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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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The following information packages present the radiological data as well as physical properties of the four Flanged Tritium Waste Containers currently stored at TA-54, Area G. Also included are summaries of the contents of each container (including waste logs) as well as photographs of the inner and outer portions of each container.
The following information packages present the radiological data as well as physical properties of the four Flanged Tritium Waste Containers currently stored at TA-54, Area G. Also included are summaries of the contents of each container (including waste logs) as well as photographs of the inner and outer portions of each container.
The following information packages present the radiological data as well as physical properties of the four Flanged Tritium Waste Containers currently stored at TA-54, Area G. Also included are summaries of the contents of each container (including waste logs) as well as photographs of the inner and outer portions of each container.
This report uses representative utility-scale and distributed wind energy projects to estimate the levelized cost of energy (LCOE) for land-based and offshore wind power plants in the United States. Data and results detailed here are derived from 2019 commissioned plants and representative industry data as well as state-of-the-art modeling capabilities. Modeling is conducted to provide more granular detail on specific cost categories. This report represents the ninth annual installment and is intended to provide insight into current component-level costs as well as a basis for understanding variability in wind energy LCOE across the country.
CSPlib is an open source software library for analyzing general ordinary differential equation (ODE) systems and detailed chemical kinetic ODE systems. It relies on the computational singular perturbation (CSP) method for the analysis of these systems. The software provides support for: General ODE models (gODE model class) for computing source terms and Jacobians for a generic ODE system; TChem model (ChemElemODETChem model class) for computing source term, Jacobian, other necessary chemical reaction data, as well as the rates of progress for a homogenous batch reactor using an elementary step detailed chemical kinetic reaction mechanism. This class relies on the TChem [2] library; A set of functions to compute essential elements of CSP analysis (Kernel class). This includes computations of the eigensolution of the Jacobian matrix, CSP basis vectors and co-vectors, time scales (reciprocals of the magnitudes of the Jacobian eigenvalues), mode amplitudes, CSP pointers, and the number of exhausted modes. This class relies on the Tines library; A set of functions to compute the eigensolution of the Jacobian matrix using Tines library GPU eigensolver; A set of functions to compute CSP indices (Index Class). This includes participation indices and both slow and fast importance indices.
LLNL has a long history of involvement in research into biological and biophysical methods for assessing ionizing radiation dose, and LLNL staff continue to participate in reviews of low dose related data as well as technology development for national security and population health purposes. A list of recent projects and funding agencies is provided in Table 1. LLNL has also maintained expertise in mammalian biology, radiobiology and cancer (computational and biological) over the last 5 years.
This workshop presentation summarizes interim results from PVEL's DOE-funded outdoor bifacial field study at PVUSA and well as data from PVEL's indoor bifacial reliability testing.
In July 2018, CMS requested assistance from Oak Ridge National Laboratory (ORNL) to provide expert data science support aimed at developing algorithms for data mining of medical data for operational and payment purposes. The project is intended to be exploratory: work is aimed at alleviating challenges associated with improper payments, specifically audit methodologies and targeting and changes to risk scores. Project goals include developing new, sophisticated methods for audit targeting and improved profile– payment error correlations, specifically focused on Medicare Part C, the program under which MAOs provide health care services to beneficiaries. ORNL conducted RADV analyses against RAPS and EDS data as well as a hospice landscape analysis per a January 2015 dataset that included Medicare beneficiaries who were in hospice in 2017 and 2018. Ongoing work under this project also involves development of predictive models for RADV investigations and hospice landscape.
This report uses representative utility-scale and distributed wind energy projects to estimate the levelized cost of energy (LCOE) for land-based and offshore wind power plants in the United States. Data and results detailed here are derived from 2020 commissioned plants and representative industry data as well as state-of-the-art modeling capabilities. Modeling is conducted to provide more granular detail on specific cost categories. This report represents the tenth annual installment and is intended to provide insight into current component-level costs as well as a basis for understanding variability in wind energy LCOE across the country.
The TurboWave I and II infrasound campaigns were conducted to examine short term variability in acoustic propagation at local and regional distances. The tests were conducted in nearly co-located regions at the Energetic Materials Research and Testing Center in Socorro, NM between 2019 and 2020 and recorded across a variety of acoustic microbarometer sensors. This report details the waveform data recorded from the experiment and coincides with data archival at the Incorporated Research Institutions for Seismology. The report includes a description of the experiment along with the types of data and instruments. The data release includes raw waveform data as well as metadata information.
