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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Subsetted model output on height-above-mean-sea-level levels for cloud-related variables from a LASSO-CACTI WRF simulation

Cloud-related model output interpolated to heights-above-mean-sea-level from a Weather Research and Forecasting (WRF) simulation from the Large-Eddy Simulation (LES) Atmospheric Radiation Measurement (ARM) Symbiotic Simulation and Observation (LASSO) deep-convection scenario for the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign. The LASSO-CACTI simulations span grid spacings from 7.5 km to 100 m through the use of four nested domains, labeled d1 through d4. The simulations are of convection near the Sierras de Córdoba mountain range, roughly centered on the ARM Mobile Facility. More information can be found at https://www.arm.gov/capabilities/modeling/lasso. This version of the output is a collection of variables subsetted from the raw WRF output to reduce the file overhead for users not needing the full raw dataset. The subset files are in netCDF format with the height-based variables interpolated to heights based on distance above mean sea level.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Subsetted model output on height-above-mean-sea-level levels for meteorology-related variables from a LASSO-CACTI WRF simulation

Meteorology-related model output interpolated to heights-above-mean-sea-level from a Weather Research and Forecasting (WRF) simulation from the Large-Eddy Simulation (LES) Atmospheric Radiation Measurement (ARM) Symbiotic Simulation and Observation (LASSO) deep-convection scenario for the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign. The LASSO-CACTI simulations span grid spacings from 7.5 km to 100 m through the use of four nested domains, labeled d1 through d4. The simulations are of convection near the Sierras de Córdoba mountain range, roughly centered on the ARM Mobile Facility. More information can be found at https://www.arm.gov/capabilities/modeling/lasso. This version of the output is a collection of variables subsetted from the raw WRF output to reduce the file overhead for users not needing the full raw dataset. The subset files are in netCDF format with the height-based variables interpolated to heights based on distance above mean sea level.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Subsetted model output for meteorology-related variables on staggered grids from a LASSO-CACTI WRF simulation

Meteorology-related model output for variables on staggered grids from a Weather Research and Forecasting (WRF) simulation from the Large-Eddy Simulation (LES) Atmospheric Radiation Measurement (ARM) Symbiotic Simulation and Observation (LASSO) deep-convection scenario for the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign. The LASSO-CACTI simulations span grid spacings from 7.5 km to 100 m through the use of four nested domains, labeled d1 through d4. The simulations are of convection near the Sierras de Córdoba mountain range, roughly centered on the ARM Mobile Facility. More information can be found at https://www.arm.gov/capabilities/modeling/lasso. This version of the output is a collection of variables subsetted from the raw WRF output to reduce the file overhead for users not needing the full raw dataset. The subset files are in netCDF format with the height-based variables on the raw, model eta levels.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Codiscovering graphical structure and functional relationships within data: A Gaussian Process framework for connecting the dots

Most problems within and beyond the scientific domain can be framed into one of the following three levels of complexity of function approximation. Type 1: Approximate an unknown function given input/output data. Type 2: Consider a collection of variables and functions, some of which are unknown, indexed by the nodes and hyperedges of a hypergraph (a generalized graph where edges can connect more than two vertices). Given partial observations of the variables of the hypergraph (satisfying the functional dependencies imposed by its structure), approximate all the unobserved variables and unknown functions. Type 3: Expanding on Type 2, if the hypergraph structure itself is unknown, use partial observations of the variables of the hypergraph to discover its structure and approximate its unknown functions. These hypergraphs offer a natural platform for organizing, communicating, and processing computational knowledge. While most scientific problems can be framed as the data-driven discovery of unknown functions in a computational hypergraph whose structure is known (Type 2), many require the data-driven discovery of the structure (connectivity) of the hypergraph itself (Type 3). We introduce an interpretable Gaussian Process (GP) framework for such (Type 3) problems that does not require randomization of the data, access to or control over its sampling, or sparsity of the unknown functions in a known or learned basis. Its polynomial complexity, which contrasts sharply with the super-exponential complexity of causal inference methods, is enabled by the nonlinear ANOVA capabilities of GPs used as a sensing mechanism.

