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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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Livermore researchers seek to understand the disease mechanism of amyotrophic lateral sclerosis (ALS) and, with that knowledge, identify therapies to cure this always-fatal disease. As the article beginning on p. 4 describes, the ALS research effort combines Laboratory core competencies in bioscience, engineering, computational modeling, and data science.
The report documents melter and off-gas performance results obtained on the DM1200 melter during processing of LAW Sub-Envelope B1 feed. The principal objectives of the DM1200 melter testing were to demonstrate suitable processing and production rates for LAW B1 feed; characterize the glass product for elemental composition; measure elemental emission rates and decontamination factors (DFs) across the melter and off-gas treatment components; and provide additional data on the performance of the thermal catalytic oxidation and selective catalytic reduction (TCO/SCR) unit efficiency in treating organic emissions. The feed was spiked with organic compounds for part of the testing duration. All data were collected for use in engineering and system design purposes. No sampling and analysis to collect environmental data was performed during these tests.
This presentation highlights the overall objectives of the SETO funded protection project. The main technical approaches are also highlighted. This high impact project produces multiple innovative outcomes, including comprehensive impact study of how IBR affects protection elements, simplified low-order IBR model for protection engineers, enhanced and data-driven protection design, co-design concept for coordination protection and IBRs.
Theoretical and experimental principles are outlined for the creation of equipment and manufacturing systems that employ magnetic fluids, for use in the auto industry and elsewhere.
In our work, the authors reviewed engine, vehicle, and fuel data since 1925 to examine the historical and recent coupling of compression ratio and fuel antiknock properties (i.e., octane number) in the U.S. light-duty vehicle market. The analysis identified historical timeframes, trends, and illustrated how three factors: consumer preferences, technical capabilities, and regulatory legislation, affect personal mobility. Data showed that throughout history these three factors have a complex and time sensitive interplay. Long term trends in the data were identified where interaction and evolution between all three factors was observed. Transportation efficiency per unit power (gal/ton-mi/hp) was found to be a good metric to integrate technical, societal, and regulatory effects into the evolutional pathway of personal mobility. From this framework, discussions of future evolutionary changes to personal mobility are also presented.
An attempt has been made to define the extent to which metabolic flux in central plant metabolism is reflected by changes in the transcriptome and metabolome, based on an analysis of in vitro cultured immature embryos of two oilseed rape (Brassica napus) accessions which contrast for seed lipid accumulation. Metabolic flux analysis (MFA) was used to constrain a flux balance metabolic model which included 671 biochemical and transport reactions within the central metabolism. This highly confident flux information was eventually used for comparative analysis of flux vs. transcript (metabolite). Metabolite profiling succeeded in identifying 79 intermediates within the central metabolism, some of which differed quantitatively between the two accessions and displayed a significant shift corresponding to flux. An RNA-Seq based transcriptome analysis revealed a large number of genes which were differentially transcribed in the two accessions, including some enzymes/proteins active in major metabolic pathways. With a few exceptions, differential activity in the major pathways (glycolysis, TCA cycle, amino acid, and fatty acid synthesis) was not reflected in contrasting abundances of the relevant transcripts. The conclusion was that transcript abundance on its own cannot be used to infer metabolic activity/fluxes in central plant metabolism. Lastly, this limitation needs to be borne in mind in evaluating transcriptome data and designing metabolic engineering experiments.
Abstract. Bed-material sediment particle size data, particularly the median sediment particle size (D50), are critical for understanding and modeling riverine sediment transport. However, sediment particle size observations are primarily available at individual sites. Large-scale modeling and assessment of riverine sediment transport are limited by the lack of continuous regional maps of bed-material sediment particle size. We hence present a map of D50 over the contiguous US in a vector format that corresponds to approximately 2.7 million river segments (i.e., flowlines) in the National Hydrography Dataset Plus (NHDPlus) dataset. We develop the map in four steps: (1) collect and process the observed D50 data from 2577 U.S. Geological Survey stations or U.S. Army Corps of Engineers sampling locations; (2) collocate these data with the NHDPlus flowlines based on their geographic locations, resulting in 1691 flowlines with collocated D50 values; (3) develop a predictive model using the eXtreme Gradient Boosting (XGBoost) machine learning method based on the observed D50 data and the corresponding climate, hydrology, geology, and other attributes retrieved from the NHDPlus dataset; and (4) estimate the D50 values for flowlines without observations using the XGBoost predictive model. We expect this map to be useful for various purposes, such as research in large-scale river sediment transport using model- and data-driven approaches, teaching environmental and earth system sciences, planning and managing floodplain zones, etc. The map is available at https://doi.org/10.5281/zenodo.4921987 (Li et al., 2021a).
