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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Design and implementation of a data analysis pipeline for paper spray ionization mass spectrometry of UO 2 Cl 2

Ambient mass spectrometry is an emerging characterization method for nuclear materials analysis. SRNL has recently acquired a new JEOL AccuTOF™-DART® LP 4G with a Filter Spray™ front end sample injection system, to develop new methods for ambient mass spectrometry of uranium analytes. Preliminary measurements of uranium-containing samples have demonstrated a need to rapidly evaluate large datasets generated with tailored data analysis scripts. To address this gap a data analysis pipeline written in R within the R Studio environment has been developed. The pipeline comprises a series of processing steps where the data are ingested, analyte peaks of interest are extracted, subsequent ratio and normalization calculations are performed, and results are presented in a series of graphical plots. In detail, the data files containing up to several hundred chronological spectra of a single sample in netCDF format were first extracted into RStudio. Then, initial 3D plots of the data were created in respect to the time, intensity, and mass to charge domains. These graphics serve as an initial easily visualized survey of instrument data. Next, a .csv format file containing the names, masses, and abundances of isotopes of interest were ingested into RStudio and were used to automatically collect intensity values at the mass value. Uranium isotope ratios were plotted against gadolinium isotope ratios (gadolinium was used as an internal standard) and assessed for ion beam stability and intensity. The spectra were filtered by applying a threshold intensity for the 158GdO+ mass channel. A Monte Carlo experiment was performed by creating a theoretical distribution of the data in order to determine the ratio of the uranium isotopes. The results show that the developed data analysis pipeline successfully filters high quality data from overall poor quality runs. Our results also support the idea that paper spray ionization mass spectrometry can be used for both detection and quantification of trace amounts of uranium compounds.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

The Consortium for Advanced Sorghum Phenomics (CASP). Final report

The goal of CASP was to accelerate breeding of biomass sorghum [Sorghum bicolor (L.) Moench] by identifying genotypes exhibiting high yield under well-watered, pre- or post-drought and/or salinity-stress conditions. We did this by combining high-throughput, non-invasive drone phenotyping with genomics and molecular profiling. Field-based phenotyping utilized a multi-modal sensor suite of LiDAR, multispectral cameras, and thermal cameras mounted on a commercial drone to detect traits required for yield prediction and selection of drought and saline tolerant lines of sorghum. Traits of interest included plant height (PH), leaf area index (LAI), wet biomass (BMW), and biomass at 65% moisture (BM65) and were measured from emergence to harvest on a weekly basis over three growing seasons. The final output were measurements of traits on a plot-by-plot basis, identified by the plot ID used by the Proprietary data processing software enabled raw field data to be turned into plant traits and delivered to the PNNL and JGI within the same workday.

09 BIOMASS FUELS↗
DOE OSTI2019

Brief Guide on Outputting Custom Variables in SABLE

Although SABLE supports a wide variety of variable outputs as a default, the code currently does not readily support outputs of custom variables via the 'plot variables' feature in its input file. Workarounds to this issue exist (such as using `spy'), but the distinct convenience and advantage that the 'plot variables' feature provides, such as easy output of results in .exo file format, disappear. The user may explore the source code to correct this issue, but due to the ongoing nature of code building and complexity of the code spanning multiple languages and layers, exploration and correction of the code can be prohibitively difficult. In this memo, a short guide to modify the code to output desired custom variables via 'plot variables' is listed along with an example variable that evaluates the velocity profile before remapping (VELOCITY PREMAP).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Application of a machine learning algorithm (XGBoost) to offline RHIC luminosity optimization

