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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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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Depleted and enriched uranium exposure quantified in former factory workers and local residents of NL Industries, Colonie, NY USA

Background: Between 1958 and 1982, NL Industries manufactured components of enriched (EU) and depleted uranium (DU) at a factory in Colonie NY, USA. More than 5 metric tons of DU was deposited as microscopic DU oxide particles on the plant site and surrounding residential community. A prior study involving a small number of individuals (n=23) indicated some residents were exposed to DU and former workers to both DU and EU, most probably through inhalation of aerosol particles. Objectives: Our aim was to measure total uranium [U] and the uranium isotope ratios: {sup 234}U/{sup 238}U; {sup 235}U/{sup 238}U; and {sup 236}U/{sup 238}U, in the urine of a cohort of former workers and nearby residents of the NLI factory, to characterize individual exposure to natural uranium (NU), DU, and EU more than 3 decades after production ceased. Methods: We conducted a biomonitoring study in a larger cohort of 32 former workers and 99 residents, who may have been exposed during its period of operation, by measuring Total U, NU, DU, and EU in urine using Sector Field Inductively Coupled Plasma - Mass Spectrometry (SF-ICP-MS). Results: Among workers, 84% were exposed to DU, 9% to EU and DU, and 6% to natural uranium (NU) only. For those exposed to DU, urinary isotopic and [U] compositions result from binary mixing of NU and the DU plant feedstock. Among residents, 8% show evidence of DU exposure, whereas none shows evidence of EU exposure. For residents, the [U] geometric mean is significantly below the value reported for NHANES. There is no significant difference in [U] between exposed and unexposed residents, suggesting that [U] alone is not a reliable indicator of exposure to DU in this group. Conclusions: Ninety four percent of workers tested showed evidence of exposure to DU, EU or both, and were still excreting DU and EU decades after leaving the workforce. The study demonstrates the advantage of measuring multiple isotopic ratios (e.g., {sup 236}U/{sup 238}U and {sup 235}U/{sup 238}U) over a single ratio ({sup 235}U/{sup 238}U) in determining sources of uranium exposure. - Highlights: • Biomonitoring study of residents and former workers exposed to DU in Colonie NY. • Urine (99 residents+32 former workers) analyzed for depleted uranium (DU). • DU detected in 84% of workers and 8% of residents >30 years after plant closed. • Enriched uranium detected in 6% of former workers based on isotope ratios.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Summary of Historical Production for Nevada Binary Facilities

The analysis described was initiated to validate inputs used in the US Department of Energy’s (DOE) economic modeling tool GETEM (Geothermal Electricity Technology Evaluation Model) by using publically available data to identify production trends at operating geothermal binary facilities in the state of Nevada. Data required for this analysis was obtained from the Nevada Bureau of Mines and Geology (NBMG), whom received the original operator reports from the Nevada Division of Minerals (NDOM). The data from the NBMG was inputted into Excel files that have been uploaded to the DOE’s National Geothermal Data System (NGDS). Once data was available in an Excel format, production trends for individual wells and facilities could be established for the periods data was available (thru 2009). Additionally, this analysis identified relationships existing between production (temperature and flow rates), power production and plant conversion efficiencies. The data trends showed that temperature declines have a significant impact on power production, and that in some instances operators increased production flow rate to offset power declines. The production trends with time that were identified are being used to update GETEM’s default inputs.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