Science & Technology - Other Topics↗
DOE OSTI2024

Carbon capture in polymer-based electrolytes

Nanoparticle organic hybrid materials (NOHMs) have been proposed as excellent electrolytes for combined CO 2 capture and electrochemical conversion due to their conductive nature and chemical tunability. However, CO 2 capture behavior and transport properties of these electrolytes after CO 2 capture have not yet been studied. Here, we use a variety of nuclear magnetic resonance (NMR) techniques to explore the carbon speciation and transport properties of branched polyethylenimine (PEI) and PEI-grafted silica nanoparticles (denoted as NOHM-I-PEI) after CO 2 capture. Quantitative 13 C NMR spectra collected at variable temperatures reveal that absorbed CO 2 exists as carbamates (RHNCOO – or RR'NCOO – ) and carbonate/bicarbonate (CO 3 2– /HCO 3 – ). The transport properties of PEI and NOHM-I-PEI studied using 1 H pulsed-field-gradient NMR, combined with molecular dynamics simulations, demonstrate that coulombic interactions between negatively and positively charged chains dominate in PEI, while the self-diffusion in NOHM-I-PEI is dominated by silica nanoparticles. These results provide strategies for selecting adsorbed forms of carbon for electrochemical reduction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Refining Fast Simulation Using Machine Learning

A growing reliance on the fast Monte Carlo (FastSim) will accompany the high luminosity and detector granularity expected in Phase 2. FastSim is roughly 10 times faster than equivalent GEANT4-based full simulation (FullSim). However, reduced accuracy of the FastSim affects some analysis variables and collections. To improve its accuracy, FastSim is refined using regression-based neural networks trained with ML. The status of FastSim refinement is presented. The results show improved agreement with the FullSim output and an improvement in correlations among output observables and external parameters.

Güngördü, Acelya Deniz↗
DOE OSTI2025

Evaluation of DNA Extraction Efficiency in Diverse Algae Strains Using Commercial Kits and Lysis Approaches

Efficient DNA extraction is essential for accurately monitoring microalgae communities in large-scale cultivation systems such as raceway ponds and wastewater ponds. Traditional phenol chloroform extracts are a staple in microbiology but are obsolete for routine sampling due to its high toxicity reagents and time intensive setups. Commercial DNA extraction kits are more favorable for the microbes found in these ponds, but lack specific kits made for these communities. Little is known about which kits perform the best, leading researchers to use a variety of different kits with inconsistent results. This project compared one precipitation based commercial kit (Lucigen Masterpure) and five wash based kits (Monarch, Zymo Quick-DNA, and three Qiagen DNeasy kits) using four brackish algae strains to determine which methods yield the greatest quantity and quality of genomic DNA. Extractions were evaluated using the manufacturers protocol, and additional pretreatment options were administered before a single kit to compare its potential in being added routinely before extractions. Pretreatment options included both cryogenic freeze-thawing and heat incubation using enzymes. DNA was quantified using Qubit fluorometry and NanoDrop purity ratios. Overall, the Qiagen PowerWater kit provided the highest DNA yield and purity, but at a significantly higher cost then the precipitation-based kit (MasterPure). It was also noted that while the precipitation-based kit was significantly cheaper, provided similar results, it took significantly more time to complete a single run. Cryogenic pretreatment (6x cycles) increased average DNA yields by up to 80%, whereas enzymatic pretreatment most improved purity ratios without substantially improving quantity. The results suggest that it may be more cost and time efficient to use Qiagen kits with the addition of lysis pretreatments to procure better results. Future works includes developing a better system to efficiently collect multi variable data, and to upscale to artificial polycultures using similar methodologies alongside sequencing to confirm kit results.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1999

Ventilatory responses to dynamic exercise elicited by intramuscular sensors

PURPOSE: Eight subjects, aged 27.0+/-1.6 yr, performed incremental workload cycling to investigate the contribution of skeletal muscle mechano- and metaboreceptors to ventilatory control during dynamic exercise. METHODS: Each subject performed four bouts of exercise: exercise with no intervention (CON); exercise with bilateral thigh cuffs inflated to 90 mm Hg (CUFF); exercise with application of lower-body positive pressure (LBPP) to 45 torr (PP); and exercise with 90 mm Hg thigh cuff inflation and 45 torr LBPP (CUFF+PP). Ventilatory responses and pulmonary gas exchange variables were collected breath-by-breath with concomitant measurement of leg intramuscular pressure. RESULTS: Ventilation (VE) was significantly elevated from CON during PP and CUFF+PP at workloads corresponding to > or = 60% CON peak oxygen uptake (VO2peak) and during CUFF at workloads > or = 80% CON VO2peak, P < 0.05. The VO2 at which ventilatory threshold occurred was significantly reduced from CON (2.17+/-0.28 L x min(-1)) to 1.60+/-0.19 L x min(-1), 1.45+/-0.15 L x min(-1), and 1.15+/-0.11 L x min(-1) during CUFF, PP, and CUFF+PP, respectively. The slope of the linear regression describing the VE/CO2 output relationship was increased from CON by approximately 22% during CUFF, 40% during PP, and 41% during CUFF+PP. CONCLUSIONS: As intramuscular pressure was significantly elevated immediately upon application of LBPP during PP and CUFF+PP without a concomitant increase in VE, it seems unlikely that LBPP-induced increases in VE can be attributed to activation of the mechanoreflex. These findings suggest that LBPP-induced reductions in perfusion pressure and decreases in venous outflow resulting from inflation of bilateral thigh cuffs may generate a metabolite sensitive intramuscular ventilatory stimulus.

