Central depression in nucleonic densities: Trend analysis in the nuclear density functional theory approach
Not Available
SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Not Available
Online-coupled climate and chemistry models are necessary to realistically represent the interactions between climate variables and chemical species and accurately simulate aerosol direct and indirect effects on cloud, precipitation, and radiation. In this Part I of a two-part paper, simulations from the Weather Research and Forecasting model coupled with the physics package of Community Atmosphere Model (WRF-CAM5) are conducted with the default heterogeneous ice nucleation parameterization over East Asia for two full years: 2006 and 2011. A comprehensive model evaluation is performed using satellite and surface observations. The model shows an overall acceptable performance for major meteorological variables at the surface and in the boundary layer, as well as column variables (e.g., precipitation, cloud fraction, precipitating water vapor, downward longwave and shortwave radiation). Moderate to large biases exist for cloud condensation nuclei over oceanic areas, cloud variables (e.g., cloud droplet number concentration, cloud liquid and ice water paths, cloud optical depth, longwave and shortwave cloud forcing). These biases indicate a need to improve the model treatments for cloud processes, especially cloud droplets and ice nucleation, as well as to reduce uncertainty in the satellite retrievals. The model simulates well the column abundances of chemical species except for column SO 2 but relatively poor for surface concentrations of several species such as CO, NO 2 , SO 2 , PM2.5, and PM10. Several reasons could contribute to the underestimation of major chemical species in East Asia including underestimations of anthropogenic emissions and natural dust emissions, uncertainties in the spatial and vertical distributions of the anthropogenic emissions, as well as biases in meteorological, radiative, and cloud predictions. Despite moderate to large biases in the chemical predictions, the model performance is generally consistent with or even better than that reported for East Asia with only a few exceptions. The model generally reproduces the observed seasonal variations and the difference between 2006 and 2011 for most variables or chemical species. Overall, these results demonstrate promising skills of WRF-CAM5 for long-term simulations at a regional scale and suggest several areas of potential improvements.
Geologic domains for the northern Gulf of Mexico derived using the Subsurface Trend Analysis (STA) method. The domains were postulated using geologic province, lithologic, and structural information and validated using statistical methods. Publication detailing the STA method: Rose, K., Bauer, J.R., and Mark-Moser, M. (2020) Subsurface trend analysis, a multi-variate geospatial approach for subsurface evaluation and uncertainty reduction, Interpretation, vol. 8, issue 1 https://library.seg.org/doi/abs/10.1190/int-2019-0019.1 Detailed discussion of domain formation and analysis: Mark-Moser, M., Miller, R., Bauer, J., Rose, K., and C. Disenhof. 2018, Analysis of Subsurface Reservoir Properties Using a Novel Geospatial Approach, Offshore Gulf of Mexico. NETL-TRS-2018 https://edx.netl.doe.gov/dataset/detailed-analysis-of-geospatial-trends-of-hydrocarbon-accumulations-offshore-gulf-of-mexico
Brief summary of SSL Pricing and Efficacy Trend Analysis for Utility Program Planning report.
Report to help utilities and energy efficiency organizations forecast the order in which important SSL applications will become cost-effective and estimate when each "tipping point" will be reached. Includes performance trend analysis from DOE's LED Lighting Facts® and CALiPER programs plus cost analysis from various sources.
Shape constrained spline fitting is a useful method to impose prior knowledge onto flexible semi-parametric models during parameter estimation. Most typically, the function shape is imposed through order restrictions on the regression coefficients. The intended shape is considered known or selected based on heuristic rules. In this study, we present a method to estimate the optimal set of order restrictions to segment a univariate data series into episodes with distinct shapes. This is also known as the qualitative trend analysis (QTA) problem. The obtained solution uses a trade-off between lack-of-fit and model complexity. Further, our practical implementation takes inspiration from the generalized order restricted information criterion (GORIC) for inequality-constrained model selection. From this, one learns (a) that QTA can be formulated as a mixed-integer quadratic program (MIQP) and (b) that the newly proposed mixed order restricted information criterion (MORIC) enables optimal segmentation. This is illustrated through didactic case studies.
