one of which collapsed (Cavern 7) in 1954 and another (Cavern 4) which is believed to be in a quasi-stable condition. This report provides explanations for these geotechnical concerns. The structural integrity of the pillar between BC-15 and 17 is examined. No salt fall is expected through 2045. However, the dilatant damaged area increases with time, especially, at the chimney area of BC-17. One drawdown leach for both caverns could be allowed if they are normally operated as a gallery, depressurized simultaneously. The possibility of a loss in integrity of BC-20 is examined in the salt between the dome edge and the cavern. The edge pillar is predicted to have experienced tensile stress since September 1999, but the small tensile stressed area is predicted to disappear in 2018 because BC-20 is filled fully with brine rather than oil since 2/7/2013. Even though BC-20 is no longer used as an SPR cavern, we need to continue monitoring the cavern integrity. BC-4 is also currently filled with brine and will not hold pressure at the wellhead. The cavern extends upward into the caprock and has no effective salt roof The results indicate that any sort of caprock roof collapse for BC-4 is not imminent but salt falls will likely occur from the near-roof portions of the cavern. The uncertainty due to salt falls illustrates the importance of continued monitoring of the area around BC-4 for behavior such as subsidence and tilt which may indicate a change in the cavern's integrity status.
In this study we investigate how an ensemble of disease models can be conditioned to observational data, in a bid to improve its predictive skill. We use the ensemble of influenza forecasting models gathered by the US Centers for Disease Control and Prevention (CDC) as the exemplar. This ensemble is used every year to forecast the annual influenza outbreak in the United States. The models constituting this ensemble draw on very different modeling assumptions and approximations and are a diverse collection of methods to approximate epidemiological dynamics. Currently, each models' predictions are accorded the same importance, or weight, when compiling the ensemble's forecast. We consider this equally-weighted ensemble as the baseline case which has to be improved upon. In this study, we explore whether an ensemble forecast can be improved by "conditioning" the ensemble to whatever observational data is available from the ongoing outbreak. "Conditioning" can imply according the ensemble's members different weights which evolve over time, or simply perform the forecast using the top k (equally-weighted) models. In the latter case, the composition of the "top-k-see of models evolves over time. This is called "model averaging" in statistics. We explore four methods to perform model-averaging, three of which are new. We find that the CDC ensemble responds best to the "top-k-models" approach to model-averaging. All the new MA methods perform better than the baseline equally-weighted ensemble. The four model-averaging methods treat the models as black-boxes and simply use their forecasts as inputs i.e., one does not need access to the models at all, but rather only their forecasts. The model-averaging approaches reviewed in this report thus form a general framework for model-averaging any model ensemble.
Geogenic noble gases are contained in crustal rocks at inter- and intracrystalline sites. In this study, bedded rock salt from southern New Mexico was deformed in a variety of triaxial compression states while measuring the release of naturally contained helium and argon utilizing mass spectrometry. Noble gas release is empirically correlated to volumetric strain and acoustic emissions. At low confining pressures, rock salt deforms primarily by microfracturing, rupturing crystal grains, and releasing helium and argon with a large amount of acoustic emissions, both measured real-time. At higher confining pressure, microfracturing is reduced and the rock salt is presumed to deform more by intracrystalline flow, releasing less amounts of noble gases with fewer acoustic emissions. Our work implies that geogenic gas release during deformation may provide an additional signal which contains information on the type and amount of deformation occurring in a variety of earth systems.
Falling particle receivers (FPRs) are an important component of future falling particle concentrating solar power plants to enable next-generation energy generation. High thermal efficiencies in a FPR are required to high thermodynamic efficiencies of the system. External winds can significantly impact the thermal performance of cavity-type FPRs primarily through changing the air flow in and out of the aperture. A numerical parametric study is performed in this paper to quantify the effect of wind on the thermal performance of a FPR. Wind direction was found to be a significant parameter that can affect the receiver thermal efficiency. The particle mass flow rate did not significantly change the overall effect of wind on the receiver. The receiver efficiency was strong function of the particle diameter, but this was primarily a result of varying curtain opacity with different diameters and not from varying effects with wind. Finally, the model was used to demonstrate that receiver efficiencies of 90% were achievable under the assumption that the effect of wind/advective losses were mitigated.