MultiGreen: A multiplexing architecture for GreenGate cloning

Genetic modification of plants fundamentally relies upon customized vector designs. The ever-increasing complexity of transgenic constructs has led to increased adoption of modular cloning systems for their ease of use, cost effectiveness, and rapid prototyping. GreenGate is a modular cloning system catered specifically to designing bespoke, single transcriptional unit vectors for plant transformation—which is also its greatest flaw. MultiGreen seeks to address GreenGate’s limitations while maintaining the syntax of the original GreenGate kit. The primary limitations MultiGreen addresses are 1) multiplexing in series, 2) multiplexing in parallel, and 3) repeated cycling of transcriptional unit assembly through binary intermediates. MultiGreen efficiently concatenates bespoke transcriptional units using an additional suite of level 1acceptor vectors which serve as an assembly point for individual transcriptional units prior to final, level 2, condensation of multiple transcriptional units. Assembly with MultiGreen level 1 vectors scales at a maximal rate of 2*⌈ log 6 n ⌉+3 days per assembly, where n represents the number of transcriptional units. Further, MultiGreen level 1 acceptor vectors are binary vectors and can be used directly for plant transformation to further maximize prototyping speed. MultiGreen is a 1:1 expansion of the original GreenGate architecture’s grammar and has been demonstrated to efficiently assemble plasmids with multiple transcriptional units. MultiGreen has been validated by using a truncated violacein operon from Chromobacterium violaceum in bacteria and by deconstructing the RUBY reporter for in planta functional validation. MultiGreen currently supports many of our in-house multi transcriptional unit assemblies and will be a valuable strategy for more complex cloning projects.

Science & Technology - Other Topics↗
DOE OSTI2017

Computational fluid dynamics simulations of a binary particle bed in a riser-based carbon stripper for chemical looping combustion

Chemical-looping combustion (CLC) is a next generation combustion technology that shows great promise as a solution for the need of high-efficiency low-cost carbon capture from fossil fueled power plants. In this paper, numerical simulations are conducted of a binary particle bed associated with a coal-direct CLC system consisting of coal (represented by plastic beads) and oxygen carrier particles and validated against an experimental riser-based carbon stripper. The detailed particle dynamics and solid-gas and solid-solid interactions are investigated using the Lagrangian particle-tracking approach known as the discrete element method coupled with the computational fluid dynamics solution for the flow field. The simulation results of the fluidization behavior and the separation ratio of the particles are in excellent agreement with the experiment. In conclusion, a credible simulation of a binary particle bed is of particular importance for understanding the details of the fluidization behavior; the baseline simulation established in this work can be used as a tool for designing and optimizing the performance of such systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Posiform planting: generating QUBO instances for benchmarking

We are interested in benchmarking both quantum annealing and classical algorithms for minimizing quadratic unconstrained binary optimization (QUBO) problems. Such problems are NP-hard in general, implying that the exact minima of randomly generated instances are hard to find and thus typically unknown. While brute forcing smaller instances is possible, such instances are typically not interesting due to being too easy for both quantum and classical algorithms. In this contribution, we propose a novel method, called posiform planting , for generating random QUBO instances of arbitrary size with known optimal solutions, and use those instances to benchmark the sampling quality of four D-Wave quantum annealers utilizing different interconnection structures (Chimera, Pegasus, and Zephyr hardware graphs) and the simulated annealing algorithm. Posiform planting differs from many existing methods in two key ways. It ensures the uniqueness of the planted optimal solution, thus avoiding groundstate degeneracy, and it enables the generation of QUBOs that are tailored to a given hardware connectivity structure, provided that the connectivity is not too sparse. Posiform planted QUBOs are a type of 2-SAT boolean satisfiability combinatorial optimization problems. Our experiments demonstrate the capability of the D-Wave quantum annealers to sample the optimal planted solution of combinatorial optimization problems with up to 5, 627 qubits.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

The MELCOR Plot File Format

This work provides a detailed explanation of the MELCOR Plot File format. MELCOR is a fully integrated, engineering-level computer code that models the progression of severe accidents in light water reactor nuclear power plants, and it primarily exports its simulation data to a binary file in the Plot File format. This work documents the Plot File's overall structure and the data types of exported information. The process of interpreting and associating time series information with MELCOR's Plot Variables for complete time histories is also presented. The format and meaning of time-independent information, called Special information, is also discussed.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Nested Pebble Bed Blanket (NesPeB)