This report is a summary of the finance trends for small-scale solar photovoltaic (PV) projects (PV <1 MW), large-scale PV projects (PV greater than or equal to 1 MW), and concentrated solar power projects as reported in the National Renewable Energy Laboratory's Renewable Energy Finance Tracking Initiative (REFTI). The report presents REFTI data during the five quarterly periods from the fourthquarter of 2009 to the first half of 2011. The REFTI project relies exclusively on the voluntary participation of industry stakeholders for its data; therefore, it does not offer a comprehensive view of the technologies it tracks. Despite this limitation, REFTI is the only publicly available resource for renewable energy project financial terms. REFTI analysis offers usable inputs into theproject economic evaluations of developers and investors, as well as the policy assessments of public utility commissions and others in the renewable energy industry.
In this paper, we introduce a new visual analytics system, called Matisse, that allows exploration of global trends in textual information streams with specific application to social media platforms. Despite the potential for real-time situational awareness using these services, interactive analysis of such semi-structured textual information is a challenge due to the high-throughput and high-velocity properties. Matisse addresses these challenges through the following contributions: (1) robust stream data management, (2) automated sen- timent/emotion analytics, (3) inferential temporal, geospatial, and term-frequency visualizations, and (4) a flexible drill-down interaction scheme that progresses from macroscale to microscale views. In addition to describing these contributions, our work-in-progress paper concludes with a practical case study focused on the analysis of Twitter 1% sample stream information captured during the week of the Boston Marathon bombings.
Following a comprehensive evaluation of WRF-CAM5 in Part I, Part II describes analyses of interannual variability, multi-year variation trends, and the direct, indirect, and total effects of anthropogenic aerosols. Here, the interannual variations of chemical column and surface concentrations, and ozone (O 3 )/particulate matter (PM) indicators are strongly correlated to anthropogenic emission changes. Despite model biases, the model captures well the observed interannual variations of temperature at 2-m, cloud fraction, shortwave cloud forcing, downwelling shortwave radiation, cloud droplet number concentration, column O 3 , and column formaldehyde (HCHO) for the whole domain. While the model reproduces the volatile organic compound (VOC)-limited regimes of O 3 chemistry at sites in Hong Kong, Taiwan, Japan, South Korea, and from the Acid Deposition Monitoring Network in East Asia (EANET) and the degree of sulfate neutralization at the EANET sites, it has limited capability in capturing the interannual variations of the ratio of O 3 and nitrogen dioxide (O 3 /NO 2 ) and PM chemical regime indicators, due to uncertainties in the emissions of precursors for O 3 and secondary PM, the model assumption for ammonium bisulfate (NH 4 HSO 4 ) as well as lack of gas/particle partitioning of total ammonia and total nitrate. While the variation trends in multi-year periods in aerosol optical depth and column concentrations of carbon monoxide, sulfur dioxide, and NO 2 are mainly caused by anthropogenic emissions, those of major meteorological and cloud variables partly reflect feedbacks of chemistry to meteorological variables. The impacts of anthropogenic aerosol indirect effects either dominate or play an important role in the aerosol total effects for most cloud and chemical predictions, whereas anthropogenic aerosol direct effects influence most meteorological and radiation variables. The direct, indirect, and total effects of anthropogenic aerosols exhibit a strong interannual variability in 2001, 2006, and 2011.
This report identifies water-level trends in wells and provides a conceptual framework that explains the hydrologic stresses and factors causing the trends in the Pahute Mesa– Oasis Valley (PMOV) groundwater basin, southern Nevada. Water levels in 79 wells were analyzed for trends between 1966 and 2016. The magnitude and duration of water-level responses to hydrologic stresses were analyzed graphically, statistically, and with water-level models. The conceptual framework consists of multiple stress-specific conceptual models to explain water-level responses to the following hydrologic stresses: recharge, evapotranspiration, pumping, nuclear testing, and wellbore equilibration. Dominant hydrologic stresses affecting waterlevel trends in each well were used to categorize trends as nonstatic, transient, or steady state.
Accurate synonym extraction is a highly desired tool in computational semantics and natural language processing (NLP). Intrinsic to many current language models is the Vector Space Model (VSM) whereby linguistic properties are modeled by real vectors of small length compared to the document size. Here, several vector models are discussed. Utilizing the open source Word2Vec tool, temporal trends in biomedical abstracts from MEDLINE have been analyzed via a unique transformation matrix method. Additionally, accuracy of the word vector method and clustering of temporal trends are discussed.
