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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

First Use of a Longitudinally Polarized Target with CLAS12

Run Group C comprises eight experiments utilizing the CLAS12 detector system in Hall B at Jefferson Lab to study the multidimensional partonic structure of nucleons. The experiments scatter electrons from polarized protons and neutrons in samples of solid NH$_3$ and ND$_3$, dynamically polarized at a temperature of 1 K in a 5 T magnetic field. After a brief description of the target system, the current status of the target and preliminary results of its performance in the experiment are presented.

Keith, Christopher↗
DOE OSTI2026

Diquark scalar production of a vectorlike quark pair at the LHC

We study the discovery potential of LHC experiments for resonantly produced vectorlike quarks ($\chi$) when the $s$-channel resonance is an ultraheavy scalar diquark ($S_{uu}$) with a mass in the 7–8.5 TeV range. Focusing on the process $pp \rightarrow S_{uu} \rightarrow \chi \chi \rightarrow (W^+b)(W^+b)$ we target the fully hadronic decay mode of both $W^+$ bosons, resulting in a six-jet final state. Signal–background separation is performed using Machine Learning algorithms trained to construct a multidimensional classifier. Our results show that ATLAS or CMS searches in this channel, with an integrated luminosity of 3000 fb$^{-1}$, could discover or exclude a scalar diquark with a mass near 8 TeV even for a relatively small Yukawa coupling to up quarks, $y_{uu} \simeq 0.2$. We also present preliminary studies of the four-jet final state from $pp \rightarrow S_{uu} \rightarrow u \chi \rightarrow u (W^+b)$.

Duminica, Ioana [Bucharest, IFIN-HH; Bucharest U.]↗
DOE OSTI2020

Measuring Intelligence with the Sandia Matrices: Psychometric Review and Recommendations for Free Raven-Like Item Sets

The Sandia Matrices are a free alternative to the Raven’s Progressive Matrices (RPMs). This study offers a psychometric review of Sandia Matrices items focused on two of the most commonly investigated issues regarding the RPMs: (a) dimensionality and (b) sex differences. Model-data fit of three alternative factor structures are compared using confirmatory multidimensional item response theory (IRT) analyses, and measurement equivalence analyses are conducted to evaluate potential sex bias. Although results are somewhat inconclusive regarding factor structure, results do not show evidence of bias or mean differences by sex. Finally, although the Sandia Matrices software can generate infinite items, editing and validating items may be infeasible for many researchers. Further, to aide implementation of the Sandia Matrices, we provide scoring materials for two brief static tests and a computer adaptive test. Implications and suggestions for future research using the Sandia Matrices are discussed.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Optimizing Performance of Combustion Chemistry Solvers on Intel's Many Integrated Core (MIC) Architectures

This work investigates novel algorithm designs and optimization techniques for restructuring chemistry integrators in zero and multidimensional combustion solvers, which can then be effectively used on the emerging generation of Intel's Many Integrated Core/Xeon Phi processors. These processors offer increased computing performance via large number of lightweight cores at relatively lower clock speeds compared to traditional processors (e.g. Intel Sandybridge/Ivybridge) used in current supercomputers. This style of processor can be productively used for chemistry integrators that form a costly part of computational combustion codes, in spite of their relatively lower clock speeds. Performance commensurate with traditional processors is achieved here through the combination of careful memory layout, exposing multiple levels of fine grain parallelism and through extensive use of vendor supported libraries (Cilk Plus and Math Kernel Libraries). Important optimization techniques for efficient memory usage and vectorization have been identified and quantified. These optimizations resulted in a factor of ~ 3 speed-up using Intel 2013 compiler and ~ 1.5 using Intel 2017 compiler for large chemical mechanisms compared to the unoptimized version on the Intel Xeon Phi. The strategies, especially with respect to memory usage and vectorization, should also be beneficial for general purpose computational fluid dynamics codes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

