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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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We propose a novel method for generating anisotropic adaptive Voronoi meshes that conforms to non-manifold curved boundaries. Our novel method modifies the sampling rules for the VoroCrust software to bring the VoroCrust seeds closer to the surface they are representing. This enables the reconstruction of two surfaces bounding a narrow region while filling the space in-between with stretched Voronoi cells.
The aim of this work was to explore the use of, and further develop, a in-situ and near-real time LIBS (Laser Induced Breakdown Spectroscopy) instrument to measure liquids relevant to the Hanford site. Specifically, we investigated the feasibility of using LIBS to measure the cation composition of HLW (High Level Waste) streams. In addition, we focused on enabling continuous interrogation of those liquids using the LIBS system. For the first part, we investigated simple (sodium nitrate) solutions as well as various Hanford tank waste surrogate slurries. We showed that relevant cations (such as sodium, aluminum, magnesium, potassium) can be measured via LIBS under good conditions but the results – both quantitative and qualitative (aka reproducibility) – strongly depend on the kind and concentration of the material used. The dependence of signal intensity on concentration was also confirmed by other measurements conducted (sodium chloride and sodium nitrate in water). For the second part, we investigated the influence of varying depth and concentration of a liquid sample. We observed that for the concentrations and fill depths investigated, the liquid fill depth did not impact the emission intensity of the analyte, while concentration is confirmed to have significant effects. The results of the sample depth experiments influenced testing of waste surrogates. In addition, we focused on further developing and adapting data fitting tools in order to efficiently, accurately, and reproducibly identify the elements and materials present in a LIBS spectrum.
Here we describe best practices for providing convenient, high-speed, secure access to large data via research data portals. We capture these best practices in a new design pattern, the Modern Research Data Portal, that disaggregates the traditional monolithic web-based data portal to achieve orders-of-magnitude increases in data transfer performance, support new deployment architectures that decouple control logic from data storage, and reduce development and operations costs. We introduce the design pattern; explain how it leverages high-performance Science DMZs and cloud-based data management services; review representative examples at research laboratories and universities, including both experimental facilities and supercomputer sites; describe how to leverage Python APIs for authentication, authorization, data transfer, and data sharing; and use coding examples to demonstrate how these APIs can be used to implement a range of research data portal capabilities. Sample code at a companion web site, https://docs.globus.org/mrdp, provides application skeletons that readers can adapt to realize their own research data portals.
We describe best practices for providing convenient, high-speed, secure access to large data via research data portals. Here, we capture these best practices in a new design pattern, the Modern Research Data Portal, that disaggregates the traditional monolithic web-based data portal to achieve orders-of-magnitude increases in data transfer performance, support new deployment architectures that decouple control logic from data storage, and reduce development and operations costs. We introduce the design pattern; explain how it leverages high-performance data enclaves and cloud-based data management services; review representative examples at research laboratories and universities, including both experimental facilities and supercomputer sites; describe how to leverage Python APIs for authentication, authorization, data transfer, and data sharing; and use coding examples to demonstrate how these APIs can be used to implement a range of research data portal capabilities. Sample code at a companion web site, https://docs.globus.org/mrdp , provides application skeletons that readers can adapt to realize their own research data portals.
In this case study, we will describe the design and assembly of a cyber security test range we have built at Oak Ridge National Laboratory in Oak Ridge, TN, USA. The range is designed to provide a flexible environment to evaluate cyber security tools—particularly those involving AI/ML—in a way that provides realistic environments and where we can control the experiments to determine the strengths and weaknesses of the tools. We have designed in the ability to repeat the evaluations, so additional tools can be evaluated and compared at a later time. The system is one that can be scaled up or down for experiment sizes. At the time of the conference we will have completed two full-scale, national, government challenges on this range. These challenges are evaluating the performance and operating costs for AI/ML-based cyber security tools for application into large, government-sized environments. These evaluations will be described, in order to provide motivation and context for various design decisions and adaptations we have made. The first challenge measured end-point security tools against 100K malware samples chosen across a range of types. The second is network detection of attempted penetration and exploitations with varying levels of covertness in a high-volume, business network. The scale of each of these challenges is requiring us to create automation systems to repeat the experiments identically for each tool. Preventing there being easy signs of malicious activity for the AI/ML tools to focus on has been a particularly interesting and challenging aspect of designing and executing these challenge events. After the events, the range continues to be used for other research such as adversarial machine learning where the repeatability, scale, and automation required for the national challenge events become essential elements for research.