The operation parameter optimization in 2020 RHIC low energy run is difficult. First, the RHIC luminosity is affected by many RHIC operation parameters, as well as affected by many Low Energy RHIC electron Cooling (LEReC) operation parameters. Second, the luminosity signal in this run is noisy and not sensitive to these parameter changes, especially when these parameters are very close to their optimized values. It is not easy to distinguish the effects of one parameter from all other operation parameters separately. Therefore, it is difficult to optimize the luminosity by varying these parameters one by one. To find a way for luminosity optimization, we analyze some operation parameters via a machine learning algorithm - XGBoost. After constructing a black-box surrogate model from XGBoost and plotting their partial dependency plots (PDF) and SHAP value plots for different operation parameters, we can find the effects of an individual parameter on the RHIC luminosity and optimize it accordingly.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Data set for Conventional Rib Turbulators for SCO2 Turbine Internal Cooling

This data set accompanies NETL Internal Report DOE/NETL-2021/2758. M. Searle, D. Straub, and J. Black, “Conventional Rib Turbulators For SCO2 Turbine Internal Cooling,” National Energy Technology Laboratory, Morgantown, June 28, 2021. The "Configuration" tab details the test configuration geometry and nominal set points. The "Exp point data" tab reports the thermal performance at the midpoint of the Wilson plot. The "Wilson plot data" tab are all Wilson resistance plots reported in the present work.

42 ENGINEERING↗
DOE OSTI2022

Classification of Orbits in Poincare Maps Using Machine Learning

Poincare plots, also called Poincare maps, are used by plasma physicists to understand the behavior of magnetically confined plasma in numerical simulations of a tokamak. These plots are created by the intersection of field lines with a two-dimensional poloidal plane that is perpendicular to the axis of the torus representing the tokamak. A plot is composed of multiple orbits, each created by a different field line as it goes around the torus. Each orbit can have one of four distinct shapes, or classes, that indicate changes in the topology of the magnetic fields confining the plasma. Given the (x, y) coordinates of the points that form an orbit, the analysis task is to assign a class to the orbit, a task that appears ideally suited for a machine learning approach. In this paper, we describe how we overcame two major challenges in solving this problem - creating a high-quality training set, with few mislabeled orbits, and converting the coordinates of the points into features that are discriminating, despite the variation within the orbits of a class and the apparent similarities between orbits of different classes. Our automated approach is not only more objective and accurate than visual classification, but is also less tedious, making it easier for plasma physicists to analyze the topology of magnetic fields from numerical simulations of the tokamak.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Neural MUSE Analysis

Researchers at Oak Ridge National Laboratory (ORNL) created data as part of the MUSE (Multi-Agency Urban Search Experiment Detector and Algorithm Test Bed) project simulating illicit nuclear materials located in various buildings along a road. In the simulation, a truck containing a radiation detector drives down the road gathering listmode data (counting the and energy of incident gamma radiation). Building materials, source shielding, driving speed, truck direction, truck location on the road, source type, and source placement are all varied between runs of the data set. This data was created using deterministic neutron transport and Monte Carlo methods through a combination of SCALE, MAVRIC, MCNP, and GADRAS. As part of a follow-on NA-22 project, two Kaggle competitions were created to determine the best algorithms for finding and identifying gamma sources in this simulated urban environment. The winning algorithm was neural network-based and had a test accuracy of 76.4% accuracy for source identification. This work seeks to build upon this work and improve the results through the application of novel machine learning techniques. As a first step, the data was classified by a simple Convolutional Neural Network (CNN) To accomplish this, the data was first preprocessed into “waterfall plots.” These plots are composed of energy vs count plots that are stacked vertically to show progression in time. The horizontal axis indicating the particle energy incorporated user defined bin spacing with options for in linear-, logarithmic-, square root-, and user-spaced bins. The z or color dimension showed the number of counts corresponding the energy-time combination. This data was then used to generate more data, by generating a local estimate of the mean of the distribution for a bin and then randomly re-sampling that bin from a Poisson distribution. Once all of this data was generated, it was fed into a well-known CNN architecture, ResNet50. The output layer of this model was removed and replaced with layers corresponding to the shape desired isotope outputs. The provided training data was used to train the classifier and the remaining testing data was used to evaluate the model. Results are soon to be forthcoming.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2024