Non-NASA Center↗
NASA NTRS2011

Noncontacting Laser Inspection System for Dimensional Profiling of Space Application Thermal Barriers

A noncontacting, two-dimensional (2-D) laser inspection system has been designed and implemented to dimensionally profile thermal barriers being developed for space vehicle applications. In a vehicle as-installed state, thermal barriers are commonly compressed between load sensitive thermal protection system (TPS) panels to prevent hot gas ingestion through the panel interface during flight. Loads required to compress the thermal barriers are functions of their construction, as well as their dimensional characteristics relative to the gaps in which they are installed. Excessive loads during a mission could damage surrounding TPS panels and have catastrophic consequences. As such, accurate dimensional profiling of thermal barriers prior to use is important. Due to the compliant nature of the thermal barriers, traditional contact measurement techniques (e.g., calipers and micrometers) are subjective and introduce significant error and variability into collected dimensional data. Implementation of a laser inspection system significantly enhanced the method by which thermal barriers are dimensionally profiled, and improved the accuracy and repeatability of collected data. A statistical design of experiments study comparing laser inspection and manual caliper measurement techniques verified these findings.

Taylor, Shawn C.↗
DOE OSTI2020

Opportunities for Using the Industrial Assessment Center Database for Industrial Water Use Analysis

The manufacturing sector accounted for approximately 5–6% of total U.S. water use in 2015. Of that amount, 75–80% is self supplied withdrawal from surface-water and groundwater sources and the remainder is from public water supplies. Although manufacturing facilities commonly locate in water-scarce areas, water scarcity still poses a great risk to the manufacturing sector. Reliable water is necessary for any facility that relies on it for process and comfort cooling, cleaning, employee use, and steam generation. One of these barriers to water efficiency is the lack of reliable data on overall U.S. industrial water use—how it is used and the quantities required for each sector. If a facility cannot be easily compared with a facility of similar size and sector, knowing if it is effectively using water conservation best practices is difficult. One potential source of industrial water use data is the U.S. Department of Energy (DOE)–sponsored Industrial Assessment Centers (IACs). IACs are university-based organizations that provide free audits to small- and medium-sized manufacturing facilities to identify productivity improvement and waste and energy reduction opportunities. The IACs also maintain a database of all the audits conducted, which currently holds more than 19,267 assessments and 145,000 recommendations (as of July 24, 2020). This database also contains energy utility (electricity, natural gas, and other fuels) and water utility data, making it a potential data source for industrial water use. This report attempts to create regression models to predict a small- or medium-sized industrial facility’s annual water use or cost based on its industrial subsector and several possible relevant variables. Using data collected by IAC assessments, models for several industrial subsectors were generated via stepwise regression techniques to determine which variables (annual sales, number of employees, facility/plant area, annual production hours, and a water stress metric) are relevant.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Experimental Analysis of the Effects of Simulator Complexity on Human Performance

Human Reliability Analysis predicts accidents caused by human errors and is an important factor in Probabilistic Safety Assessment that comprehensively evaluates the safety of nuclear power plants. This study compares and analyzes the human performance of nuclear power plant operators according to the simulator complexity as part of the HRA data collection support method development project conducted by Idaho National Laboratory. This experiment was conducted by setting two types of simulators and scenarios as independent variables. The data collected by conducting the experiments in two different simulators were analyzed using an analysis of variance test and a correlation analysis, and four human performance charts were derived.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Dataset from ORNL Flexible Research Platform (FRP)

The data comes from a two-story light commercial building which can be used to physically simulate light commercial buildings common in the nation's existing building stock. Based on a data collection plan, these measurements were performed for five different building operations. In the data set for each period, 98 data variables were selected and collected, in which 7 variables are weather data and the rest are all building and system operation data.