Approximately 93% of US total energy supply is dependent on wellbores in some form. The industry will drill more wells in next ten years than in the last 100 years (King, 2014). Global well population is around 1.8 million of which approximately 35% has some signs of leakage (i.e. sustained casing pressure). Around 5% of offshore oil and gas wells “fail” early, more with age and most with maturity. 8.9% of “shale gas” wells in the Marcellus play have experienced failure (120 out of 1,346 wells drilled in 2012) (Ingraffea et al., 2014). Current methods for identifying wells that are at highest priority for increased monitoring and/or at highest risk for failure consists of “hand” analysis of multi-arm caliper (MAC) well logging data and geomechanical models. Machine learning (ML) methods are of interest to explore feasibility for increasing analysis efficiency and/or enhanced detection of precursors to failure (e.g. deformations). MAC datasets used to train ML algorithms and preliminary tests were run for “predicting” casing collar locations and performed above 90% in classification and identifying of casing collar locations.
Approximately 93% of US total energy supply is dependent on wellbores in some form. The industry will drill more wells in next ten years than in the last 100 years (King, 2014). Global well population is around 1.8 million of which approximately 35% has some signs of leakage (i.e. sustained casing pressure). Around 5% of offshore oil and gas wells “fail” early, more with age and most with maturity. 8.9% of “shale gas” wells in the Marcellus play have experienced failure (120 out of 1,346 wells drilled in 2012) (Ingraffea et al., 2014). Current methods for identifying wells that are at highest priority for increased monitoring and/or at highest risk for failure consists of “hand” analysis of multi-arm caliper (MAC) well logging data and geomechanical models. Machine learning (ML) methods are of interest to explore feasibility for increasing analysis efficiency and/or enhanced detection of precursors to failure (e.g. deformations). MAC datasets used to train ML algorithms and preliminary tests were run for “predicting” casing collar locations and performed above 90% in classification and identifying of casing collar locations.
The SCEPTRE1 code models coupled photon and electron transport in material media using discrete ordinates methods. The code can use different solvers in 1D and 3D in parallel, promising users the ability to run problems that are significant both in physics and computational scale. One capability that is perhaps unique at this time in the NNSA complex is SCEPTRE’s coupled photon– electron transport physics in a discrete ordinates approach. For example, incident x–rays can generate a significant electron population, whose transport can be simulated within the same code. In this context, fully–coupled means that photons can produce electrons and electrons can produce photons; uncoupled or no–coupling means photons do not produce electrons. Partial–coupling, an intermediate option in which photons can produce electrons but electrons do not produce photons, is not considered in the present work. The MCNP code can treat this physics with a Monte Carlo approach. MCNP should be viewed as complementary to SCEPTRE in both computational method (Monte Carlo versus discrete ordinates) as well as data libraries. SCEPTRE and MCNP should provide important checks on each other, especially from complementary modeling frameworks of a S n (discrete ordinates) mesh based method versus Monte Carlo particle based method. In order to learn more about SCEPTRE, and how it might inform engineering work at the Laboratory, we used it to carry out a set of initial studies. This was a series of 1D simulations of photon beams hitting Aluminum slabs. Our studies mainly used SCEPTRE directly, and made little use of the closely associated RAPTURE code (more on this below). The goals of these studies were first, to familiarize ourselves with operating the code, and second, to begin to assess the importance of coupled electron–photon transport. To support these studies, we began to develop a set of scripts to initialize inputs, run tools, process outputs, and plot results. An initial version of these scripts has been committed to the Lightning workflow repository. This memo relates the results of those studies.
I describe the construction of new equations of state for Kel F-800, as both linear and quadratic fits to shock data and Sesame 97200. This is the first SESAME table for Kel F-800 of which I’m aware. The SESAME table recovers shock data by construction and does reasonably well with thermal data, but differs with Brillouin scattering results for sound speed and density under static compression.
This analysis uses representative utility-scale and distributed wind energy projects to estimate the levelized cost of energy (LCOE) for land-based, offshore, and distributed wind power in the United States. Data and results detailed here are derived from 2021 commissioned plants and representative industry data as well as state-of-the-art modeling capabilities. Modeling is conducted to provide more granular detail on specific cost categories. This study represents the 11th annual installment and is intended to provide insight into current component-level costs as well as a basis for understanding variability in wind energy LCOE across the country.
