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Wave statistics computed using output from the NOAA WWIII hindcast simulations, spanning thirty years from 1980 to 2009. The statistics are computed based on frequency-directional variance density spectra every three hours for 1951 locations in US waters.
The purpose of this document is to describe the waste stream from Z-Pinch Residual Waste Project that due to worker safety concerns and operational efficiency is a candidate for blending Transuranic and low level waste together and can be safely packaged as low-level waste consistent with DOE Order 435.1 requirements and NRC guidance 10 CFR 61.42. This waste stream consists of the Pu-ICE post-shot containment systems, including plutonium targets, generated from the Z Machine experiments requested by LANL and conducted by SNL/NM. In the past, this TRU waste was shipped back to LANL after Sandia sends the TRU data package to LANL to certify the characterization (by CCP), transport and disposition at WIPP (CBFO) per LANL MOU-0066. The Low Level Waste is managed, characterized, shipped and disposed of at NNSS by SNL/NM per Sandia MOU # 11-S-560.
To permit calculation of the fatigue strength of drive components, drives are classified in terms of their loading conditions. Formulas are presented for the equivalence factors used in converting the actual variable loading conditions to conventional constant conditions. The overload coefficients required in calculating the static strength of drive components at the peak load are also determined.
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Failure detection methods are of significant interest for photovoltaic (PV) site operators to help reduce gaps between expected and observed energy generation. Current approaches for field-based fault detection, however, rely on multiple data inputs and can suffer from interpretability issues. In contrast, this work offers an unsupervised statistical approach that leverages hidden Markov models (HMM) to identify failures occurring at PV sites. Using performance index data from 104 sites across the United States, individual PV-HMM models are trained and evaluated for failure detection and transition probabilities. This analysis indicates that the trained PV-HMM models have the highest probability of remaining in their current state (87.1% to 93.5%), whereas the transition probability from normal to failure (6.5%) is lower than the transition from failure to normal (12.9%) states. A comparison of these patterns using both threshold levels and operations and maintenance (O&M) tickets indicate high precision rates of PV-HMMs (median = 82.4%) across all of the sites. Although additional work is needed to assess sensitivities, the PV-HMM methodology demonstrates significant potential for real-time failure detection as well as extensions into predictive maintenance capabilities for PV.
Ultra-relativistic motion and efficient conversion of kinetic energy to radiation are required by gamma-ray burst (GRB) observations, yet they are difficult to simultaneously achieve. Three leading mechanisms have been proposed to explain the observed emission emanating from GRB outflows: radiation from either relativistic internal or external shocks, or thermal emission from a photosphere. Previous works were dedicated to independently treating these three mechanisms and arguing for a sole, unique origin of the prompt emission of GRBs. In contrast, herein, we first explain why all three models are valid mechanisms and that a contribution from each of them is expected in the prompt phase. Additionally, we show that a single parameter, the dimensionless entropy of the GRB outflow, determines which mechanism contributes the most to the emission. More specifically, internal shocks dominate for low values of the dimensionless entropy, external shocks for intermediate values, and finally, photospheric emission for large values. We present a unified framework for the emission mechanisms of GRBs with easily testable predictions for each process.
We provide evidence that UGC 1382, long believed to be a passive elliptical galaxy, is actually a giant low surface brightness (GLSB) galaxy that rivals the archetypical GLSB Malin 1 in size. Like other GLSB galaxies, it has two components: a high surface brightness disk galaxy surrounded by an extended low surface brightness (LSB) disk. For UGC 1382, the central component is a lenticular system with an effective radius of 6 kpc. Beyond this, the LSB disk has an effective radius of ∼38 kpc and an extrapolated central surface brightness of ∼26 mag arcsec{sup 2}. Both components have a combined stellar mass of ∼8 × 10{sup 10} M {sub ⊙}, and are embedded in a massive (10{sup 10} M {sub ⊙}) low-density (<3 M {sub ⊙} pc{sup 2}) HI disk with a radius of 110 kpc, making this one of the largest isolated disk galaxies known. The system resides in a massive dark matter halo of at least 2 × 10{sup 12} M {sub ⊙}. Although possibly part of a small group, its low-density environment likely plays a role in the formation and retention of the giant LSB and HI disks. We model the spectral energy distributions and find that the LSB disk is likely older than the lenticular component. UGC 1382 has UV–optical colors typical of galaxies transitioning through the green valley. Within the LSB disk are spiral arms forming stars at extremely low efficiencies. The gas depletion timescale of ∼10{sup 11} years suggests that UGC 1382 may be a very-long-term resident of the green valley. We find that the formation and evolution of the LSB disk in UGC 1382 is best explained by the accretion of gas-rich LSB dwarf galaxies.