The direct and indirect drivers shaping RNA viral communities in grassland soil

Recent studies have revealed diverse RNA viral communities in soils. Yet, how environmental factors influence soil RNA viruses remains largely unknown. Here, we recovered RNA viral communities from 24 metatranscriptomes sequenced from grassland soils managed under a range of environmental conditions including 1) water content: 100% and 25% water holding capacity, 2) plant presence: planted with tall wheatgrass (Thinopyrum ponticum) and bare soil, 3) cultivar type: Alkar and Jose, and 4) soil depth: 0-5 cm and 15-25 cm. The recovered RNA viral communities were novel with nearly one-third of the RNA viral contigs uniquely detected in the studied grassland. The classified RNA viral contigs are mostly known as eukaryotic RNA viruses (74.7%) belonging to Phyla Duplornaviricota, Kitrinoviricota, Lenarviricota, and Pisuviricota. Eukaryotic RNA viruses of Family Mitoviridae as well as their natural hosts, Fungi, are one of the most dominant taxa. Consistent with the results of nonmetric multidimensional scaling analysis, the four environmental conditions (water content, plant presence, cultivar, and soil depth) significantly influence the assemblages of soil RNA viral communities as suggested by the correlation analysis and the random forest algorithm. The modularity analysis of the factor network and the structural equation modeling further support the hierarchical associations among the four environmental factors and the community factors representing the co-existing eukaryotic, prokaryotic, and RNA viral communities. The soil water content, plant presence, and type of cultivar demonstrate a significant positive impact on eukaryotic RNA viral richness directly as well as indirectly on eukaryotic RNA viral abundance via influencing the co-existing eukaryotic members in this soil. Our data also provide statistical support for the negative influence of soil depth on soil eukaryotic richness and abundances resulting in its indirect impact on soil eukaryotic RNA viral communities. This study provides field-relevant information on how environmental and community factors collectively shape soil RNA communities and contribute to ecological understanding of RNA viral survival under various environmental conditions and virus-host interactions in soil.

Wu, Ruonan↗
DOE OSTI2018

Automated Management in Aerospace Production

Software for analysis of the efficiency of high-tech production is proposed. The software is based on a multidimensional model of product-information transformation during the manufacturing process, with a complex relation between the product parameters. Technological inheritance is taken into account.

42 ENGINEERING↗
DOE OSTI2024

β-Ga2O3-Based Heterostructures and Heterojunctions for Power Electronics: A Review of the Recent Advances

During the past decade, Gallium Oxide (Ga2O3) has attracted intensive research interest as an ultra-wide-bandgap (UWBG) semiconductor due to its unique characteristics, such as a large bandgap of 4.5–4.9 eV, a high critical electric field of ~8 MV/cm, and a high Baliga’s figure of merit (BFOM). Unipolar β-Ga2O3 devices such as Schottky barrier diodes (SBDs) and field-effect transistors (FETs) have been demonstrated. Recently, there has been growing attention toward developing β-Ga2O3-based heterostructures and heterojunctions, which is mainly driven by the lack of p-type doping and the exploration of multidimensional device architectures to enhance power electronics’ performance. This paper will review the most recent advances in β-Ga2O3 heterostructures and heterojunctions for power electronics, including NiOx/β-Ga2O3, β-(AlxGa1−x)2O3/β-Ga2O3, and β-Ga2O3 heterojunctions/heterostructures with other wide- and ultra-wide-bandgap materials and the integration of two-dimensional (2D) materials with β-Ga2O3. Discussions of the deposition, fabrication, and operating principles of these heterostructures and heterojunctions and the associated device performance will be provided. This comprehensive review will serve as a critical reference for researchers engaged in materials science, wide- and ultra-wide-bandgap semiconductors, and power electronics and benefits the future study and development of β-Ga2O3-based heterostructures and heterojunctions and associated power electronics.

Herath Mudiyanselage, Dinusha (ORCID:0000000186119↗
DOE OSTI2018

Capacity Optimization of a Centralized Charging Station in Joint Operation with a Wind Farm

In the context of large-scale wind power integration and rapid development of electric vehicles (EVs), a joint operation pattern was proposed to use a centralized charging station (CCS) to address high uncertainties incurred by wind power integration. Additionally, this would directly remove the significant indeterminacy of wind power. Because the CCS is adjacent to a wind power gathering station, it could work jointly with wind farms to operate in power system as an independent enterprise. By combining the actual operational characteristics of the wind farm and the CCS, a multidimensional operating index evaluation system was created for the joint system. Based on the wind farm’s known capacity, a multi-target capacity planning model was established for CCS to maximize the probability of realizing diverse indices of the system based on dependent-chance goal programming. In this model, planning and dispatching are combined to improve the feasibility of results in the operational stage. This approach takes the effects of randomness into account for wind power and battery swapping. The model was solved through combining Monte Carlo simulation and a genetic algorithm (GA) based on segmented encoding. Finally, as shown in the simulation results, the proposed model could comprehensively include factors such as initial investment, wind power price, battery life, etc., to optimize CCS capacity and to ultimately improve the operating indices of the joint system.