Effects of hurricane disturbance and increased temperature on carbon cycling and storage of a Puerto Rican forest: a mechanistic investigation of above- and belowground processes (Final Technical Report)

The overall goal of the Tropical Responses for Altered Climate Experiment (TRACE) is to assess the effects of increasing temperature on tropical plant and soil carbon fluxes and storage as the forest recovers from major hurricane disturbance that occurred in September 2017. Ultimately, we aimed to reduce uncertainty and increase confidence with which tropical forests are represented in Earth System models to make more accurate global forecasts of future climate. We focused on both above- and belowground processes and explored temperature controls over critical aspects of carbon and nutrient cycling for tropical plants, soil, and microbes. TRACE is located in a wet tropical forest in the Luquillo Experimental Forest close to the USDA Forest Service Sabana Field Research Station in Luquillo, Puerto Rico. The warming treatment consists of six 4.7 m diameter plots. Three of the plots receive infrared warming and three have the same infrastructure but are not warmed (using ‘dummy’ heaters). Each plot was monitored from 2018-2023 to investigate two major questions: 1) Are there legacy effects of prior warming on forest recovery following hurricane disturbance? 2) Will the trajectory of forest recovery following disturbance be affected by warmer temperatures? Concurrent soil incubation experiments were conducted to enable more controlled mechanistic investigations of temperature response on microbial function. In sum, our goal was to use this novel climate manipulation experiment (the only of its kind in any tropical forest) and once-in-a-lifetime chance to assess how temperature and hurricane disturbance interact to affect coupled biogeochemical cycling in a tropical forest.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

EDXplorer: A Utility for APA Analysis

The automated particle analysis (APA) method of scanning electron microscopy (SEM) energy dispersive X-ray spectroscopy (EDS/EDX) is a useful tool for analyzing the elemental and morphological data of particulate samples. Often, such datasets have many thousands of particles, and it can be difficult to sift through the data to find meaningful trends. EDXplorer is a software utility for processing APA data. They enable the user to easily load data and determine the important components and aspects of the datasets using a powerful and versatile library of data plotting functions, mainly centered around scatter plots and histograms. These programs are intended to fill a void in the data processing of data from certain instruments, where often the user must rely on their own code or other software that is not user-friendly. EDXplorer is intended as a general plotting utility for browsing through data and discovering data trends.

Moseley, Duncan [ORNL] (ORCID:0000000343518347)↗
DOE OSTI2018

SPRUCE Bog Surface Elevation Assessments with SPRUCE Elevation Transect Instrument, Marcell Experimental Forest, Minnesota, 2013-2025

This data set reports plot-specific bog surface elevation measurements collected with the SPRUCE Elevation Transect (SET) instrument at the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experimental site within the Marcell Experimental Forest in northern Minnesota, USA. The SET instrument is a modified version of the Cahoon et al. (2002) surface elevation table instrument. The device is mounted to fixed stands that are anchored in deep sediments, making it capable of identifying subtle changes in bog surface elevation over time. Measurements are reported as absolute elevation in meters above mean sea level (amsl) for two locations in each of the SPRUCE experimental treatment plots and additional ambient boardwalk plots: 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 19, 20 and 21. Measurements were collected from June 2013 through September 2025 (2013-06-02 to 2025-09-08). Changes in bog surface elevation may indicate alterations in the carbon and water cycles due to changing environmental conditions. This data set contains one data file in comma separate (*.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma separate (.csv) format and a user guide in PDF (*.pdf) format.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