There is mounting evidence for the binary nature of the progenitors of gamma-ray bursts (GRBs). For a long GRB, the induced gravitational collapse paradigm proposes as progenitor, or “in-state,” a tight binary system composed of a carbon–oxygen core (CO{sub core}) undergoing a supernova explosion that triggers hypercritical accretion onto a neutron star (NS) companion. For a short GRB (S-GRB), an NS–NS merger is traditionally adopted as the progenitor. We divide long and S-GRBs into two subclasses, depending on whether or not a black hole (BH) is formed in the merger or in the hypercritical accretion process exceeding the NS critical mass. For long bursts, when no BH is formed, we have the subclass of X-ray flashes (XRFs), with isotropic energy E{sub iso}≲10{sup 52} erg and rest-frame spectral peak energy E{sub p,i}≲200 keV. When a BH is formed, we have the subclass of binary-driven hypernovae (BdHNe), with E{sub iso}≳10{sup 52} erg and E{sub p,i}≳200 keV. In analogy, short bursts are similarly divided into two subclasses. When no BH is formed, short gamma-ray flashes (S-GRFs) occur, with E{sub iso}≲10{sup 52} erg and E{sub p,i}≲2 MeV. When a BH is formed, the authentic S-GRBs occur, with E{sub iso}≳10{sup 52} erg and E{sub p,i}≳2 MeV. We give examples and observational signatures of these four subclasses and their rate of occurrence. From their respective rates it is possible that “in-states” of S-GRFs and S-GRBs originate from the “out-states” of XRFs. We indicate two additional progenitor systems: white dwarf–NS and BH–NS. These systems have hybrid features between long and short bursts. In the case of S-GRBs and BdHNe evidence is given of the coincidence of the onset of the high-energy GeV emission with the birth of a Kerr BH.
Since the peculiar nature of Lambda Boötis was first noticed in 1943, the Lambda Boo stars have been recognized as a group of peculiar A-type stars. They are Population I dwarfs that show deficiencies of iron-peak elements (up to 2 dex), but have near-solar C, N, O, and S abundances. In a previous paper, we used both observed and synthetic ultraviolet spectra to demonstrate that the C i 1657 Å/Al ii 1671 Å equivalent width ratio can help distinguish between Lambda Boo stars and other metal-weak stars hotter than 8000 K. In this paper, using observed and synthetic visible (4000–6800 Å) spectra, we demonstrate that the C i 5052.17 Å/Mg ii 4481 Å equivalent width ratio can be used as a quantitative diagnostic for cooler Lambda Boo stars.
We present a simple method for identifying candidate white dwarf systems with dusty exoplanetary debris based on a single temperature blackbody model fit to the infrared excess. We apply this technique to a sample of Southern Hemisphere white dwarfs from the recently completed Edinburgh–Cape Blue Object Survey and identify four new promising dusty debris disk candidates. We demonstrate the efficacy of our selection method by recovering three of the four Spitzer confirmed dusty debris disk systems in our sample. Further investigation using archival high-resolution imaging shows that Spitzer data of the unrecovered fourth object is likely contaminated by a line-of-sight object that either led to a misclassification as a dusty disk in the literature or is confounding our method. Finally, in our diagnostic plot, we show that dusty white dwarfs, which also host gaseous debris, lie along a boundary of our dusty debris disk region, providing clues to the origin and evolution of these especially interesting systems.