Traditional trial-and-error methods for materials discovery are inefficient to meet the urgent demands posed by the rapid progression of climate change. This urgency has driven the increasing interest in integrating robotics and machine learning into materials research to accelerate experimental learning. However, idealized decision-making frameworks to achieve maximum sampling efficiency are not always compatible with high-throughput experimental workflows inside a laboratory. For multi-step chemical processes, differences in hardware capacities can complicate the digital framework by introducing constraints on the maximum number of samples in each step of the experiment, hence causing varying batch sizes in variable selection within the same batch. Therefore, designing flexible sampling algorithms is necessary to accommodate the multi-step synthesis with practical constraints unique to each high-throughput workflow. In this work, we designed and employed three strategies on a high-throughput robotic platform to optimize the sulfonation reaction of redox-active molecules used in flow batteries. Our strategies adapt to the multi-step experimental workflow, where their formulation and heating steps are separate, causing varying batch size requirements. By strategically sampling using clustering and mixed-variable batch Bayesian optimization, we were able to iteratively identify optimal conditions that maximize the yields. Our work presents a flexible approach that allows tailoring the machine learning decision-making to suit the practical constraints in individual high-throughput experimental platforms, followed by performing resource-efficient yield optimization using available open-source Python libraries.
Microfluidics is an appealing analytical tool in the global effort to close the nuclear fuel cycle. Using a microfluidic chip permits analysis of greatly reduced sample volumes compared to what is necessary for traditional analytical methods. There is a commensurate reduction in disposal volume and cost. Development of spectroscopic methods practicable on the microscale are necessary to take advantage of the microchip configuration. This study uses simultaneously applied UV-vis and micro-Raman spectroscopies adapted to function on the microscale to analyze in situ both the Nd3+ (UV-vis active) and HNO3 (Raman active) concentrations in the same sample. An adjustable translation platform was designed to hold the micro-Raman probe above and perpendicular to the chip face, and the UV-vis probe in the plane of the chip. These complimentary spectral techniques when processed through multivariate PLS (Partial Least Squares) models gave an accurate picture of the widely varying solution concentrations as a function of time for each solution component. Solution matrix effects can drastically alter analyte signatures as measured by both UV-vis absorbance and Raman spectroscopy. PLS methods successfully modeled these spectral changes and accurately measured concentrations of components of interest within the microfluidic chip.
Critical military training and testing on lands along the nation’s coastal and estuarine shorelines are increasingly placed at risk because of encroachment pressures in surrounding areas, impairments due to other anthropogenic disturbances, and changes in climate and sea level. The U.S. Department of Defense (DoD) intends to enhance and sustain its training and testing assets and also optimize its stewardship of natural resources through the development and application of an ecosystem-based management approach on DoD installations. To accomplish this goal, particularly for installations in estuarine/coastal environments, the Strategic Environmental Research and Development Program (SERDP) launched the Defense Coastal/Estuarine Research Program (DCERP) as a 10-year effort at Marine Corps Base Camp Lejeune (MCBCL) in North Carolina. The results of the second 5 years of the program (DCERP2) are presented in the DCERP2 Final Report.There were four overarching objectives of DCERP2. The first objective was to understand the effects of climate change impacts, including warming temperatures, variability in the hydrological cycle, storm events, and sea level rise on the coastal ecosystems at MCBCL from observations and measurements made over the 10-year program. The second objective was to understand the carbon cycle of the coastal and terrestrial ecosystems at MCBCL through a highly integrated sampling program. The third objective was to develop models, tools, and indicators to evaluate current and projected future ecosystem state changes and translate scientific findings into actionable information for installation managers. The last objective was to recommend adaptive management strategies to sustain ecosystem natural resources within the context of an active military installation.This data package includes a zipfile containing the results of continuous (30 minute interval) water quality samples and wind speed and direction anemometer (MET) data from Stone's Bay at Station 3 (AVP1). Description from Monitoring Report: Autonomous vertical profilers (AVPs) were deployed from June 2008 through October 2016 in central Stones Bay at Station 3, and in Morgan Bay upstream of Station 5 and were used to continuously monitor key water quality parameters. The AVPs consist of a moored floating platform housing a computer controlled winch mechanism that lowers and raises a YSI Model 6600 multi-parameter water quality datasonde at programmable time intervals (Reynolds-Fleming et al., 2002). Winch speed and sampling frequency of the AVP system and datasonde were configured to produce full water column profiles of temperature, salinity, DO concentration, chl concentration (by fluorescence), and turbidity every 30 minutes and achieve vertical depth resolution of approximately 10 cm. An anemometer (RM Young) affixed to the mast of the AVP measured six minute averages of wind speed and direction every 30 minutes. Data were stored locally on the AVP computers, and telemetered back to the laboratory each night. Datasondes were exchanged with freshly cleaned and calibrated datasondes as necessary but at least on a monthly basis.