SPRUCE Phospholipid Fatty Acid (PLFA) Abundances, August 2021-June 2022

This data set provides the results for phospholipid fatty acid analysis (PLFA) of peat samples collected from ambient plots in the SPRUCE experiment site, with experimental plot samples still being processed. The samples used to generate this data set were collected on 2021-08-23 and 2022-06-21. This data set includes abundances for groups of lipids indicating total biomass, fungi, Gram-positive bacteria, Gram-negative bacteria, actinomycetes, and anaerobic bacteria. This data set contains one file in comma separate (*.csv) format. Samples: On each sampling date, cores extending to 200 cm were collected and subsampled at 10 cm intervals from 0 and 100 cm and 25 cm intervals below 100 cm depth in Plot 7 and 21. During the lipid extraction procedure, samples were pooled into the following depth increments in order to obtain sufficient material to achieve adequate lipid yield: 0-10, 10-20, 20-30, 30-50, 50-100, and 100-150, and 150-200 cm.

actinomycetes↗
DOE OSTI2023

SPRUCE Anaerobic oxidation of methane mitigates net methane production and responds to long-term experimental warming in a northern bog: Supporting data

This data set contains measurements of anaerobic oxidation of methane (AOM), net methane (CH4) production, carbon dioxide (CO2) production, and gross CH4 production measurements from the SPRUCE experiment (Hanson et al. 2017). CH4 is greenhouse gas with 45 times the sustained global warming potential of CO2 over 100 years, which may have significant implications on global climate change. The process of AOM plays a large role in the extent of CH4 emissions from wetland environments like those at the SPRUCE experiment site. This study conducted two on different dates. In experiment 1, net CH4 and CO2 production were measured with a gas chromatograph through the peat profile at in situ temperatures of the SPRUCE plots four times from May to October, 2016 (2016-05 to 2016-10). Two treatments were examined, a Porewater Treatment that was a 1:1 volumetric peat:porewater slurry and a No Porewater Treatment that was field-moist peat with no porewater added. Additionally, net CH4 production were measured at frequent intervals (from 4 hr to 9 d) in peat from selected SPRUCE plots from soil collected on October 16, 2016 (2016-10-16). Samples were collected from the SPRUCE plots, immediately made anaerobic, and transported to the University of Oregon for rate measurements within several days of collection. In experiment 2, AOM, net CH4 production, CO2 production, and gross CH4 production were measured through the peat profile at in situ field temperatures once from samples collected in July, 2021 (2021-07). The same two porewater treatments were examined. AOM was measured with a 13CH4 tracer method using a Picarro G2201-i analyzer for isotopic CO2/CH4 quantification with a small stable isotope module attached. This data set contains three data files in comma separate (*.csv) format. This data set is associated with the following publication: Barney, M, AM Hopple, LL Gregory, JK Keller, and SD Bridgham. 2024. Anaerobic oxidation of methane mitigates net methane production and responds to long-term experimental warming in a northern bog. Soil Biology and Biochemistry, 190, 109316. https://doi.org/10.1016/j.soilbio.2024.109316.

SPRUCE Experiment, Marcell Experimental Forest, Me↗
DOE OSTI2019

Legacy effects of long-term CO2 enrichment on plant biomass recovery from fire seven years after return to ambient CO2 levels1

We sought to determine if elevated CO2 resulted in long-lasting changes to plant structure and function, particularly disturbance response patterns. Minimal legacy effects were observed 1 yr after the termination of a previous long-term experiment; however, those observations were based on 1 yr of data and only focused on fine root growth, herbivory, and arthropod diversity. We examined above- and belowground biomass inside the footprints of chambers from a former long-term elevated CO2 experiment in a scrub-oak community 7 yr after CO2 enrichment ceased and 2 yr after fire. Aboveground biomass was 40.6% higher in the previously elevated plots compared with ambient plots, suggesting that there are legacy effects in the form of more rapid aboveground recovery from fire on previously elevated CO2 plots. Belowground biomass exhibited minimal change since the historic CO2 enrichment, suggesting that differences were a result of the original treatment. After disturbance, regrowth occurs via sprouting from large belowground structures; thus changes in aboveground recovery are likely due to changes to belowground biomass caused by altered atmospheric CO2. Elevated CO2 has significantly affected disturbance recovery patterns in this community. These changes have the potential to alter plant carbon allocation patterns in frequently disturbed systems.

