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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Pathway modeling of microarray data: A case study of pathway activity changes in the testis following in utero exposure to dibutyl phthalate (DBP)

Pathway activity level analysis, the approach pursued in this study, focuses on all genes that are known to be members of metabolic and signaling pathways as defined by the KEGG database. The pathway activity level analysis entails singular value decomposition (SVD) of the expression data of the genes constituting a given pathway. We explore an extension of the pathway activity methodology for application to time-course microarray data. We show that pathway analysis enhances our ability to detect biologically relevant changes in pathway activity using synthetic data. As a case study, we apply the pathway activity level formulation coupled with significance analysis to microarray data from two different rat testes exposed in utero to Dibutyl Phthalate (DBP). In utero DBP exposure in the rat results in developmental toxicity of a number of male reproductive organs, including the testes. One well-characterized mode of action for DBP and the male reproductive developmental effects is the repression of expression of genes involved in cholesterol transport, steroid biosynthesis and testosterone synthesis that lead to a decreased fetal testicular testosterone. Previous analyses of DBP testes microarray data focused on either individual gene expression changes or changes in the expression of specific genes that are hypothesized, or known, to be important in testicular development and testosterone synthesis. However, a pathway analysis may inform whether there are additional affected pathways that could inform additional modes of action linked to DBP developmental toxicity. We show that Pathway activity analysis may be considered for a more comprehensive analysis of microarray data.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Thermodynamic modeling of adsorption at the liquid-solid interface

Adsorptive separation techniques are significantly energy efficient in comparison to conventional thermal separation techniques such as distillation. Despite extensive research and development activities undertaken for mixed-gas adsorption, the use of adsorption techniques for the separation of multicomponent liquid mixtures is still limited. A major barrier is the lack of accurate adsorption thermodynamic models, which form the scientific foundation of process simulation of such systems, making the translation to industrial scale challenging. In this work, we have rigorously computed the surface excess of adsorption for six binary liquid mixtures on silica gel at 303 K using the frameworks of the generalized Langmuir isotherm (gL) and the adsorbed solution theory (AST). The six binary liquid mixtures were formed by the pair-wise combinations of four components: benzene, 1,2-dichloroethane, cyclohexane, and n-heptane. We have based our calculations by considering simultaneous equilibria of three phases: saturated vapor phase, bulk liquid phase, and adsorbed phase. Further, the composition of the corresponding saturated vapor phase was estimated by the Nonrandom Two-Liquid activity coefficient model and experimental vapor-liquid equilibria data. The activity coefficients of the adsorbed phase, the central issue of multicomponent adsorption thermodynamics, were calculated using the adsorption Nonrandom Two-Liquid activity coefficient model. Devoid of simplifying assumptions, gL and AST provide rigorous thermodynamic frameworks for adsorption equilibria of multicomponent liquid mixtures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Open Humans: A platform for participant-centered research and personal data exploration

Background: Many aspects of our lives are now digitized and connected to the internet. As a result, individuals are now creating and collecting more personal data than ever before. This offers an unprecedented chance for human-participant research ranging from the social sciences to precision medicine. With this potential wealth of data comes practical problems (e.g., how to merge data streams from various sources), as well as ethical problems (e.g., how best to balance risks and benefits when enabling personal data sharing by individuals). Results: To begin to address these problems in real time, we present Open Humans, a community-based platform that enables personal data collections across data streams, giving individuals more personal data access and control of sharing authorizations, and enabling academic research as well as patient-led projects. We showcase data streams that Open Humans combines (e.g., personal genetic data, wearable activity monitors, GPS location records, and continuous glucose monitor data), along with use cases of how the data facilitate various projects. Conclusions: Open Humans highlights how a community-centric ecosystem can be used to aggregate personal data from various sources, as well as how these data can be used by academic and citizen scientists through practical, iterative approaches to sharing that strive to balance considerations with participant autonomy, inclusion, and privacy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Spectroscopic Evidence of Alfvén Wave Damping in the Off-limb Solar Corona