Critical military training and testing on lands along the nation’s coastal and estuarine shorelines are increasingly placed at risk because of encroachment pressures in surrounding areas, impairments due to other anthropogenic disturbances, and changes in climate and sea level. The U.S. Department of Defense (DoD) intends to enhance and sustain its training and testing assets and also optimize its stewardship of natural resources through the development and application of an ecosystem-based management approach on DoD installations. To accomplish this goal, particularly for installations in estuarine/coastal environments, the Strategic Environmental Research and Development Program (SERDP) launched the Defense Coastal/Estuarine Research Program (DCERP) as a 10-year effort at Marine Corps Base Camp Lejeune (MCBCL) in North Carolina. The results of the second 5 years of the program (DCERP2) are presented in the DCERP2 Final Report.There were four overarching objectives of DCERP2. The first objective was to understand the effects of climate change impacts, including warming temperatures, variability in the hydrological cycle, storm events, and sea level rise on the coastal ecosystems at MCBCL from observations and measurements made over the 10-year program. The second objective was to understand the carbon cycle of the coastal and terrestrial ecosystems at MCBCL through a highly integrated sampling program. The third objective was to develop models, tools, and indicators to evaluate current and projected future ecosystem state changes and translate scientific findings into actionable information for installation managers. The last objective was to recommend adaptive management strategies to sustain ecosystem natural resources within the context of an active military installation.This data package includes a zipfile containing the results of continuous (30 minute interval) water quality samples and wind speed and direction anemometer (MET) data from Morgan Bay upstream of Station 5 (AVP2): Autonomous vertical profilers (AVPs) were deployed from June 2008 through October 2016 in central Stones Bay at Station 3, and in Morgan Bay upstream of Station 5 and were used to continuously monitor key water quality parameters. The AVPs consist of a moored floating platform housing a computer controlled winch mechanism that lowers and raises a YSI Model 6600 multi-parameter water quality datasonde at programmable time intervals (Reynolds-Fleming et al., 2002). Winch speed and sampling frequency of the AVP system and datasonde were configured to produce full water column profiles of temperature, salinity, DO concentration, chl concentration (by fluorescence), and turbidity every 30 minutes and achieve vertical depth resolution of approximately 10 cm. An anemometer (RM Young) affixed to the mast of the AVP measured six minute averages of wind speed and direction every 30 minutes. Data were stored locally on the AVP computers, and telemetered back to the laboratory each night. Datasondes were exchanged with freshly cleaned and calibrated datasondes as necessary but at least on a monthly basis.
We present a combination of neutron powder-diffraction measurements demonstrating negative linear compressibility and irregular thermal expansion in 7 Li 2 CO 3 . This is shown to be due to an interplay between the tilting of the rigid carbonate group and the shear strain in the unit cell, which leads to a first-order transition from monoclinic to hexagonal symmetry. The phase evolution is shown to be highly sensitive to the level of hydrostaticity in the sample. Under hydrostatic conditions, the sample begins transformation at 8.5 GPa leading to a change from tetrahedral to octahedral Li coordination. Symmetry adapted basis mode analysis, combined with density functional theoretical (DFT) calculations and Raman spectroscopy, is used to show that this transition is reverse proper ferroelastic in nature.
We have gathered a sample of 112 main-sequence stars with known debris disks. We collected published information and performed adaptive optics observations at Lick Observatory to determine if these debris disks are associated with binary or multiple stars. We discovered a previously unknown M-star companion to HD 1051 at a projected separation of 628 AU. We found that 25% {+-} 4% of our debris disk systems are binary or triple star systems, substantially less than the expected {approx}50%. The period distribution for these suggests a relative lack of systems with 1-100 AU separations. Only a few systems have blackbody disk radii comparable to the binary/triple separation. Together, these two characteristics suggest that binaries with intermediate separations of 1-100 AU readily clear out their disks. We find that the fractional disk luminosity, as a proxy for disk mass, is generally lower for multiple systems than for single stars at any given age. Hence, for a binary to possess a disk (or form planets) it must either be a very widely separated binary with disk particles orbiting a single star or it must be a small separation binary with a circumbinary disk.