New hadron production data are needed if we want to reduce our neutrino flux uncertainty. EMPHATIC offers a cost-effective approach to reducing the hadron production uncertainties by at least a factor of 2. We have developed an initial design of the spectrometer, run plans for 2019-21, and are putting together a proposal (should be on arXiv very soon). Hardware contributions from Fermilab, Canada and Japan. Possibilities for new institutions: VME-based electronics, DAQ development, and people power. Useful data collected during an engineering run in January 2018, analysis is progressing rapidly. We are aiming for publication of results soon.
Dry storage systems are used for interim storage of spent nuclear fuel (SNF). However, with the growing need to extend the operational periods of these systems, there are concerns about the degradation of their concrete overpacks, which could compromise the system's structural integrity and safety during hazardous events. Traditional concrete mixtures used in SNF dry storage systems have remained largely unchanged since their inception and often use conventional ingredients. These materials are susceptible to degradation mechanisms such as chemical attacks, alkali-silica reactions (ASR), and freeze–thaw cycles, which can lead to a loss of strength and durability over time. To address these challenges, this paper reviews the application of ultra-high-performance concrete (UHPC) as a promising alternative for spent nuclear fuel dry storage system overpacks. UHPC offers superior mechanical properties, exceptional durability, and reduced susceptibility to degradation mechanisms compared to conventional concrete. This paper focuses on the role of supplementary cementitious materials (SCMs) such as silica fume, fly ash, and metakaolin in enhancing UHPC performance for SNF storage applications. These SCMs have been shown to significantly improve the material’s microstructure, strength, and resistance to environmental stressors typically encountered in SNF storage environments. Moreover, incorporating SCMs supports sustainable construction by reducing cement consumption and associated carbon emissions. The review brings together existing research and experimental data, providing insights for engineers and researchers on developing UHPC mixtures that meet the rigorous demands of spent nuclear fuel dry storage systems, extending their service life and minimizing inspection intervals.
Over the past 5 years, ORNL has run 95 diesel range fuels in homogene-ous charge compression ignition (HCCI), including 40 bio-diesels and associated diesel fuels in their blending. The bio-diesel blends varied in oxygen content, iodine number, cetane, boiling point distribution, chemical composition, and some contained nitrogen. All fuels were run in an HCCI engine at 1800 rpm, in the power range of 2.5 to 4.5 bar IMEP, using intake air heating for combustion phasing control, and at a compression ratio of 10.6. The engine response to fuel variables has been analyzed statistically. Generally, the engine responded well to fuels with lower nitrogen and oxygen, lower cetane, and lower aromatics. Because of the wide range of fuels combined in the model, it provides only a broad overview of the engine response. It is recommended that data be truncated and re-modeled to obtain finer resolution of engine response to particular fuel variables.
Information concerning times to repair or restore equipment to service given a failure is valuable to probabilistic risk assessments (PRAs). Examples of such uses in modern PRAs include estimation of the probability of failing to restore a failed component within a specified time period (typically tied to recovering a mitigating system before core damage occurs at nuclear power plants) and the determination of mission times for support system initiating event (SSIE) fault tree models. Information on equipment repair or restoration times applicable to PRA modeling is limited and dated for U.S. commercial nuclear power plants. However, the Mitigating Systems Performance Index (MSPI) program covering all U.S. commercial nuclear power plants provides up-to-date information on restoration times for a limited set of component types. This paper describes the MSPI program data available and analyzes the data to obtain median and mean component restoration times as well as non-restoration cumulative probability curves. The MSPI program provides guidance for monitoring both planned and unplanned outages of trains of selected mitigating systems deemed important to safety. For systems included within the MSPI program, plants monitor both train UA and component unreliability (UR) against baseline values. If the combined system UA and UR increases sufficiently above established baseline results (converted to an estimated change in core damage frequency or CDF), a “white” (or worse) indicator is generated for that system. That in turn results in increased oversight by the US Nuclear Regulatory Commission (NRC) and can impact a plant’s insurance rating. Therefore, there is pressure to return MSPI program components to service as soon as possible after a failure occurs. Three sets of unplanned outages might be used to determine the component repair durations desired in this article: all unplanned outages for the train type that includes the component of interest, only unplanned outages associated with failures of the component of interest, and only unplanned outages associated with PRA failures of the component of interest. The paper will describe how component repair times can be generated from each set and which approach is most applicable. Repair time information will be summarized for MSPI pumps and diesel generators using data over 2003 – 2007. Also, trend information over 2003 – 2012 will be presented to indicate whether the 2003 – 2007 repair time information is still considered applicable. For certain types of pumps, mean repair times are significantly higher than the typically assumed 24 h duration.