33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019
We examine the problem of crawling the community structure of a multiplex network containing multiple layers of edge relationships. While there has been a great deal of work examining community structure in general, and some work on the problem of sampling a network to preserve its community structure, to the best of our knowledge, this is the first work to consider this problem on multiplex networks. We consider the specific case in which the layers of a multiplex network have different query (collection) costs and reliabilities; and a data collector is interested in identifying the community structure of the most expensive layer. We propose MultiComSample (MCS), a novel algorithm for crawling a multiplex network. MCS uses multiple levels of multi-armed bandits to determine the best layers, communities and node roles for selecting nodes to query. We test MCS against six baseline algorithms on real-world multiplex networks, and achieved large gains in performance. For example, after consuming a budget equivalent to sampling 20% of the nodes in the expensive layer, we observe that MCS outperforms the best baseline by up to 49%.
Vulnerability analysts protecting software lack adequate tools for understanding data flow in binaries. We present a case study in which we used human factors methods to develop a taxonomy for understanding data flow and the visual representations needed to support decision making for binary vulnerability analysis. Using an iterative process, we refined and evaluated the taxonomy by generating three different data flow visualizations for small binaries, trained an analyst to use these visualizations, and tested the utility of the visualizations for answering data flow questions. Throughout the process and with minimal training, analysts were able to use the visualizations to understand data flow related to security assessment. Our results indicate that the data flow taxonomy is promising as a mechanism for improving analyst understanding of data flow in binaries and for supporting efficient decision making during analysis.
In order to improve the accuracy of subsurface target classification with ground penetrating radar (GPR) systems, it is desired to transmit and receive ultra-wide band pulses with varying combinations of polarization (a technique referred to as polarimetry). The sinuous antenna exhibits such desirable properties as ultra-wide bandwidth, polarization diversity, and low-profile form factor, making it an excellent candidate for the radiating element of such systems. However, sinuous antennas are dispersive since the active region moves with frequency along the structure, resulting in the distortion of radiated pulses. This distortion may be compensated in signal processing with accurately simulated or measured antenna phase information. However, in a practical GPR, the antenna performance may deviate from that simulated, accurate measurements may be impractical, and/or the dielectric loading of the environment may cause deviations. In such cases, it may be desirable to employ a simple dispersion model based on antenna design parameters which may be optimized in situ. This paper explores the dispersive properties of the sinuous antenna and presents a simple, adjustable, model that may be used to correct dispersed pulses. The dispersion model is successfully applied to both simulated and measured scenarios, thereby enabling the use of sinuous antennas in polarimetric GPR applications.
The wave energy resource for U.S. coastal regions has been estimated at approximately 1,200 TWh/ yr (EPRI 2011). The magnitude is comparable to the natural gas and coal energy generation. Although the wave energy industry is relatively new from a commercial perspective, wave energy conversion (WEC) technology is developing at an increasing pace. Ramping up to commercial scale deployment of WEC arrays requires demonstration of performance that is economically competitive with other energy generation methods. The International Electrotechnical Commission has provided technical specifications for developing wave energy resource assessments and characterizations, but it is ultimately up to developers to create pathways for making a specific site competitive. The present study uses example sites to evaluate the annual energy production using different wave energy conversion strategies and examines pathways available to make WEC deployments competitive. The wave energy resource is evaluated for sites along the U.S. coast and combinations of wave modeling and basic resource assessments determine factors affecting the cost of energy at these sites. The results of this study advance the understanding of wave resource and WEC device assessment required to evaluate commercial-scale deployments.