17 WIND ENERGY↗
DOE OSTI2022

Predicting Rare Earth Element Potential in Produced and Geothermal Waters of the United States via Emergent Self-Organizing Maps

This work applies emergent self-organizing map (ESOM) techniques, a form of machine learning, in the multidimensional interpretation and prediction of rare earth element (REE) abundance in produced and geothermal waters in the United States. Visualization of the variables in the ESOM trained using the input data shows that each REE, with the exception of Eu, follows the same distribution patterns and that no single parameter appears to control their distribution. Cross-validation, using a random subsample of the starting data and only using major ions, shows that predictions are generally accurate to within an order of magnitude. Using the same approach, an abridged version of the U.S. Geological Survey Produced Waters Database, Version 2.3 (which includes both data from produced and geothermal waters) was mapped to the ESOM and predicted values were generated for samples that contained enough variables to be effectively mapped. Results show that in general, produced and geothermal waters are predicted to be enriched in REEs by an order of magnitude or more relative to seawater, with maximum predicted enrichments in excess of 1000-fold. Cartographic mapping of the resulting predictions indicates that maximum REE concentrations exceed values in seawater across the majority of geologic basins investigated and that REEs are typically spatially co-associated. The factors causing this co-association were not determined from ESOM analysis, but based on the information currently available, REE content in produced and geothermal waters is not directly controlled by lithology, reservoir temperature, or salinity.

Engle, Mark A. (ORCID:0000000152587374)↗
DOE OSTI2024

Uncertainty Quantification in CO2 Trapping Mechanisms: A Case Study of PUNQ-S3 Reservoir Model Using Representative Geological Realizations and Unsupervised Machine Learning

Evaluating uncertainty in CO2 injection projections often requires numerous high-resolution geological realizations (GRs) which, although effective, are computationally demanding. This study proposes the use of representative geological realizations (RGRs) as an efficient approach to capture the uncertainty range of the full set while reducing computational costs. A predetermined number of RGRs is selected using an integrated unsupervised machine learning (UML) framework, which includes Euclidean distance measurement, multidimensional scaling (MDS), and a deterministic K-means (DK-means) clustering algorithm. In the context of the intricate 3D aquifer CO2 storage model, PUNQ-S3, these algorithms are utilized. The UML methodology selects five RGRs from a pool of 25 possibilities (20% of the total), taking into account the reservoir quality index (RQI) as a static parameter of the reservoir. To determine the credibility of these RGRs, their simulation results are scrutinized through the application of the Kolmogorov–Smirnov (KS) test, which analyzes the distribution of the output. In this assessment, 40 CO2 injection wells cover the entire reservoir alongside the full set. The end-point simulation results indicate that the CO2 structural, residual, and solubility trapping within the RGRs and full set follow the same distribution. Simulating five RGRs alongside the full set of 25 GRs over 200 years, involving 10 years of CO2 injection, reveals consistently similar trapping distribution patterns, with an average value of Dmax of 0.21 remaining lower than Dcritical (0.66). Using this methodology, computational expenses related to scenario testing and development planning for CO2 storage reservoirs in the presence of geological uncertainties can be substantially reduced.

Mahjour, Seyed Kourosh↗
DOE OSTI2023

Functional Meta-Analysis of the Proteomic Responses of Arabidopsis Seedlings to the Spaceflight Environment Reveals Multi-Dimensional Sources of Variability across Spaceflight Experiments

The human quest for sustainable habitation of extraterrestrial environments necessitates a robust understanding of life’s adaptability to the unique conditions of spaceflight. This study provides a comprehensive proteomic dissection of the Arabidopsis plant’s responses to the spaceflight environment through a meta-analysis of proteomics data from four separate spaceflight experiments conducted on the International Space Station (ISS) in different hardware configurations. Raw proteomics LC/MS spectra were analyzed for differential expression in MaxQuant and Perseus software. The analysis of dissimilarities among the datasets reveals the multidimensional nature of plant proteomic responses to spaceflight, impacted by variables such as spaceflight hardware, seedling age, lighting conditions, and proteomic quantification techniques. By contrasting datasets that varied in light exposure, we elucidated proteins involved in photomorphogenesis and skotomorphogenesis in plant spaceflight responses. Additionally, with data from an onboard 1 g control experiment, we isolated proteins that specifically respond to the microgravity environment and those that respond to other spaceflight conditions. This study identified proteins and associated metabolic pathways that are consistently impacted across the datasets. Notably, these shared proteins were associated with critical metabolic functions, including carbon metabolism, glycolysis, gluconeogenesis, and amino acid biosynthesis, underscoring their potential significance in Arabidopsis’ spaceflight adaptation mechanisms and informing strategies for successful space farming.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Hydrothermal Synthesis and Structural Investigation of a Crystalline Uranyl Borosilicate