We investigate the off-limb active-region and quiet-Sun corona using spectroscopic data. The active region is clearly visible in several spectral lines formed in the temperature range of 1.1–2.8 MK. We derive the electron number density using the line ratio method, and the nonthermal velocity in the off-limb region up to the distance of 140 Mm. We compare density scale heights derived from several spectral line pairs with expected scale heights per the hydrostatic equilibrium model. Using several isolated and unblended spectral line profiles, we estimate nonthermal velocities in the active region and quiet Sun. Nonthermal velocities obtained from warm lines in the active region first show an increase and then later either a decrease or remain almost constant with height in the far off-limb region, whereas nonthermal velocities obtained from hot lines show consistent decrease. However, in the quiet-Sun region, nonthermal velocities obtained from various spectral lines show either a gradual decrease or remain almost constant with height. Using these obtained parameters, we further calculate Alfvén wave energy flux in both active and quiet-Sun regions. We find a significant decrease in wave energy fluxes with height, and hence provide evidence of Alfvén wave damping. Furthermore, we derive damping lengths of Alfvén waves in the both regions and find them to be in the range of 25–170 Mm. Different damping lengths obtained at different temperatures may be explained as either possible temperature-dependent damping or by measurements obtained in different coronal structures formed at different temperatures along the line of sight. Temperature-dependent damping may suggest some role of thermal conduction in the damping of Alfvén waves in the lower corona.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Active Subspace Methods for Data-Intensive Inverse Problems

The project has developed theory and computational tools to exploit active subspaces to reduce the dimension in statistical calibration problems. This dimension reduction enables MCMC methods to calibrate otherwise intractable models. The same theoretical and computational tools can also reduce the measurement dimension for calibration problems that use large stores of data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Improved evaluation of safeguards parameters from spent fuel measurements with the Differential Die-Away (DDA) instrument

The Differential Die-Away (DDA) technique is a highly sensitive non-destructive assay method for characterizing and detecting the presence of fissile material within an item of interest. DDA utilizes a series of pulses from a neutron generator (NG) to actively interrogate an item of interest. The die-away time of the neutron population induced by this active interrogation and the integral of the total differential die-away signal can be used to characterize items such as nuclear waste drums and spent nuclear fuel assemblies. In this work, Los Alamos National Laboratory (LANL) conceptualized, designed, and fabricated a DDA instrument that was deployed for field test measurements at the Central Interim Storage Facility for Spent Nuclear Fuel (Clab) in Oskarshamn, Sweden. The instrument performed multiple static measurements at fixed locations and dynamic axial scans of 15 pressurized water reactor (PWR) and 10 boiling water reactor (BWR) spent fuel assemblies, collecting both passive and active measurement data. The static assays of the assemblies measured the differential die-away signal, die-away time, and total passive neutron emission rate to create calibration curves for the evaluation of assembly multiplication, burnup, initial enrichment, effective fissile mass, and total elemental plutonium mass. Each calibration curve was optimized by minimizing the relative root mean square error (RRMSE) of assembly assay results compared to declared assembly parameters. The same quantities were also measured with the axial scans, and the resulting data were applied in two ways: (1) in the creation of calibration curves to improve evaluation of the same safeguards parameters as static assays, and (2) for comparison to simulation. In most cases, across both PWR and BWR assemblies, axial scan data improved the estimation of the above parameters, quantified by decreasing the calibration curve RRMSE. These axial scan results demonstrate the ability of the DDA instrument and analysis method to characterize spent PWR and BWR fuel as well as, or better than, a static assay of the same assembly. Furthermore, the DDA instrument’s unique ability to obtain both active and passive data in a single, axial scan of an entire spent fuel assembly represents a more efficient and accurate way of assaying spent fuel for verification purposes. These results represent a significant advancement for characterizing spent nuclear fuel compared to current technologies.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

Model Evaluation Report for Corrective Action Unit 98: Frenchman Flat, Nevada National Security Site, Nye County, Nevada, Revision 1