Recent advances in magnetic confinement fusion technology have attracted billions of dollars of investments in startups from venture capitals and corporations, resulting in the development of devices aiming to demonstrate net energy gain in a self-heated burning plasma, such as SPARC (under construction) and others. However, future fusion power plants must operate in regimes that will require technologies far beyond current experience. According to a National Academies of Science, Engineering, and Medicine report, to have nuclear fusion power plants contributing in a timely manner to the planned reduction of atmospheric carbon dioxide, a pilot plant should be built by 2035, and it should demonstrate fusion power production and the performance of the tritium fuel system (requiring a high enough tritium breeding) by 2040. A recognized key technology gap by [26] is the fusion first wall and blanket since no current blanket concept is considered satisfactory or has been built and proven. The first wall and blanket in magnetic fusion reactors form a vital and complex system, as it must satisfy different functions such as power extraction, tritium breeding, plasma containment, radiation shielding, and safety. The list of design requirements is even longer: high enough tritium production for fusion self-sufficiency, low material activation, decay heat and shutdown dose rates, high thermal efficiency, high-capacity factor, high magnets-divertor-vacuum vessel-first wall life, low corrosion, low cost, and intrinsically safe (requiring minimal licensing). Despite fifty-plus years of research, the first wall and blanket concepts proposed suffer from fundamental technical problems and immaturity (TRL=2-3) that jeopardize the timely delivery of a commercial fusion power plant. A fusion first-wall blanket has never been built nor tested, and a "winning", practical functioning design requires enough engineering margins (high enough tritium breeding considering the uncertainty, etc.), manufacturing simplicity, ease of continuous operation, maintenance, and low cost. A new, groundbreaking blanket concept called "Nested Pebble Bed Blanket" (NesPeB) was developed at ORNL under the successful ARPA-E GAMOW FERMI project (patent application allowed by the USPTO). The NesPeB blanket concept addresses current blanket concepts' shortcomings and technical immaturity, paving the way for accelerated delivery of fusion power plants. NesPeB is based on nested pebbles, which are binary-sized lithium-ceramic pebbles enclosed in "Beryllide" perforated and coated spherical shells, which are also binary-sized, stacked on top of each other, forming a "bed" and cooled by Nitrogen gas also "sweeping" the Helium and Tritium generated by the neutron irradiation of Lithium; the vacuum vessel plasma facing material is Molybdenum-96 and -97 with the first wall cooled by Helium while the divertor armor is made of Tungsten. The simulations of the NesPeB blanket using Fusion Reactors Models Integrator (FERMI) are encouraging as they estimate a tritium breeding ratio (TBR) greater than 1.2 using natural Lithium, acceptable pressure drop, and excellent heat transfer properties. Furthermore, the NesPeB blanket is not limited by magneto-hydro-dynamics (MHD) effects, is designed for online refueling, relies on existing tritium extraction technologies, has a simple construction, and limits the corrosion and chemical reactivity problems. NesPeB has the potential to be transformational and disruptive since it can solve all the main, challenging technical problems of fusion device blankets and accelerate a pilot plant delivery for 10 or more years.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1975

Liquid-metal binary cycles for stationary power

The use of topping cycles to increase electric power plant efficiency is discussed, with particular attention to mercury and alkali metal Rankine cycle systems that could be considered for topping cycle applications. An overview of this technology, possible system applications, the required development, and possible problem areas is presented.

Gutstein, M.↗
DOE OSTI2020

US Geothermal Electricity and Heat Market Analysis and Report

The power production data from FY19 is provided for U.S. geothermal power plants. The spreadsheet includes the plant name and type, nameplate capacity, summer capacity, winter capacity, and net generation for each power plant.