Since 1990, the National Renewable Energy Laboratory’s (NREL's) National Wind Technology Center (NWTC) has tested more than 150 wind turbine blades. NWTC researchers can test full-scale and subcomponent articles, conduct data analyses, and provide engineering expertise on best design practices. Structural testing of wind turbine blades enables designers, manufacturers, and owners to validate designs and assess structural performance to specific load conditions. Rigorous structural testing can reveal design and manufacturing problems at an early stage of development that can lead to overall improvements in design and increase system reliability.
HyRAM is a software toolkit for conducting quantitative risk assessment (QRA) and consequence modeling for hydrogen infrastructure and transportation systems. HyRAM contains validated, simplified hydrogen behavior models, a standardized QRA approach, and engineering models and generic data relevant to hydrogen installations. HyRAM 1.0 was developed by Sandia National Laboratories for the U. S. Department of Energy to increase access to technical data about hydrogen and to enable the use of that data to support development and revision of national and international codes and standards with risk assessment and consequence modeling. The HyRAM toolkit integrates analytical and probabilistic models for quantifying accident scenarios, predicting physical effects, and characterizing the impact of hydrogen hazards (including thermal effects from jet fires and overpressure effects from deflagrations in confined areas) on people and structures. HyRAM incorporates generic probabilities for equipment failures for nine types of components, and probabilistic models for the impact of heat flux on humans and structures, with computationally and experimentally validated analytical first order models of hydrogen release and flame physics. Version 1 can be used to quantify the risk and consequences associated with gaseous hydrogen releases from user-defined hydrogen installations.
In order to support engineering analysis of Virtual Environment for Reactor Analysis (VERA) model results, the Consortium for Advanced Simulation of Light Water Reactors (CASL) needs a tool that provides visualizations of HDF5 files that adhere to the VERAOUT specification. VERAView provides an interactive graphical interface for the visualization and engineering analyses of output data from VERA. The Python-based software provides instantaneous 2D and 3D images, 1D plots, and alphanumeric data from VERA multi-physics simulations.
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Since 1990, the National Wind Technology Center (NWTC) at the National Renewable Energy Laboratory (NREL) has validated more than 150 wind turbine blades, as well as blades and components for marine energy devices. Researchers at the NWTC can experimentally validate full-scale and subcomponent articles, conduct data analyses, and provide engineering expertise to optimize design practices. Structural evaluation of turbine blades enables designers, manufacturers, and owner operators t o assess designs and structural performance under specific extreme loading and dynamic conditions. Rigorous structural validation can reveal design and manufacturing problems at an early state of development that lead to improvements in component design and increase system reliability.
The study focuses on the MIP and Boggess pads of the MSEEL (Marcellus Shale Energy and Environmental Laboratory), a public-private partnership with a mandate to publicly release data for scientists and engineers to engage with. Multiple diagnostic tools are used to characterize the formation and monitor fracture treatment and propagation. Geomechanical modeling is used to understand the insitu stresses, microseismic to describe half-lengths and heights, and fiber optics to characterize offset well. Fracture Driven Interactions (FDI’s) and interstage communication. Recent publications covering the MSEEL MIP-3H and MIP-5H wells are reviewed and discussed. A preview of the findings from the Boggess pad (first production in November 2019) is also shared here
Since 1990, the National Renewable Energy Laboratory's (NREL's) National Wind Technology Center (NWTC) has tested more than 150 wind turbine blades. NWTC researchers can test full-scale and subcomponent articles, conduct data analyses, and provide engineering expertise on best design practices. Structural testing of wind turbine blades enables designers, manufacturers, and owners to validate designs and assess structural performance to specific load conditions. Rigorous structural testing can reveal design and manufacturing problems at an early stage of development that can lead to overall improvements in design and increase system reliability.