The relevance of multidimensional and porous crystalline materials to nuclear waste remediation and storage applications has motivated exploratory research focused on materials discovery of compounds, such as actinide mixed-oxoanion phases, which exhibit rich structural chemistry. The novel phase K1.8Na1.2[(UO2)BSi4O12] has been synthesized using hydrothermal methods, representing the first example of a uranyl borosilicate. The three-dimensional structure crystallizes in the orthorhombic space group Cmce with lattice parameters a = 15.5471(19) Å, b = 14.3403(17) Å, c = 11.7315(15) Å, and V = 2615.5(6) Å3, and is composed of UO6 octahedra linked by [BSi4O12]5− chains to form a [(UO2)BSi4O12]3− framework. The synthesis method, structure, results of Raman, IR, and X-ray absorption spectroscopy, and thermal stability are discussed.

Pace, Kristen A. (ORCID:0000000185748939)↗
DOE OSTI2020

Material Discovery and High Throughput Exploration of Ru Based Catalysts for Low Temperature Ammonia Decomposition

High throughput experimentation has the capability to generate massive, multidimensional datasets, allowing for the discovery of novel catalytic materials. Here, we show the synthesis and catalytic screening of over 100 unique Ru-Metal-K based bimetallic catalysts for low temperature ammonia decomposition, with a Ru loading between 1–3 wt% Ru and a fixed K loading of 12 wt% K, supported on γ-Al2O3. Bimetallic catalysts containing Sc, Sr, Hf, Y, Mg, Zr, Ta, or Ca in addition to Ru were found to have excellent ammonia decomposition activity when compared to state-of-the-art catalysts in literature. Furthermore, the Ru content could be reduced to 1 wt% Ru, a factor of four decrease, with the addition of Sr, Y, Zr, or Hf, where these secondary metals have not been previously explored for ammonia decomposition. The bimetallic interactions between Ru and the secondary metal, specifically RuSrK and RuFeK, were investigated in detail to elucidate the reaction kinetics and surface properties of both high and low performing catalysts. The RuSrK catalyst had a turnover frequency of 1.78 s−1, while RuFeK had a turnover frequency of only 0.28 s−1 under identical operating conditions. Based on their apparent activation energies and number of surface sites, the RuSrK had a factor of two lower activation energy than the RuFeK, while also possessing an equivalent number of surface sites, which suggests that the Sr promotes ammonia decomposition in the presence of Ru by modifying the active sites of Ru.

McCullough, Katherine (ORCID:0000000197525740)↗
DOE OSTI2022

Soil Metabolomics Predict Microbial Taxa as Biomarkers of Moisture Status in Soils from a Tidal Wetland

We present observations from a laboratory-controlled study on the impacts of extreme wetting and drying on a wetland soil microbiome. Our approach was to experimentally challenge the soil microbiome to understand impacts on anaerobic carbon cycling processes as the system transitions from dryness to saturation and vice-versa. Specifically, we tested for impacts on stress responses related to shifts from wet to drought conditions. We used a combination of high-resolution data for small organic chemical compounds (metabolites) and biological (community structure based on 16S rRNA gene sequencing) features. Using a robust correlation-independent data approach, we further tested the predictive power of soil metabolites for the presence or absence of taxa. Here, we demonstrate that taking an untargeted, multidimensional data approach to the interpretation of metabolomics has the potential to indicate the causative pathways selecting for the observed bacterial community structure in soils.

16S rRNA gene↗
DOE OSTI2020

Comparison of the Transcriptome Response within the Swine Tracheobronchial Lymphnode Following Infection with PRRSV, PCV-2 or IAV-S.