A new physical framework for the emergence of the Hubble sequence is outlined, based on novel analyses performed to quantify the evolution of cold streams of a large sample of galaxies from a state-of-the-art ultra-high resolution, large-scale adaptive mesh-refinement hydrodynamic simulation in a fully cosmological setting. It is found that the following three key physical variables of galactic cold inflows crossing the virial sphere substantially decrease with decreasing redshift: the number of streams N {sub 90} that make up 90% of concurrent inflow mass flux, average inflow rate per stream M-dot {sub 90} and mean (mass flux weighted) gas density in the streams n {sub gas}. Another key variable, the stream dimensionless angular momentum parameter λ, is found to instead increase with decreasing redshift. Assimilating these trends and others naturally leads to a physically coherent scenario for the emergence of the Hubble sequence, including the following expectations: (1) the predominance of a mixture of disproportionately small irregular and complex disk galaxies at z ≥ 2 when most galaxies have multiple concurrent streams, (2) the beginning of the appearance of flocculent spirals at z ∼ 1-2 when the number of concurrent streams are about 2-3, (3) the grand-design spiral galaxies appear at z ≤ 1 when galaxies with only one major cold stream significantly emerge. These expected general trends are in good accord with observations. Early-type galaxies are those that have entered a perennial state of zero cold gas stream, with their abundance increasing with decreasing redshift.
Photovoltaic (PV) module soiling is a growing area of concern for performance and reliability. This paper provides evaluations of the fundamental interactions of dust/soiling particles with several PV module surfaces. The purpose is to investigate the basic mechanisms involving the chemistry, morphology, and resulting particle adhesion to the first photon-incident surface. The evaluation and mapping of the chemistry and composition of single dust particles collected from operating PV module surfaces are presented. The first correlated direct measurements of the adhesive force of individual grains from field-operating collectors on identical PV module glass are reported, including correlations with specific compositions. Special microscale atomic force microscopy techniques are adapted to determine the force between the particle and the module glass surface. Results are presented for samples under dry and moisture-exposed conditions, confirming the effects of cementation for surfaces having soluble mineral and/or organic concentrations. Additionally, the effects of hydrocarbon fuels on the enhanced bonding of soiling particles to surfaces are determined for samples from urban and highly trafficked regions. Comparisons between glass and dust-mitigating superhydrophobic and superhydrophilic coatings are presented. Potential limitations of this proximal probe technique are discussed in terms of results and initial proof-of-concept experiments.
This study describes the composition and potential metabolic adaptation of microbial communities in northeastern Siberia, a repository of the oldest permafrost in the Northern Hemisphere. Samples of contrasting depth (1.75 to 25.1 m below surface), age (from ~ 10 kyr to 1.1 Myr) and salinity (from low 0.1–0.2 ppt and brackish 0.3–1.3 ppt to saline 6.1 ppt) were collected from freshwater permafrost (FP) of borehole AL1_15 on the Alazeya River, and coastal brackish permafrost (BP) overlying marine permafrost (MP) of borehole CH1_17 on the East Siberian Sea coast. To avoid the limited view provided with culturing work, we used 16S rRNA gene sequencing to show that the biodiversity decreased dramatically with permafrost age. Nonmetric multidimensional scaling (NMDS) analysis placed the samples into three groups: FP and BP together (10–100 kyr old), MP (105–120 kyr old), and FP (> 900 kyr old). Younger FP/BP deposits were distinguished by the presence of Acidobacteriota, Bacteroidota, Chloroflexota_A, and Gemmatimonadota, older FP deposits had a higher proportion of Gammaproteobacteria, and older MP deposits had much more uncultured groups within Asgardarchaeota, Crenarchaeota, Chloroflexota, Patescibacteria, and unassigned archaea. The 60 recovered metagenome-assembled genomes and un-binned metagenomic assemblies suggested that despite the large taxonomic differences between samples, they all had a wide range of taxa capable of fermentation coupled to nitrate utilization, with the exception of sulfur reduction present only in old MP deposits.