Falling particle receivers are an emerging technology for use in concentrating solar power systems. In this work, quartz tubes cut in half to form tube shells (referred to as quartz half-shells) are investigated for use as a full or partial aperture cover to reduce radiative and advective losses from the receiver. A receiver subdomain and surrounding air volume are modeled using ANSYS® Fluent®. The model is used to simulate fluid dynamics and heat transfer for the following cases: (1) open aperture, (2) aperture fully covered by quartz half-shells, and (3) aperture partially covered by quartz half-shells. We compare the percentage of total incident solar power lost due to conduction through the receiver walls, advective losses through the aperture, and radiation exiting out of the aperture. Contrary to expected outcomes, simulation results using the simplified receiver subdomain show that quartz aperture covers can increase radiative losses and, in the partially covered case, also increase advective losses. These increased heat losses are driven by elevated quartz half-shell temperatures and have the potential to be mitigated by active cooling and/or material selection.
Advances in machine intelligence have sparked interest in hardware accelerators to implement these algorithms, yet embedded electronics have stringent power, area budgets, and speed requirements that may limit non- volatile memory (NVM) integration. In this context, the development of fast nanomagnetic neural networks using minimal training data is attractive. Here, we extend an inference-only proposal using the intrinsic physics of domain-wall MTJ (DW-MTJ) neurons for online learning to implement fully unsupervised pattern recognition operation, using winner-take-all networks that contain either random or plastic synapses (weights). Meanwhile, a read-out layer trains in a supervised fashion. We find our proposed design can approach state-of-the-art success on the task relative to competing memristive neural network proposals, while eliminating much of the area and energy overhead that would typically be required to build the neuronal layers with CMOS devices.
Sandia National Laboratories has developed a model characterizing the nonlinear encoding operator of the world's first hyperspectral x-ray computed tomography (H-CT) system as a sequence of discrete-to-discrete, linear image system matrices across unique and narrow energy windows. In fields such as national security, industry, and medicine, H-CT has various applications in the non-destructive analysis of objects such as material identification, anomaly detection, and quality assurance. However, many approaches to computed tomography (CT) make gross assumptions about the image formation process to apply post-processing and reconstruction techniques that lead to inferior data, resulting in faulty measurements, assessments, and quantifications. To abate this challenge, Sandia National Laboratories has modeled the H-CT system through a set of point response functions, which can be used for calibration and anaylsis of the real-world system. This work presents the numerical method used to produce the model through the collection of data needed to describe the system; the parameterization used to compress the model; and the decompression of the model for computation. By using this linear model, large amounts of accurate synthetic H-CT data can be efficiently produced, greatly reducing the costs associated with physical H-CT scans. Furthermore, successfully approximating the encoding operator for the H-CT system enables quick assessment of H-CT behavior for various applications in high-performance reconstruction, sensitivity analysis, and machine learning.
Ionic liquids are a unique class of materials with several potential applications in electrochemical energy storage. When used in electrolytes, these highly coordinating solvents can influence device performance through their high viscosities and strong solvation behaviors. In this work, we explore the effects of pyrrolidinium cation structure and Li+ concentration on transport processes in ionic liquid electrolytes. We present correlated experimental measurements and molecular simulations of Li+ mobility and O2 diffusivity, and connect these results to dynamic molecular structural information and device performance. In the context of Li-O2/Li-air battery chemistries, we find that Li+ mobility is largely influenced by Li+-anion coordination, but that both Li+ and O2 diffusion may be affected by variations of the pyrrolidinium cation and Li+ concentration.
Of specific concern to this report and the related experiments is ionization of air by gammas rays and the cascading electrons in the High-Energy Radiation Megavolt Electron Source (HERMES) III courtyard. When photons generated by HERMES encounter a neutral atom or molecule, there is a chance that they will interact via one of several mechanisms: photoelectric effect, Compton scattering, or pair production. In both the photoelectric effect and Compton scattering, an electron is liberated from the atom or molecule with a direction of travel preferentially aligned with the gamma ray. This results in a flow of electrons away from the source region, which results in large scale electric and magnetic fields. The strength of these fields and their dynamics are dependent on the conductivity of the air. A more comprehensive description is provided by Longmire and Gilbert.