Porcine reproductive and respiratory syndrome virus (PRRSV) is a major respiratory pathogen of swine that has become extremely costly to the swine industry worldwide, often causing losses in production and animal life due to their ease of spread. However, the intracellular changes that occur in pigs following viral respiratory infections are still scantily understood for PRRSV, as well as other viral respiratory infections. The aim of this study was to acquire a better understanding of the PRRS disease by comparing gene expression changes that occur in tracheobronchial lymph nodes (TBLN) of pigs infected with either porcine reproductive and respiratory syndrome virus (PRRSV), porcine circovirus type 2 (PCV-2), or swine influenza A virus (IAV-S) infections. The study identified and compared gene expression changes in the TBLN of 80 pigs following infection by PRRSV, PCV-2, IAV-S, or sham inoculation. Total RNA was pooled for each group and time-point (1, 3, 6, and 14 dpi) to make 16 libraries—analyses are by Digital Gene Expression Tag Profiling (DGETP). The data underwent standard filtering to generate a list of sequence tag raw counts that were then analyzed using multidimensional and differential expression statistical tests. The results showed that PRRSV, IAV-S and PCV-2 viral infections followed a clinical course in the pigs typical of experimental infection of young pigs with these viruses. Gene expression results echoed this course, as well as uncovered genes related to intersecting and unique host immune responses to the three viruses. By testing and observing the host response to other respiratory viruses, our study has elucidated similarities and differences that can assist in the development of vaccines and therapeutics that shorten or prevent a chronic PRRSV infection.

Miller↗
DOE OSTI2019

An Optimization-Based Framework to Define the Probabilistic Design Space of Pharmaceutical Processes with Model Uncertainty

To increase manufacturing flexibility and system understanding in pharmaceutical development, the FDA launched the quality by design (QbD) initiative. Within QbD, the design space is the multidimensional region (of the input variables and process parameters) where product quality is assured. Given the high cost of extensive experimentation, there is a need for computational methods to estimate the probabilistic design space that considers interactions between critical process parameters and critical quality attributes, as well as model uncertainty. In this paper we propose two algorithms that extend the flexibility test and flexibility index formulations to replace simulation-based analysis and identify the probabilistic design space more efficiently. The effectiveness and computational efficiency of these approaches is shown on a small example and an industrial case study.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Fatigue-Crack Detection and Monitoring through the Scattered-Wave Two-Dimensional Cross-Correlation Imaging Method Using Piezoelectric Transducers

Piezoelectric transducers are convenient enablers for generating and receiving Lamb waves for damage detection. Fatigue cracks are one of the most common causes for the failure of metallic structures. Increasing emphasis on the integrity of critical structures creates an urgent need to monitor structures and to detect cracks at an early stage to prevent catastrophic failures. This paper presents a two-dimensional (2D) cross-correlation imaging technique that can not only detect a fatigue crack but can also precisely image the fatigue cracks in metallic structures. The imaging method was based on the cross-correlation algorithm that uses incident waves and the crack-scattered waves of all directions to generate the crack image. Fatigue testing for crack generation was then conducted in both an aluminum plate and a stainless-steel plate. Piezoelectric wafer transducer was used to actuate the interrogating Lamb wave. To obtain the scattered waves as well as the incident waves, a scanning laser Doppler vibrometer was adopted for acquiring time-space multidimensional wavefield, followed with frequency-wavenumber processing. The proof-of-concept study was conducted in an aluminum plate with a hairline fatigue crack. A frequency-wavenumber filtering method was used to obtain the incident wave and the scattered wave wavefields for the cross-correlation imaging. After this, the imaging method was applied to evaluate cracks on a stainless-steel plate generated during fatigue loading tests. The presented imaging method showed successful inspection and quantification results of the crack and its growth.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Can Simple Machine Learning Tools Extend and Improve Temperature-Based Methods to Infer Streambed Flux?

Temperature-based methods have been developed to infer 1D vertical exchange flux between a stream and the subsurface. Current analyses rely on fitting physically based analytical and numerical models to temperature time series measured at multiple depths to infer daily average flux. These methods have seen wide use in hydrologic science despite strong simplifying assumptions including a lack of consideration of model structural error or the impacts of multidimensional flow or the impacts of transient streambed hydraulic properties. We performed a “perfect-model experiment” investigation to examine whether regression trees, with and without gradient boosting, can extract sufficient information from model-generated subsurface temperature time series, with and without added measurement error, to infer the corresponding exchange flux time series at the streambed surface. Using model-generated, synthetic data allowed us to assess the basic limitations to the use of machine learning; further examination of real data is only warranted if the method can be shown to perform well under these ideal conditions. We also examined whether the inherent feature importance analyses of tree-based machine learning methods can be used to optimize monitoring networks for exchange flux inference.

54 ENVIRONMENTAL SCIENCES↗