Polysiloxane foams utilized for stockpile applications experience changes over time induced by exposure to thermal, radiative, and mechanical stresses. The interplay of these stresses can lead to chemical and/or physical changes in the polymer, such as compression set and material degradation over time. Understanding the mechanisms of material aging and identifying material degradation prior to failure are critical in maintaining the integrity of the nuclear stockpile. Our prior work using solid-state NMR spectroscopy has shown that we can observe signatures of thermal, radiative, and mechanical aging of silicone elastomers using differences in NMR parameters such as relaxation (T1 and T2). However, most of our measurements have been on bulk samples and have only provided an average aging behavior of each sample. Here, we have used magnetic resonance imaging (MRI) to provide relaxation-weighted images that are a metric of structural heterogeneity (cross-link density) in polymers across multiple length scales. In addition, the larger sample size that can be accommodated in animal MRI scanners has the potential to make the technique adaptable to detecting heterogeneities whole parts.
Polysiloxane foams utilized for stockpile applications experience changes over time induced by exposure to thermal, radiative, and mechanical stresses. The interplay of these stresses can lead to chemical and/or physical changes in the polymer, such as compression set and material degradation over time. Understanding the mechanisms of material aging and identifying material degradation prior to failure are critical in maintaining the integrity of the nuclear stockpile. Our prior work using solid-state NMR spectroscopy has shown that we can observe signatures of thermal, radiative, and mechanical aging of silicone elastomers using differences in NMR parameters such as relaxation (T1 and T2). However, most of our measurements have been on bulk samples and have only provided an average aging behavior of each sample. Here, we have used magnetic resonance imaging (MRI) to provide relaxation-weighted images that are a metric of structural heterogeneity (crosslink density) in polymers across multiple length scales. In addition, the larger sample size that can be accommodated in animal MRI scanners has the potential to make the technique adaptable to detecting heterogeneities in whole parts.
The molecular evolution of HIV-1 is characterized by frequent substitutions, indels and recombination events. In addition, a HIV-1 population may adapt through frequency changes of its variants. To reveal such population dynamics we analyzed HIV-1 subpopulation frequencies in an untreated patient with stable, low plasma HIV-1 RNA levels and close to normal CD4+ T-cell levels. The patient was intensively sampled during a 32-day period as well as approximately 1.5 years before and after this period (days 2664, 1, 2, 3, 11, 18, 25, 32 and 522). 77 sequences of HIV-1 env (approximately 3100 nucleotides) were obtained from plasma by limiting dilution with 7–11 sequences per time point, except day 2664. Phylogenetic analysis using maximum likelihood methods showed that the sequences clustered in six distinct subpopulations. We devised a method that took into account the relatively coarse sampling of the population. Data from days 1 through 32 were consistent with constant within-patient subpopulation frequencies. However, over longer time periods, i.e. between days 1…32 and 522, there were significant changes in subpopulation frequencies, which were consistent with evolutionarily neutral fluctuations. We found no clear signal of natural selection within the subpopulations over the study period, but positive selection was evident on the long branches that connected the subpopulations, which corresponds to >3 years as the subpopulations already were established when we started the study. Thus, selective forces may have been involved when the subpopulations were established. Genetic drift within subpopulations caused by de novo substitutions could be resolved after approximately one month. Overall, we conclude that subpopulation frequencies within this patient changed significantly over a time period of 1.5 years, but that this does not imply directional or balancing selection. We show that the short-term evolution we study here is likely representative for many patients of slow and normal disease progression.
We present an algorithm for simulating the equations of ideal magnetohydrodynamics and other systems of differential equations on an unstructured set of points represented by sample particles. Local, third-order, least-squares, polynomial interpolations (Moving Least Squares interpolations) are calculated from the field values of neighboring particles to obtain field values and spatial derivatives at the particle position. Field values and particle positions are advanced in time with a second-order predictor-corrector scheme. The particles move with the fluid, so the time step is not limited by the Eulerian Courant-Friedrichs-Lewy condition. Full spatial adaptivity is implemented to ensure the particles fill the computational volume, which gives the algorithm substantial flexibility and power. A target resolution is specified for each point in space, with particles being added and deleted as needed to meet this target. Particle addition and deletion is based on a local void and clump detection algorithm. Dynamic artificial viscosity fields provide stability to the integration. The resulting algorithm provides a robust solution for modeling flows that require Lagrangian or adaptive discretizations to resolve. This paper derives and documents the Phurbas algorithm as implemented in Phurbas version 1.1. A following paper presents the implementation and test problem results.