15 GEOTHERMAL ENERGY↗
NASA NTRS1991

Stochastic robustness

To carry out stochastic robustness analysis, an expected probability distribution is assigned to each uncertain parameter in the system. The Monte Carlo analysis proceeds by repeatedly assigning shaped random values to each plant parameter, evaluating the stability of performance metric, and performing the binary classification (stable/unstable, etc.). If the system is stable, the state response to a unit disturbance impulse can be propagated to establish whether the response would violate settling time envelopes and whether peak actuator use would violate predetermined maximums. The final estimates of the probability of each form of unacceptable behavior are found by dividing the number of cases in which the overall system had that form of unacceptability by the number of cases run. Stability robustness can be portrayed graphically using the stochastic root locus and by using histograms of parameter values found in the unacceptable cases.

Marrison, C.↗
DOE OSTI2016

Use of Agrobacterium rhizogenes strain 18r12v and paromomycin selection for transformation of Brachypodium distachyon and Brachypodium sylvaticum

In this study, the genetic transformation of monocot grasses is a resource intensive process, the quality and efficiency of which is dependent in part upon the method of DNA introduction, as well as the ability to effectively separate transformed from wildtype tissue. Agrobacterium -mediated transformation of Brachypodium has relied mainly on Agrobacterium tumefaciens strain AGL1. Currently the antibiotic hygromycin B has been the selective agent of choice for robust identification of transgenic calli in Brachypodium distachyon and Brachypodium sylvaticum but few other chemicals have been shown to work as well for selection of transgenic Brachypodium cells in tissue culture. This study demonstrates that Agrobacterium rhizogenes strain 18r12v and paromomycin selection can be successfully used for the efficient generation of transgenic B. distachyon and B. sylvaticurn . Additionally we observed that the transformation rates were similar to or higher than those obtained with A. turnefaciens strain AGL1 and hygromycin selection. The A. rhizogenes strain 18r12v harboring the pARS1 binary vector and paromomycin selection is an effective means of generating transgenic Brachypodium plants. This novel approach will facilitate the transgenic complementation of T-DNA knockout mutants of B. distachyon which were created using hygromycin selection, as well as aid the implementation of more complex genome manipulation strategies which require multiple rounds of transformation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Evaluation of artificial neural network performance for classification of potato plants infected with potato virus Y using spectral data on multiple varieties and genotypes

Potato virus Y (Potyviridae, PVY) is a plant virus that poses a significant threat to potato producers on a global basis. The pathogen has disrupted seed potato supplies and negatively impacted yield and quality of commercial potato crops. The potato industry currently manages PVY infection levels via insecticide applications, regional seed certification programs that rely on field scouting to visually assess individual plants for infection status, and destructive and costly tissue sampling coupled with laboratory assays. Despite these efforts, PVY continues to confound potato industry stakeholders resulting in economic harm. Remote sensing and machine learning provide for the development of new tools to more accurately detect and spatially quantify PVY-infected plants versus the current state of the art. However, there is a need to understand how the occurrence of many different potato varieties impact the dynamics of developing models to detect potato plants impacted with PVY and their potential effectiveness. This study evaluates classification modelling outcomes using spectral datasets collected in different temporal and spatial environments (greenhouse and a production field) on multiple potato varieties consisting of labelled instances of plants infected with PVY and those not infected with the virus. A modelling framework was developed to support iterative modelling runs using artificial neural network (ANN) architectures configured as binary classifiers to develop sample populations to support statistical analysis on model performance using specific spectral subsets. When using spectral data to detect PVY-infected plants, ANN models achieved the highest mean accuracy of 0.894 on a single variety. Conversely, the same ANN model architecture only achieved a mean accuracy of 0.575 on a spectral data set representing 29 potato breeding lines. Additionally, statistical analysis indicates spectral regions including the red edge, near infrared and shortwave infrared contain more important spectral features for the ANN classifier introduced in this research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Stillwater Hybrid Geo-Solar Power Plant Optimization Analyses