In September 1980, the initial delayed critical configuration of the Solution High-Energy Burst Assembly (SHEBA I) was assembled, and the US Department of Energy’s Oak Ridge National Laboratory (ORNL) staff performed near critical and subcritical measurements using the californium source-driven noise analysis (CSDNA) method at the Los Alamos National Laboratory (LANL) Critical Experiments Facility. An unreflected 56 cm outside diameter stainless-steel cylindrical tank was partially filled with uranyl fluoride solution (235U enrichment was 4.95 wt %) until delayed criticality was achieved. Then, measurements were performed for various fuel solution heights from delayed critical to 60% of the height required for delayed criticality. The stainless-steel tank had an inside diameter of 54.6 cm and a height of 105 cm and was partially (20–36.5 cm) filled with an aqueous solution of uranyl fluoride (with a density of 2.162 g/cm 3 ). The density of the uranium in solution was 1.042 g/cm 3 , and the solution had a H/U atomic ratio of 20.43. The tank had a 6.35 mm wall thickness and an axial reentrant tube with an inside diameter of 6.02 cm and wall thickness of 0.165 cm. The tank was essentially unreflected on the top and sides because it was in a thin metal shed. The reactivity of the near–delayed critical configuration was −10.4 cents, which corresponds to a k eff value of 0.99922. In addition to the CSDNA measurements, the prompt neutron decay constants were determined from break frequency noise analysis (BNFA) measurements. The subcritical neutron multiplication factors from CSDNA and BFNA compared extremely well. These data can be used as the basis of International Nuclear Criticality Safety Evaluation benchmark for the near-critical configuration, and the k eff values at various subcritical configurations can be used as subcritical benchmarks. Furthermore, the measured prompt neutron decay constants can be used as reactor physics benchmarks. This report documents the experimental information for the measurements performed so that, at a later date, researchers could perform the required uncertainty and calculational analyses and documentation to use these data for an International Criticality Safety Benchmark Evaluation Program (ICSBEP) or Nuclear Energy Agency benchmarks. The measured prompt neutron decay constants can be used as the basis of a benchmark for the International Reactor Physics Evaluation Program (IRPhEP). The data from these measurements are available from the ORNL Records Management Services Department, and the logbook is available from ICSBEP at Idaho National Laboratory.
Solar photovoltaic (PV) cybersecurity is a growing field of research. As deployments of solar PV has increased, cyber risk has also increased. However, utility solar PV installations are not required to comply with North American Electric Reliability Corporation (NERC) Critical Infrastructure Protection (CIP) unless they meet a minimum generation threshold of 75 Megawatts (MW). Individual residential scale solar PV deployments will not meet that generation threshold and are therefore excluded from NERC CIP requirements. With most solar installations below 75MW, solar PV has been deployed with minimal oversight and highly variable cybersecurity maturity. The resources that make up the digital supply chain can include software, code, data, as well as other digital components. However as clean energy technology advances, cybersecurity threats and vulnerabilities continue to evolve and grow in sophistication. Solar PV faces a unique challenge in which it can be deployed in residential buildings and purchased by a consumer directly. This makes the supply chain of PV a unique challenge, where responsible parties for cybersecurity vary widely depending on the type of solar PV being deployed. Supply chain cybersecurity for solar PV represents a critical area for ensuring safe operations as the U.S. moves towards a clean energy future.
Microstructural damage accumulation models have been used to produce calibrated life estimation models for a DR22/P22 steel wye-block welds, and a Jethete stainless steel turbine blade (bucket). The calibrated life estimation models will aid the power plant operator in determining optimal maintenance and operation schedules based upon historical operational data as well as current, or future operation schemes. The impact of this project will enable existing coal-fueled power plants to operate safely for longer periods of time and at higher efficiencies, thereby reducing the economic and environmental impact of the existing coal power plant fleet. Testing, characterization, and modelling indicates that the operating life of P22 pipelines and their welds are dominated by fatigue damage mechanisms. Specifically, fatigue is of no concern in these materials when operating under realistic conditions manifesting in the main steam piping of coal-fueled power plants. However, if a low-temperature overload ever occurs during operation, fatigue will manifest as a damage mechanism of interest. Primary impact provided by the completion of this work is in the manifestation of a detailed ABAQUS solid model providing accurate boundary conditions to enable the prediction of operational stresses and strains. The completed plant solid model, in conjunction with the calibrated fatigue and creep life models provide definitive confirmation that creep is the dominant damage mechanism during operational conditions. Jethete life modelling has been completed by the manifestation of a material-specific, and temperature-specific, Kitagowa diagram. The Kitagowa diagram provides maintenance and operation decision making with scientifically-based go/no-go support based upon the crack-like features that have been identified by use of inspection.