Model evaluation focused solely on the PIN STRIPE and MILK SHAKE underground nuclear tests’ contaminant boundaries (CBs) because they had the largest extent, uncertainty, and potential consequences. The CAMBRIC radionuclide migration experiment also had a relatively large CB, but because it was constrained by transport data (notably Well UE-5n), there was little uncertainty, and radioactive decay reduced concentrations before much migration could occur. Each evaluation target and the associated data-collection activity were assessed in turn to determine whether the new data support, or demonstrate conservatism of, the CB forecasts. The modeling team—in this case, the same team that developed the Frenchman Flat geologic, source term, and groundwater flow and transport models—analyzed the new data and presented the results to a PER committee. Existing site understanding and its representation in numerical groundwater flow and transport models was evaluated in light of the new data and the ability to proceed to the CR stage of long-term monitoring and institutional control.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Low Activity Waste Glass Waste Form Data Package for Hanford's Integrated Disposal Facility Performance Assessment - 16480

A Direct Feed Low Activity Waste (DFLAW) flowsheet has been initiated to begin immobilization of Hanford tank wastes. In the DFLAW configuration, a pretreatment system will segregate the high level and the low activity wastes (LAW). The LAW will be immobilized in the Waste Treatment Plant (WTP) and disposed of in Hanford's Integrated Disposal Facility (IDF). To obtain authorization to dispose of wastes in the IDF, a Performance Assessment (PA) must be completed in accordance with DOE Order 435.1. The PA shall include calculations of potential doses to representative future members of the public and potential releases from the facility to provide a reasonable expectation that performance objectives will not be exceeded as a result of operation and closure of the facility. To support the development of the PA, data from ILAW glass testing are needed to model the long term release of contaminants over the compliance period and verify that impacts to groundwater quality will be within performance objective limits. Washington River Protection Solutions is conducting an ILAW glass testing program to produce the required data. This program is a combined effort involving National Laboratories with known expertise in glass waste form research. The work involves accelerated testing to develop parameters that can be used in computer simulations of glass corrosion and contaminant release in the IDF environment. The results of this work will be compiled in an ILAW glass waste form performance data package for the IDF PA. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Active Subspace Methods for Data-Intensive Inverse Problems

Fostered by advances in computing, simulation and data science have emerged as the third and fourth paradigms enabling scientific discovery. Many mathematical challenges arise when integrating these paradigms to produce new knowledge. One of the most important challenges is the inverse or calibration problem: find the inputs of a simulation such that its outputs agree with a given set of measurements. As both the cost of the complex simulations and the volume of measurement data sets increase, intuition-driven trial-and-error methods for calibration quickly become untenable; modern scientists need more computationally efficient and mathematically rigorous methods. When measurements and simulated predictions contain uncertainty, the Bayesian perspective on statistical inference provides a popular mathematical framework for developing computational methods to solve inverse problems. Markov chain Monte Carlo (MCMC) methods are a mainstay of this framework; they characterize a probability distribution on the space of model inputs that is conditioned on the given measurements. However, despite recent advances, MCMC methods remain impractical for expensive simulations with more than a handful of inputs. At its heart, MCMC searches the space of inputs for corresponding outputs that match measurements, and so it is afflicted with the curse of dimensionality. Additionally, MCMC methods become less efficient as the volume of the measurement data increases, particularly if the measurements are redundant. To make MCMC—and Bayesian methods, in general—amenable to today’s computer models with millions of inputs and terabytes of measurement data, one must devise methods to reduce the dimension of both the simulation inputs and the measurements. For many models used in practice, the outputs vary primarily on a low-dimensional manifold of the nominally high-dimensional input space. Recently developed active subspace methods discover this low-dimensional manifold and exploit it to enable analyses typically suited for low-dimensional settings (e.g., statistical inversion of complex systems). We hypothesize that active subspace methods can provide the necessary dimension reduction, enabling existing Bayesian methods to find solutions for a large class of otherwise intractable statistical inverse problems. We will test the hypothesis with a series of mathematical analyses and computational experiments that will proceed along three research veins: (i) integrating active subspace methods into methods for statistical inversion, (ii) addressing current challenges in active subspace methods, and (iii) incorporating methods for nonlinear dimensionality reduction into active subspace methods. We will apply the methods we develop to real inverse problems in chemical kinetics and turbulent flame modeling. Our team combines expertise in methods for inverse problems, dimension reduction, and reduced order modeling. With a history of active collaboration and thoughtful engagement, we bring enthusiasm and fresh perspectives to a well studied subject. We are excited by the potential opportunity to work together to extend and apply active subspace methods to data-intensive inverse problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Active Subspace Methods for Data-Intensive Inverse Problems (Final Report)