The Stillwater Power Plant is the first hybrid plant in the world able to bring together a medium-enthalpy geothermal unit with solar thermal and solar photovoltaic systems. Solar field and power plant models have been developed to predict the performance of the Stillwater geothermal / solar-thermal hybrid power plant. The models have been validated using operational data from the Stillwater plant. A preliminary effort to optimize performance of the Stillwater hybrid plant using optical characterization of the solar field has been completed. The Stillwater solar field optical characterization involved measurement of mirror reflectance, mirror slope error, and receiver position error. The measurements indicate that the solar field may generate 9% less energy than the design value if an appropriate tracking offset is not employed. A perfect tracking offset algorithm may be able to boost the solar field performance by about 15%. The validated Stillwater hybrid plant models were used to evaluate hybrid plant operating strategies including turbine IGV position optimization, ACC fan speed and turbine IGV position optimization, turbine inlet entropy control using optimization of multiple process variables, and mixed working fluid substitution. The hybrid plant models predict that each of these operating strategies could increase net power generation relative to the baseline Stillwater hybrid plant operations.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Approaches for Water Removal in Direct-Fired sCO 2 Power Cycles

There is interest in investigation of water removal processes in direct fired sCO 2 flows, as this may potentially lead to greater system efficiency as the removal of this contaminant will result in sCO 2 behaving close to idealized behaviors. Water removal should be split into a two-step process, condensation of the water, followed by separation of the liquid phase water from the sCO 2 . The two main avenues of condensation are manipulation of pressure and temperature for phase change. For this paper, temperature-based phase change is the primary focus through the implementation of heat exchangers. Of the heat exchangers investigated it was found that printed circuit heat exchangers (PCHEs) could be an alternative for this use case, though the specific design of flow channel geometry and flow direction depends on the specific system case and cannot be determined at this point. For water separation there were four processes identified, all of which already assume water is in liquid phase at that point in the system. Of these separation avenues the best candidate is the hydrocyclone as it has a proven history of separating liquid-liquid phase mixtures with small density differences in oilfield use, in addition they have been investigated and modeled specifically for water separation for sCO 2 flows and the footprint is relatively small.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS1978

High temperature solar photon engines

High temperature heat engines designed to make maximum use of the thermodynamic potential of concentrated solar radiation are described. Plasmas between 2000 K and 4000 K can be achieved by volumetric absorption of radiation in alkali metal vapors, leading to thermal efficiencies up to 75% for terrestrial solar power plants and up to 50% for space power plants. Two machines capable of expanding hot plasmas using practical technology are discussed. A binary Rankine cycle uses fluid mechanical energy transfer in a device known as the 'Comprex' or 'energy exchanger.' The second machine utilizes magnetohydrodynamics in a Brayton cycle for space applications. Absorption of solar energy and plasma radiation losses are investigated for a solar superheater using potassium vapor.

Hertzberg, A.↗
DOE OSTI2020

Biomass Production of Prairie Cordgrass (Spartina pectinata Link.) Using Urea and Kura Clover (Trifolium ambiguum Bieb.) as a Source of Nitrogen

Abstract Optimizing nitrogen (N) management is an important factor for sustainable perennial biomass systems. However, N application is costly, both financially and environmentally. Our objectives were to determine: (1) N rate and plant spacing effects on yield and yield components of prairie cordgrass swards and (2) fertilizer N replacement value (FNRV) of kura clover in prairie cordgrass-kura clover binary mixtures. Plots were established in Illinois, Minnesota, South Dakota, and Wisconsin, USA, in 2010. Kura clover was transplanted on 30-cm centers in all treatments in which it was a component; prairie cordgrass seedlings were transplanted within the kura clover on 60- and 90-cm centers. Monoculture prairie cordgrass stands were established at the same population densities of mixed stands and fertilized with 0, 75, 150, or 225 kg N ha -1 . Biomass was harvested in the autumn from 2011 to 2013. N (urea), year, plant spacing, and year × plant spacing affected prairie cordgrass production at all locations. Prairie cordgrass yield increased with N application, but the response varied by location. N application tended to increase prairie cordgrass tiller density and consistently increased tiller mass. Prairie cordgrass yield with 0 N was equal to or less than the yield of prairie cordgrass/kura clover mixtures at all locations in 2011 and 2012; however, kura clover provided a FNRV of 25–82 kg N ha -1 to prairie cordgrass in 2013. Kura clover has potential to provide N to prairie cordgrass in binary mixtures of these two species and on land that may not be easily farmed due to wetness.