Cardinal temperatures for plant processes have been used for thermotolerance screening of genotypes, geoclimatic adaptability determination and phenological prediction. Current simulation models for switchgrass (Panicum virgatum L.) utilize single cardinal temperatures across genotypes for both vegetative and reproductive processes although in-tra-specific variation exists among genotypes. An experiment was conducted to estimate the cardinal temperatures for seed germination of 14 diverse switchgrass genotypes and to classify genotypes for temperature tolerance. Stratified seeds of each genotype were germinated at eight constant temperatures from 10 °C to 45 °C under a constant light intensity of 35 μmol m -2 s -1 for 12 hd -1 . Germination was recorded at 6-h intervals in all treatments. Maximum seed germination (MSG) and germination rate (GR), estimated by fitting Sigmoidal function to germination-time series data, varied among genotypes. Quadratic and bilinear models best described the MSG and GR responses to temperature, respectively. The mean cardinal temperatures, T min , T opt , and T max , were 8.1, 26.6, and 45.1 °C for MSG and 11.1, 33.1, and 46.0 °C for GR, respectively. Cardinal temperatures for MSG and GR; however, varied significantly among genotypes. Genotypes were classified as sensitive (Cave-in-Rock, Dacotah, Expresso, Forestburg, Kanlow, Sunburst, Trailblazer, and Tusca), intermediate (Alamo, Blackwell, Carthage, Shawnee, and Shelter) and tolerant (Summer) to high temperature based on cumulative temperature response index (CTRI) estimated by summing individual response indices estimated from the MSG and GR cardinal temperatures. Similarly, genotypes were also classified as sensitive (Alamo, Blackwell, Carthage, Dacotah, Shawnee, Shelter and Summer), moderately sensitive (Cave-in-rock, Forestburg, Kanlow, Sunburst, and Tusca), moderately tolerant (Trailblazer), and tolerant (Expresso) to low temperatures. The cardinal temperature estimates would be useful to improve switchgrass models for field applications. Additionally, the identified cold- and heat-tolerant genotypes can be selected for niche environments and in switchgrass breeding programs to develop new genotypes for low and high temperature environments.
Presently around the globe there is a significant interest in using biomass for power generation as power generation from coal continues to raise environmental concerns. Biomass alone can be used for generation of power which can bring lot of environmental benefits. However the constraints of using biomass alone can include high investments costs for biomass feed systems and also uncertainty in the security of the feedstock supply due to seasonal variations and in most of the countries biomass is dispersed and the infrastructure for biomass supply is not well established. Alternatively cofiring biomass along with coal offer advantages like (a) reducing the issues related to biomass quality and buffers the system when there is insufficient feedstock quantity and (b) costs of adapting the existing coal power plants will be lower than building new systems dedicated only to biomass. However with the above said advantages there exists some technical constrains including low heating and energy density values, low bulk density, lower grindability index, higher moisture and ash content to successfully cofire biomass with coal. In order to successfully cofire biomass with coal, biomass feedstock specifications need to be established to direct pretreatment options that may include increasing the energy density, bulk density, stability during storage and grindability. Impacts on particle transport systems, flame stability, pollutant formation and boiler tube fouling/corrosion must also be minimized by setting feedstock specifications including composition and blend ratios if necessary. Some of these limitations can be overcome by using pretreatment methods. This paper discusses the impact of feedstock pretreatment methods like sizing, baling, pelletizing, briquetting, washing/leaching, torrefaction, torrefaction and pelletization and steam explosion in attainment of optimum feedstock characteristics to successfully cofire biomass with coal.
The Ensemble Portal harvests resources from multiple heterogonous federated collections. Managing these dynamically increasing collections requires an automatic mechanism to categorize records in to corresponding topics. We propose an approach to use existing ACM DL metadata to build classifiers for harvested resources in the Ensemble project. We also present our experience on utilizing the Amazon Mechanical Turk platform to build ground truth training data sets from Ensemble collections.