Fostered by advances in computing, simulation and data science have emerged as the third and fourth paradigms enabling scientific discovery. Many mathematical challenges arise when integrating these paradigms to produce new knowledge. One of the most important challenges is the inverse or calibration problem: find the inputs of a simulation such that its outputs agree with a given set of measurements. As both the cost of the complex simulations and the volume of measurement data sets increase, intuition-driven trial-and-error methods for calibration quickly become untenable; modern scientists need more computationally efficient and mathematically rigorous methods. When measurements and simulated predictions contain uncertainty, the Bayesian perspective on statistical inference provides a popular mathematical framework for developing computational methods to solve inverse problems. Markov chain Monte Carlo (MCMC) methods are a mainstay of this framework; they characterize a probability distribution on the space of model inputs that is conditioned on the given measurements. However, despite recent advances, MCMC methods remain impractical for expensive simulations with more than a handful of inputs. At its heart, MCMC searches the space of inputs for corresponding outputs that match measurements, and so it is afflicted with the curse of dimensionality. Additionally, MCMC methods become less efficient as the volume of the measurement data increases, particularly if the measurements are redundant. To make MCMC—and Bayesian methods, in general—amenable to today’s computer models with millions of inputs and terabytes of measurement data, one must devise methods to reduce the dimension of both the simulation inputs and the measurements. For many models used in practice, the outputs vary primarily on a low-dimensional manifold of the nominally high-dimensional input space. Recently developed active subspace methods discover this low-dimensional manifold and exploit it to enable analyses typically suited for low-dimensional settings (e.g., statistical inversion of complex systems). We hypothesize that active subspace methods can provide the necessary dimension reduction, enabling existing Bayesian methods to find solutions for a large class of otherwise intractable statistical inverse problems. We will test the hypothesis with a series of mathematical analyses and computational experiments that will proceed along three research veins: (i) integrating active subspace methods into methods for statistical inversion, (ii) addressing current challenges in active subspace methods, and (iii) incorporating methods for nonlinear dimensionality reduction into active subspace methods. We will apply the methods we develop to real inverse problems in chemical kinetics and turbulent flame modeling. Our team combines expertise in methods for inverse problems, dimension reduction, and reduced order modeling. With a history of active collaboration and thoughtful engagement, we bring enthusiasm and fresh perspectives to a well studied subject. We are excited by the potential opportunity to work together to extend and apply active subspace methods to data-intensive inverse problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

MOVES Activity Updates Using Fleet DNA Data: Interim Report

The U.S. Environmental Protection Agency's Motor Vehicle Emissions Simulator (MOVES) is a publicly available tool used by researchers and policy makers to help understand motor vehicle emission sources at national, county, and project levels. However, estimates of heavy-duty activity (MOVES2014), have been identified as areas needing improvement. The start activities in MOVES2014 are calculated using a limited data set, prompting the concern that inventory values are not representative. In addition, MOVES2014 is believed to underestimate heavy-duty activity that is not captured in the current drive cycles used to represent on-network activity. For example, MOVES2014 does not account for work-day idling activity that takes place on off-network roads, such as at a distribution center while the truck is queuing or during loading and unloading. Under the guidance and expertise of the EPA, the National Renewable Energy Laboratory has leveraged its extensive Fleet DNA database of heavy-duty vehicles to reinforce the data set behind the next-generation MOVES model and enhance idle activity and start fractions using six heavy-duty vehicle classes. The data available in Fleet DNA from 420 conventional, diesel powered vehicles provided over 120,000 hours of operation. Start fraction, soak fraction, and idle fraction by hour of the day were derived for each source type, state, and vocation, and results were provided in the form of .CSV files representing MOVES table inputs. This midterm report details these results providing graphical analysis and context for the start, soak, and idle distributions.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2013