Energy & Fuels↗
DOE OSTI2022

PRMI: A Dataset of Minirhizotron Images for Diverse Plant Root Study

Understanding a plant's root system architecture (RSA) is crucial for a variety of plant science problem domains including sustainability and climate adaptation. Minirhizotron (MR) technology is a widely-used approach for phenotyping RSA non-destructively by capturing root imagery over time. Precisely segmenting roots from the soil in MR imagery is a critical step in studying RSA features. In this paper, we introduce a large-scale dataset of plant root images captured by MR technology. In total, there are over 72K RGB root images across six different species including cotton, papaya, peanut, sesame, sunflower, and switchgrass in the dataset. The images span a variety of conditions including varied root age, root structures, soil types, and depths under the soil surface. All of the images have been annotated with weak image-level labels indicating whether each image contains roots or not. The image-level labels can be used to support weakly supervised learning in plant root segmentation tasks. In addition, 63K images have been manually annotated to generate pixel-level binary masks indicating whether each pixel corresponds to root or not. These pixel-level binary masks can be used as ground truth for supervised learning in semantic segmentation tasks. By introducing this dataset, we aim to facilitate the automatic segmentation of roots and the research of RSA with deep learning and other image analysis algorithms.

Xu, Weihuang↗
DOE OSTI2016

Conditional Tree Reduction in the ADAPT Framework

Probabilistic Risk Assessment (PRA) uses fault tree/event tree analysis to evaluate the environmental impacts of nuclear power plants due to internal or external initiating events. In a fault tree, basic failure events are assembled using primarily And/Or logic to determine the combinations of failures that lead to failure of the system as a whole. An event tree is forward-facing, and begins with a single initiating event. From there the analysis considers what event may occur next, and branches out among the possible configurations of occurrence and non-occurrence of that event. This branching continues until end states are reached. End states may include a safe and stable configuration of the plant, or any number of differing failure states. Both fault and event trees require basic event probabilities as inputs to provide insight on the likelihood of different outcomes. PRA has been applied to nuclear power plants as an analysis tool since the 1975 Reactor Safety Study. In a traditional PRA, the order of events is prescribed by the analyst and each event is typically a binary aleatory uncertainty: occurrence or non-occurrence. Discrete dynamic event tree (DDET) analysis eliminates this subjectivity in ordering of events by using the output of a dynamic system model (simulator) to inform the branching. Branching conditions are triggered by the existence of a relevant plant state in the code, and therefore only occur as physically appropriate. The DDET approach also allows consideration of both epistemic and aleatory uncertainties on a phenomenologically and stochastically consistent platform. A DDET may be orders of magnitude larger than a traditional event tree for the same initiating event, as more of the uncertainty space is likely to be explored and the tree does not require manual assembly. Because of the size, manual inspection of the entire DDET is often infeasible. This problem may be approached from the front by reducing the number of sampled branching values or by online pruning of the tree, thus reducing the total number of branches. Utilizing post-processing techniques to draw actionable insights via clustering has also been proposed. One post-processing tool to reduce the DDET according to user-defined rules has been recently implemented in the Analysis of Dynamic Accident Progression Trees (ADAPT) DDET driver code. The tool creates a 'slice' of the DDET comprised of the set of branches that meet a set of user-input rules, for immediate visualization or for further calculations conditional on the rules being met. This extension is expected to ease analysis of the results of large and complex DDETs, leading to greater applicability of DDETs in PRA. It should be emphasized that this tool is a visualization aide and does not impact the theoretical basis of the DDET approach. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