Plant Sciences↗
DOE OSTI2021

The Transcriptional Response of Soil Bacteria to Long-Term Warming and Short-Term Seasonal Fluctuations in a Terrestrial Forest

Terrestrial ecosystems are an important carbon store, and this carbon is vulnerable to microbial degradation with climate warming. After 30 years of experimental warming, carbon stocks in a temperate mixed deciduous forest were observed to be reduced by 30% in the heated plots relative to the controls. In addition, soil respiration was seasonal, as was the warming treatment effect. We therefore hypothesized that long-term warming will have higher expressions of genes related to carbohydrate and lipid metabolism due to increased utilization of recalcitrant carbon pools compared to controls. Because of the seasonal effect of soil respiration and the warming treatment, we further hypothesized that these patterns will be seasonal. We used RNA sequencing to show how the microbial community responds to long-term warming (~30 years) in Harvard Forest, MA. Total RNA was extracted from mineral and organic soil types from two treatment plots (+5°C heated and ambient control), at two time points (June and October) and sequenced using Illumina NextSeq technology. Treatment had a larger effect size on KEGG annotated transcripts than on CAZymes, while soil types more strongly affected CAZymes than KEGG annotated transcripts, though effect sizes overall were small. Although, warming showed a small effect on overall CAZymes expression, several carbohydrate-associated enzymes showed increased expression in heated soils (~68% of all differentially expressed transcripts). Further, exploratory analysis using an unconstrained method showed increased abundances of enzymes related to polysaccharide and lipid metabolism and decomposition in heated soils. Compared to long-term warming, we detected a relatively small effect of seasonal variation on community gene expression. Together, these results indicate that the higher carbohydrate degrading potential of bacteria in heated plots can possibly accelerate a self-reinforcing carbon cycle-temperature feedback in a warming climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Experimental Warming Changes Phenology and Shortens Growing Season of the Dominant Invasive Plant Bromus tectorum (Cheatgrass)

Bromus tectorum (cheatgrass) has successfully invaded and established throughout the western United States. Bromus tectorum grows early in the season and this early growth allows B. tectorum to outcompete native species, which has led to dramatic shifts in ecosystem function and plant community composition after B. tectorum invades. If the phenology of native species is unable to track changing climate as effectively as B. tectorum’s phenology then climate change may facilitate further invasion. To better understand how B. tectorum phenology will respond to future climate, we tracked the timing of B. tectorum germination, flowering, and senescence over a decade in three in situ climate manipulation experiments with treatments that increased temperatures (2°C and 4°C above ambient), altered precipitation regimes, or applied a combination of each. Linear mixed-effects models were used to analyze treatment effects on the timing of germination, flowering, senescence, and on the length of the vegetative growing season (time from germination to flowering) in each experiment. Altered precipitation treatments were only applied in early years of the study and neither precipitation treatments nor the treatments’ legacies significantly affected B. tectorum phenology. The timing of germination did not significantly vary between any warming treatments and their respective ambient plots. However, plots that were warmed had advances in the timing of B. tectorum flowering and senescence, as well as shorter vegetative growing seasons. The phenological advances caused by warming increased with increasing degrees of experimental warming. The greatest differences between warmed and ambient plots were seen in the length of the vegetative growing season, which was shortened by approximately 12 and 7 days in the +4°C and +2°C warming levels, respectively. The effects of experimental warming were small compared to the effects of interannual climate variation, suggesting that interactive controls and the timing of multiple climatic factors are important in determining B. tectorum phenology. Taken together, these results help elucidate how B. tectorum phenology may respond to future climate, increasing our predictive capacity for estimating when to time B. tectorum control efforts and how to more effectively manage this exotic annual grass.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Dry Season Transpiration and Soil Water Dynamics in the Central Amazon