DETERMINATION OF THE POINT-SPREAD FUNCTION FOR THE FERMI LARGE AREA TELESCOPE FROM ON-ORBIT DATA AND LIMITS ON PAIR HALOS OF ACTIVE GALACTIC NUCLEI

The Large Area Telescope (LAT) on the Fermi Gamma-ray Space Telescope is a pair-conversion telescope designed to detect photons with energies from Almost-Equal-To 20 MeV to >300 GeV. The pre-launch response functions of the LAT were determined through extensive Monte Carlo simulations and beam tests. The point-spread function (PSF) characterizing the angular distribution of reconstructed photons as a function of energy and geometry in the detector is determined here from two years of on-orbit data by examining the distributions of {gamma} rays from pulsars and active galactic nuclei (AGNs). Above 3 GeV, the PSF is found to be broader than the pre-launch PSF. We checked for dependence of the PSF on the class of {gamma}-ray source and observation epoch and found none. We also investigated several possible spatial models for pair-halo emission around BL Lac AGNs. We found no evidence for a component with spatial extension larger than the PSF and set upper limits on the amplitude of halo emission in stacked images of low- and high-redshift BL Lac AGNs and the TeV blazars 1ES0229+200 and 1ES0347-121.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Seismic Characterization of the Blue Mountain Geothermal Field

Subsurface characterization is crucial for geothermal energy exploration and production. Yet hydrothermal reservoirs usually reside in highly fractured and faulted zones where accurate characterization is very challenging because of low signal-to-noise ratios of land seismic data and lack of coherent reflection signals. We perform an active-source seismic characterization for the Blue Mountain geothermal field in Nevada using active seismic data to reveal the elastic medium property complexity and fault distribution at this field. We first employ an unsupervised machine learning method to attenuate groundroll and near-surface guided-wave noise and enhance coherent reflection and scattering signals from noisy seismic data. We then build a smooth initial P-wave velocity model based on an existing magnetotellurics survey result, and use 3D first-arrival traveltime tomography to refine the initial velocity model. We then derive a set of elastic wave velocities and anisotropic parameters using elastic full-waveform inversion, and obtain PP and PS images using elastic reverse-time migration. We identify major faults by analyzing the variations of seismic velocities and anisotropy parameters, and reveal mid- to small-scale faults by applying a supervised machine learning method to the seismic migration images. Our characterization reveals complex velocity heterogeneities and anisotropies, as well as faults, with a high spatial resolution. These results can provide valuable information for optimal placement of future injection and production wells to increase geothermal energy production at the Blue Mountain geothermal power plant.

58 GEOSCIENCES↗
DOE OSTI2013

Charged particle detectors with active detector surface for partial energy deposition of the charged particles and related methods

A radiation detector is disclosed. The radiation detector comprises an active detector surface configured to generate charge carriers in response to charged particles associated with incident radiation. The active detector surface is further configured with a sufficient thickness for a partial energy deposition of the charged particles to occur and permit the charged particles to pass through the active detector surface. The radiation detector further comprises a plurality of voltage leads coupled to the active detector surface. The plurality of voltage leads is configured to couple to a voltage source to generate a voltage drop across the active detector surface and to separate the charge carriers into a plurality of electrons and holes for detection. The active detector surface may comprise one or more graphene layers. Timing data between active detector surfaces may be used to determine energy of the incident radiation. Other apparatuses and methods are disclosed herein.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Three dimensional low-mode areal-density non-uniformities in indirect-drive implosions at the National Ignition Facility