Cui, Borui↗
DOE OSTI2021

Scalable Comparative Visualization of Ensembles of Call Graphs

Optimizing the performance of large-scale parallel codes is critical for efficient utilization of computing resources. Code developers often explore various execution parameters, such as hardware configurations, system software choices, and application parameters, and are interested in detecting and understanding bottlenecks in different executions. They often collect hierarchical performance profiles represented as call graphs, which combine performance metrics with their execution contexts. The crucial task of exploring multiple call graphs together is tedious and challenging because of the many structural differences in the execution contexts and significant variability in the collected performance metrics (e.g., execution runtime). In this paper, we present Ensemble CallFlow to support the exploration of ensembles of call graphs using new types of visualizations, analysis, graph operations, and features. We introduce ensemble-Sankey , a new visual design that combines the strengths of resource-flow (Sankey) and box-plot visualization techniques. Whereas the resource-flow visualization can easily and intuitively describe the graphical nature of the call graph, the box plots overlaid on the nodes of Sankey convey the performance variability within the ensemble. Our interactive visual interface provides linked views to help explore ensembles of call graphs, e.g., by facilitating the analysis of structural differences, and identifying similar or distinct call graphs. Finally, we demonstrate the effectiveness and usefulness of our design through case studies on large-scale parallel codes.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1992

Search for subgrid scale parameterization by projection pursuit regression

The dependence of subgrid-scale stresses on variables of the resolved field is studied using direct numerical simulations of isotropic turbulence, homogeneous shear flow, and channel flow. The projection pursuit algorithm, a promising new regression tool for high-dimensional data, is used to systematically search through a large collection of resolved variables, such as components of the strain rate, vorticity, velocity gradients at neighboring grid points, etc. For the case of isotropic turbulence, the search algorithm recovers the linear dependence on the rate of strain (which is necessary to transfer energy to subgrid scales) but is unable to determine any other more complex relationship. For shear flows, however, new systematic relations beyond eddy viscosity are found. For the homogeneous shear flow, the results suggest that products of the mean rotation rate tensor with both the fluctuating strain rate and fluctuating rotation rate tensors are important quantities in parameterizing the subgrid-scale stresses. A model incorporating these terms is proposed. When evaluated with direct numerical simulation data, this model significantly increases the correlation between the modeled and exact stresses, as compared with the Smagorinsky model. In the case of channel flow, the stresses are found to correlate with products of the fluctuating strain and rotation rate tensors. The mean rates of rotation or strain do not appear to be important in this case, and the model determined for homogeneous shear flow does not perform well when tested with channel flow data. Many questions remain about the physical mechanisms underlying these findings, about possible Reynolds number dependence, and, given the low level of correlations, about their impact on modeling. Nevertheless, demonstration of the existence of causal relations between sgs stresses and large-scale characteristics of turbulent shear flows, in addition to those necessary for energy transfer, provides important insight into the relation between scales in turbulent flows.

Meneveau, C.↗
NASA NTRS1959

Wind-Tunnel Investigation of the Effect of Angle of Attack and Flapping-Hinge Offset on Periodic Bending Moments and Flapping of a Small Rotor

A two-blade rotor having a diameter of 4 feet and a solidity of 0.037 was tested in the Langley 300-MPH 7- by 10-foot tunnel to obtain information on the effect of certain rotor variables on the blade periodic bending moments and flapping angles during the various stages of transformation between the helicopter and autogiro configuration. Variables studied included collective pitch angle, flapping-hinge offset, rotor angle of attack, and tip-speed ratio. The results show that the blade periodic bending moments generally increase with tip-speed ratio up into the transition region, diminish over a certain range of tip-speed ratio, and increase again at higher tip-speed ratios. Above the transition region, the bending moments increase with collective pitch angle and rotor angle of attack. The absence of a flapping hinge results in a significant amplification of the periodic bending moments, the magnitudes of which increase with tip-speed ratio. When the flapping hinge is used, an increase in flapping-hinge offset results in reduced period bending moments. The aforementioned trends exhibited by the bending moments for changes in the variables are essentially duplicated by the periodic flapping motions. The existence of substantial amounts of blade stall increased both the periodic bending moments and the flapping angles. Harmonic analysis of the bending moments shows significant contributions of the higher harmonics, particularly in the transition region.