Rising deployment of inverter-based resources (IBRs), characterized by a lack of rotating mass, is decreasing the total inertia of the system. This can lead to an increased Rate of Change of Frequency (RoCoF) during the disturbance and false activation of protective devices. There is a need to assess the inertia over the past decade amidst the evolving landscape of renewable energy sources to develop strategies for integrating energy storage, enhancing resilience measures, and ensuring the stable and reliable operation of the grid. Therefore, a realistic assessment of the inertia trend using a measurement-based approach that addresses the limitations of existing models is proposed. An inertia study of the Western Interconnection in the United States is performed utilizing the data from 2013 to 2022, obtained from FNET/ GridEye network. The three-second RoCoF time window is chosen for the study as it showed an optimum balance between a strong correlation with the power imbalance (ΔP) and minimum inclusion of primary response from governor. The obtained inertia trend result shows a small percentage declination of inertia over the decade. By examining the result alongside a generation mix graph, insights are gained into the dynamic interplay between shifting energy landscape and system inertia.
The transition from conventional to modern power systems is causing an increase in integration of inverter-based resources (IBRs). This generally leads to a decrease in total system inertia, which in-turn increases the system’s rate-of-change-of-frequency (RoCoF) during disturbances. This poses a threat to the frequency stability of the system and may falsely trigger protective devices. To monitor system status and plan for integrating renewable energy sources like photovoltaic, wind, and energy storage systems, a realistic study of inertia estimation and analysis in the United States (US) over the past decade is needed. This paper uses field-measured phasor measurement unit (PMU) data collected throughout the US from 2013 to 2023 via the Frequency Monitoring Network (FNET/GridEye) operated by the University of Tennessee, Knoxville (UTK) and Oak Ridge National Laboratory (ORNL). The collected PMU frequency data is utilized to estimate the system inertia of the three US interconnections: Eastern, Western, and Texas. Various RoCoF time windows are investigated for estimating the inertia of each interconnection by maximizing the correlation coefficient between the measured RoCoF and power mismatch. The resulting inertia trends over the past decade show approximately a 6% decline in inertia in the Eastern interconnection, a 15% decline in inertia in the Western interconnection, and a 16% increase in inertia in Texas. Key insights into how inertia is changing amidst the complex energy landscape are extracted using the fuel mix trend data. This provides valuable information for future energy strategies and planning.
An LED lamp or luminaire can generally be found that matches or exceeds the efficacy of benchmark technologies in a given product category, and LED products continue to expand into ever-higher lumen output niches. However, the price premium for LED continues to pose a barrier to adoption in many applications, in spite of expected savings from reduced energy use and maintenance. Other factors—such as dimmability and quality of light—can also present challenges. The appropriate type, timing, and magnitude of energy efficiency activities will vary from organization to organization based on local variables and the method of evaluation. A number of factors merit consideration when prioritizing activities for development. Category-specific projections for pricing and efficacy are provided herein to assist in efficiency program planning efforts.
Rare cancers collectively account for a significant proportion of the overall cancer burden in Japan. We aimed to describe and examine the incidence of each rare cancer and the temporal changes using the internationally agreed rare cancer classification. Cancer cases registered in regional population-based cancer registries from 2011 to 2015 and the National Cancer Registry (NCR) from 2016 to 2018 were classified into 18 families, 68 Tier-1 cancer groupings, and 216 single cancer entities based on the RARECAREnet list. Crude incidence rates and age-standardized incidence rates (ASR) were calculated for Tier-1 and Tier-2 cancers. The annual percent change and the 95% and 99% confidence limits for annual ASR for each of the 68 Tier-1 cancers were estimated using the log-linear regression of the weighted least squares method. The differences in ASRs between 2011 and 2018 were evaluated as an absolute change. A total of 5,640,879 cases were classified into Tier-1 and Tier-2 cancers. The ASRs of 18 out of 52 Tier-1 cancers in the rare cancer families increased, whereas the ASR for epithelial tumors of gallbladder decreased. The ASRs of 6 out of the 16 Tier-1 cancers in the common cancer families increased, whereas those of epithelial tumors of stomach and liver decreased. There was no significant change in the incidence of the other 40 Tier-1 cancers. The incidence of several cancers increased due to the dissemination of diagnostic concepts, improved diagnostic techniques, changes in coding practice, and the initiation of the NCR.
Not Available