To achieve hotspot ignition, an inertial confinement fusion implosion must achieve high hotspot pressure that is inertially confined by a dense shell of DT fuel. This requires a symmetric implosion having high in-flight shell velocity and high areal density at stagnation. The size of the driver and scale of the capsule required can be minimized by maintaining a high efficiency of energy coupling from the imploding shell to the hotspot. Significant 3D low mode asymmetries, however, are commonly observed in indirect-drive implosions and reduce the coupling of shell kinetic energy to the hotspot. To better quantify the magnitudes and impacts of shell density asymmetries, we have developed new analysis techniques and analytic models [Hurricane et al., Phys. Plasmas 27(6), 062704 (2020)]. To build confidence in the underlying data, we have also developed an analytic neutron transport model to cross-compare two independent measurements of asymmetry, which shows excellent agreement across shots for mode-1 (ℓ = 1). This work also demonstrates that asymmetry can introduce potential sampling bias into down-scattered ratio measurements causing the solid-angle-average and uncertainty-weighted-average down-scattered ratios to differ significantly. Diagnosing asymmetries beyond mode-1 (ℓ > 1) presents significant challenges. Using new diagnostic instruments and analysis techniques, however, evidence of significant Legendre mode P2 (ℓ = 2, m = 0) and additional 3D asymmetries (ℓ > 1, m ≠ 0) are beginning to emerge from the high precision activation diagnostic data (real-time nuclear activation detectors) and down-scattered neutron imaging data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Weighted active space protocol for multireference machine-learned potentials

Multireference methods such as multiconfiguration pair-density functional theory accurately capture electronic correlation in systems with strong multiconfigurational character, but their cost precludes direct use in molecular dynamics. Combining these methods with machine-learned interatomic potentials (MLPs) can extend their reach. However, the sensitivity of multireference calculations to the choice of the active space complicates the consistent evaluation of energies and gradients across structurally diverse nuclear configurations. To overcome this limitation, we introduce the weighted active space protocol (WASP), a systematic approach to assign a consistent active space for a given system across uncorrelated configurations. By integrating WASP with MLPs and enhanced sampling techniques, we propose a data-efficient active learning cycle that enables the training of an MLP on multireference data. We demonstrated the approach on the TiC + -catalyzed C–H activation of methane, a reaction that poses challenges for Kohn–Sham density functional theory due to its significant multireference character. This framework enables accurate and efficient modeling of catalytic dynamics, establishing a paradigm for simulating complex reactive processes beyond the limits of conventional electronic-structure methods.

enhanced sampling↗
DOE OSTI2019

Formylglycine-generating enzyme binds substrate directly at a mononuclear Cu(I) center to initiate O 2 activation

The formylglycine-generating enzyme (FGE) is required for the posttranslational activation of type I sulfatases by oxidation of an active-site cysteine to C α -formylglycine. FGE has emerged as an enabling biotechnology tool due to the robust utility of the aldehyde product as a bioconjugation handle in recombinant proteins. Here, we show that Cu(I)–FGE is functional in O 2 activation and reveal a high-resolution X-ray crystal structure of FGE in complex with its catalytic copper cofactor. We establish that the copper atom is coordinated by two active-site cysteine residues in a nearly linear geometry, supporting and extending prior biochemical and structural data. The active cuprous FGE complex was interrogated directly by X-ray absorption spectroscopy. These data unambiguously establish the configuration of the resting enzyme metal center and, importantly, reveal the formation of a three-coordinate tris(thiolate) trigonal planar complex upon substrate binding as furthermore supported by density functional theory (DFT) calculations. Critically, inner-sphere substrate coordination turns on O 2 activation at the copper center. These collective results provide a detailed mechanistic framework for understanding why nature chose this structurally unique monocopper active site to catalyze oxidase chemistry for sulfatase activation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