McCarty, John Locke↗
DOE OSTI2021

Improvements to the macroscopic-microscopic approach of nuclear fission

The well-established macroscopic-microscopic (mac-mic) description of nuclear fission enables the prediction of fission-fragment yields for a broad range of fissioning systems. In this work, we present several key enhancements to this approach. We improve upon the microscopic sector of nuclear potential-energy surfaces by magnifying the resolution of the Lipkin-Nogami equations and strengthening the Strutinsky procedure, thus reducing spurious effects from the continuum. We further present a novel deterministic method for calculating fission dynamics under the assumption of strongly damped nucleonic motion. Our technique directly determines the evolution of the scissioned shape distribution according to the number of random-walk steps rather than the statistical accumulation of fission events. We show that our new technique is equivalent to the Metropolis random walk pioneered over the past decade by Randrup and colleagues. It further improves upon it because we remove the need for altering the nuclear landscape via a biased potential. With our final improvement, we calculate fission fragments mass and charge distributions using particle number projection, which affords the simultaneous calculation of both mass- and charge-yield distributions. Fission fragments are thus calculated from the quantum-mechanical A -body states of the potential-energy surface rather than from the collective mass asymmetry variable α g of the finite-range liquid-drop model used in past work. We highlight the success of our enhancements by predicting the odd-even staggering and the charge polarization for the neutron-induced fission of 233 U and 235 U .

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Co-expression networks in Chlamydomonas reveal significant rhythmicity in batch cultures and empower gene function discovery

The unicellular green alga Chlamydomonas reinhardtii is a choice reference system for the study of photosynthesis and chloroplast metabolism, cilium assembly and function, lipid and starch metabolism, and metal homeostasis. Despite decades of research, the functions of thousands of genes remain largely unknown, and new approaches are needed to categorically assign genes to cellular pathways. Growing collections of transcriptome and proteome data now allow a systematic approach based on integrative co-expression analysis. We used a dataset comprising 518 deep transcriptome samples derived from 58 independent experiments to identify potential co-expression relationships between genes. We visualized co-expression potential with the R package corrplot, to easily assess co-expression and anti-correlation between genes. We extracted several hundred high-confidence genes at the intersection of multiple curated lists involved in cilia, cell division, and photosynthesis, illustrating the power of our method. Surprisingly, Chlamydomonas experiments retained a significant rhythmic component across the transcriptome, suggesting an underappreciated variable during sample collection, even in samples collected in constant light. Our results therefore document substantial residual synchronization in batch cultures, contrary to assumptions of asynchrony. We provide step-by-step protocols for the analysis of co-expression across transcriptome data sets from Chlamydomonas and other species to help foster gene function discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Predicting drug-metagenome interactions: Variation in the microbial β-glucuronidase level in the human gut metagenomes

Characterizing the gut microbiota in terms of their capacity to interfere with drug metabolism is necessary to achieve drug efficacy and safety. Although examples of drug-microbiome interactions are well-documented, little has been reported about a computational pipeline for systematically identifying and characterizing bacterial enzymes that process particular classes of drugs. The goal of our study is to develop a computational approach that compiles drugs whose metabolism may be influenced by a particular class of microbial enzymes and that quantifies the variability in the collective level of those enzymes among individuals. The present paper describes this approach, with microbial β-glucuronidases as an example, which break down drug-glucuronide conjugates and reactivate the drugs or their metabolites. We identified 100 medications that may be metabolized by β-glucuronidases from the gut microbiome. These medications included morphine, estrogen, ibuprofen, midazolam, and their structural analogues. The analysis of metagenomic data available through the Sequence Read Archive (SRA) showed that the level of β-glucuronidase in the gut metagenomes was higher in males than in females, which provides a potential explanation for the sex-based differences in efficacy and toxicity for several drugs, reported in previous studies. Our analysis also showed that infant gut metagenomes at birth and 12 months of age have higher levels of β-glucuronidase than the metagenomes of their mothers and the implication of this observed variability was discussed in the context of breastfeeding as well as infant hyperbilirubinemia. Overall, despite important limitations discussed in this paper, our analysis provided useful insights on the role of the human gut metagenome in the variability in drug response among individuals. Importantly, this approach exploits drug and metagenome data available in public databases as well as open-source cheminformatics and bioinformatics tools to predict drug-metagenome interactions.

59 BASIC BIOLOGICAL SCIENCES↗