With current observations and future projections of more intense and frequent droughts in the tropics, understanding the impact that extensive dry periods may have on tree and ecosystem-level transpiration and concurrent carbon uptake has become increasingly important. Here, we investigate paired soil and tree water extraction dynamics in an old-growth upland forest in central Amazonia during the 2018 dry season. Tree water use was assessed via radial patterns of sap flow in eight dominant canopy trees, each a different species with a range in diameter, height, and wood density. Paired multi-sensor soil moisture probes used to quantify volumetric water content dynamics and soil water extraction within the upper 100 cm were installed adjacent to six of those trees. To link depth-specific water extraction patterns to root distribution, fine root biomass was assessed through the soil profile to 235 cm. To scale tree water use to the plot level (stand transpiration), basal area was measured for all trees within a 5 m radius around each soil moisture probe. The sensitivity of tree transpiration to reduced precipitation varied by tree, with some increasing and some decreasing in water use during the dry period. Tree-level water use scaled with sapwood area, from 11 to 190 L per day. Stand level water use, based on multiple plots encompassing sap flow and adjacent trees, varied from ∼1.7 to 3.3 mm per day, increasing linearly with plot basal area. Soil water extraction was dependent on root biomass, which was dense at the surface (i.e., 45% in the upper 5 cm) and declined dramatically with depth. As the dry season progressed and the upper soil dried, soil water extraction shifted to deeper levels and model projections suggest that much of the water used during the month-long dry-down could be extracted from the upper 2–3 m. Results indicate variation in rates of soil water extraction across the research area and, temporally, through the soil profile. These results provide key information on whole-tree contributions to transpiration by canopy trees as water availability changes. In addition, information on simultaneous stand level dynamics of soil water extraction that can inform mechanistic models that project tropical forest response to drought.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Passive Acoustic Monitoring Provides Insights into Avian Use of Energycane Cropping Systems in Southern Florida

Birds are important indicators of ecosystem health and provide a range of benefits to society. It is important, therefore, to understand the impacts of agricultural land use changes on bird populations. The cultivation of energycane (EC)—a sugarcane hybrid—for biofuel production represents one form of agricultural land use change in southern Florida. We used passive acoustic monitoring (PAM) to examine bird community use of experimental EC fields and other agricultural land uses at two study sites in southern Florida. We deployed 16 acoustic recorders in different study plots and used the automatic species identifier BirdNET to identify 40 focal bird species. We found seasonal differences in daily avian species diversity and richness between EC experimental plots and reference agricultural fields (corn fields, orchards, pastureland), and between time periods (pre-planting, post-planting). Daily avian species diversity and richness were lower in the EC experimental plots during Fall and Winter months when plants reached maximum height (>400 cm in some areas). Despite seasonal differences in daily measures of species diversity and richness, we found no differences in cumulative species richness, suggesting that there may be little overall (season-long) effects of EC production. These findings could provide insight to avian seasonal habitat preferences and underscore the potential limitations of PAM in areas experiencing dynamic vegetation changes. More research is needed to better understand if utilization of EC cropping systems results in positive or negative effects on avian populations (e.g., foraging habitat quality, predator–prey dynamics, nest success).

BirdNET↗
DOE OSTI2017

SNaX: A Database of Supernova X-Ray Light Curves

We present the Supernova X-ray Database (SNaX), a compilation of the X-ray data from young supernovae (SNe). The database includes the X-ray fluxes and luminosities of young SNe, from days to years after outburst. The original goal and intent of this study was to present a database of Type IIn SNe (SNe IIn), which we have accomplished. Our ongoing goal is to expand the database to include all SNe for which published data are available. The database interface allows one to search for SNe using various criteria, plot all or selected data points, and download both the data and the plot. The plotting facility allows for significant customization. There is also a facility for the user to submit data that can be directly incorporated into the database. We include an option to fit the decay of any given SN light curve with a power-law. The database includes a conversion of most data points to a common 0.3–8 keV band so that SN light curves may be directly compared with each other. A mailing list has been set up to disseminate information about the database. We outline the structure and function of the database, describe its various features, and outline the plans for future expansion.

79 ASTRONOMY AND ASTROPHYSICS↗