We present AviSense, an SDR-based real-time, versatile system for monitoring aviation bands. AviSense detects and characterizes signal activities to enable practical and effective anomaly detection. We identify and tackle the challenges posed by a diverse set of critical aviation bands and technologies. We evaluate our methodology with real-world aviation signal measurements and demonstrate that we can classify aviation signals as normal with 99% accuracy. We also provide an exhaustive evaluation of our anomalous signal detection capability and show that we can accurately classify anomalous signals with 99% accuracy, with few exceptions. We also demonstrate that AviSense can effectively distinguish between different types of anomalies. We build a prototype implementation of AviSense for distributed monitoring.
The US Department of Energy is conducting analyses to inform future decisions regarding an integrated waste management system for the acceptance of spent nuclear fuel (SNF) from commercial nuclear reactor sites. The ability to prepare SNF for shipment from reactor sites is likely to be restricted by ongoing operations at those sites. This report examines the extent to which those operations will restrict the reactor sites' availability to prepare SNF for shipment. Specifically, the analysis focuses on the impact and frequency of refueling operations within the commercial reactor sites in the US. In this report, a characterization of reactor sites into families is described. This characterization is based on the number of operating reactors on site, the length of refueling cycles for those reactors, and the configuration of spent fuel pools on site. Each reactor family is analyzed to estimate the amount of time for which each reactor site in the family would be available to prepare SNF shipments. This analysis is a work in progress, and the results are used to inform the development of new models of an integrated waste management system for commercial SNF. (authors)
Canadian Nuclear Laboratories (CNL) is developing a Near Surface Disposal Facility (NSDF) to be constructed at its Chalk River Laboratories (CRL) site. In general, a NSDF is a facility for radioactive waste disposal, under an engineered cover, located at or within a few meters of the earth's surface. The NSDF will establish a safe, local and permanent means for the disposal of low level radioactive waste. The NSDF is intended to dispose historic waste currently in interim storage as well as waste arising from building decommissioning and environmental remediation activities. The NSDF is to be composed of engineered containment mound, waste water treatment plant and other supporting facilities and infrastructures that are needed to preserve radioactive waste. The NSDF life cycle will comprise two distinct phases: pre closure and post closure. The pre closure phase involves construction, operation and closure activities. The post closure phase includes institutional control and post-institutional control to ensure long term safety and radiological acceptability of the disposal facility. As part of the evolving activities for the NSDF project, a process is developed in accordance with Canadian regulations and International Atomic Energy Agency (IAEA) principles to classify systems that comprise NSDF for safe operation, long term safety of the disposal facility and the protection of workers, the public and the environment. This paper describes the methodology developed to classify Structures, Systems and Components (SSCs) important to safety in post closure phase and determine their design rules. The design rules are the relevant codes, standards and proven engineering practices that should be applied, as appropriate, to the design of SSCs to meet the applicable design requirements. (authors)
The present work explores alternative approaches for in situ monitoring with near real-time data analysis and imaging of dry casks using muons. Monitoring can provide useful signals about the cask contents in a yes/no decision format faster and with fewer muons than traditional imaging. Imaging can be performed as a second step in this process if specific information about the location of the fuel assemblies is needed. To explore and analyze these two modalities, simulations of muon scattering and displacement were performed for various scenarios, including vertical and horizontal fully loaded dry casks, half-loaded dry casks, dry casks with one row of fuel assemblies missing, dry casks with one fuel assembly missing, and empty dry casks. This work demonstrates monitoring can provide fast and reliable indication about the contents of the cask. For 100,000 muons, a few minutes of measurement time with a 3.6 m{sup 2} detector at a 45 deg. zenith angle, 90% detection probability for missing fuel assemblies can be achieved with a false alarm rate lower than 1%. If a positive signal is detected, then longer measurements can be performed and coupled with muon imaging algorithms to determine the location of missing nuclear material. In these cases, transmission muon radiography can produce images of acceptable resolution for all scenarios. In the horizontal dry cask case, all scenarios can be identified and separated adequately. In the vertical dry cask case, similar conclusions can be drawn, but because of shadowing from other fuel assemblies, the location of a missing fuel assembly cannot be identified in the 2-D transmission map. However, views from different directions may resolve this. (authors)