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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Soil Respiration Variability and Correlation Across a Wide Range of Temporal Scales

Abstract The high temporal variability of the soil‐to‐atmosphere CO 2 flux (soil respiration, R S ) has been studied at hourly to multiannual time scales but remains less well understood than R S spatial variability. How R S fluxes vary and are autocorrelated at various time lags has practical implications for sampling and more fundamentally for our understanding of its abiotic and biotic underlying mechanisms. We examined the variability, correlation, and sampling requirements of R S over a wide range of temporal scales in a temperate deciduous forest in eastern Maryland, USA, using both automated (temporally continuous, N = 30,036 over 10 months) and survey (spatially diverse, temporally sparse, N = 1,912 over 17 months) data. Data from a global R S database were also used to examine interannual variability in comparable forests. The coefficient of variability of successive measurements generally varied from the minute (median coefficient of variation 16%) to hourly and daily (11–12%) time scales. Successive R S values measured at a given collar exhibited a strong hour‐to‐hour correlation ( r = 0.931) and a moderate correlation at a 2‐hr lag (0.289); day‐to‐day (i.e., 24 hr lag) hourly observations were uncorrelated. Daily R S means were well correlated at a 1‐day lag ( r = 0.856) but not at any further time lag. In a linear mixed‐effects model predicting R S , soil temperature and moisture exerted consistently strong effects regardless of time scale, and model coefficient of variability was generally high (>80%). These results provide new opportunities to explore the drivers and variability of R S fluxes, quantify sampling requirements, and improve error propagation.

Bond‐Lamberty, Ben↗
DOE OSTI2021

Automatic detection of impact craters on Al foils from the Stardust interstellar dust collector using convolutional neural networks

NASA's Stardust mission utilized a sample collector composed of aerogel and aluminum foil to return cometary and interstellar particles to Earth. Analysis of the aluminum foil begins with locating craters produced by hypervelocity impacts of cometary and interstellar dust. Interstellar dust craters are typically less than one micrometer in size and are sparsely distributed, making them difficult to find. In this paper, we describe a convolutional neural network based on the VGG16 architecture that achieves high specificity and sensitivity in locating impact craters in the Stardust interstellar collector foils. Finally, we evaluate its implications for current and future analyses of Stardust samples.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

BRE‐X Emissions Database for End‐of‐Life Scenarios of Selective Building Construction Materials to Enable Circular Economy in Construction

In the United States, construction and demolition debris predominately end up in landfills with minimal end‐of‐life Re‐X (recover, recycle, reuse, etc.) scenarios, resulting in large environmental impacts and lost opportunities for material recovery. Except for concrete and metals, which seem to have a few well‐defined end‐of‐life pathways, there seems to be a lack of well‐documented end‐of‐life scenarios for other construction materials, let alone their emissions data. Hence, there is a need for documented end‐of‐life Re‐X scenarios and end‐of‐life data of more building materials to motivate widespread use of Re‐X strategies in building design. This paper outlines the efforts of the National Renewable Energy Laboratory, Carbon Leadership Forum, Building Transparency, and Skidmore, Owings & Merrill to (a) create an open‐access BRE‐X (Building Re‐X) end‐of‐life emissions database consisting of greenhouse gas emissions data associated with various end‐of‐life scenarios for a select list of high‐impact building construction materials, and (b) integrate the BRE‐X end‐of‐life emissions database with CAD/BIM/LCA tools for evaluating various end‐of‐life scenarios. The paper also presents a few existing life cycle inventory databases that contain sparse amounts of end‐of‐life data for a few construction materials and their limitations in terms of scaling and data consolidation. Finally, a sample of how the collected data can be ingested into whole‐building LCA tools using open data formats and a public access link to the BRE‐X end‐of‐life emissions database is also included.

36 MATERIALS SCIENCE↗
DOE OSTI2014

The Modular Borehole Monitoring Program. A research program to optimize well-based monitoring for geologic carbon sequestration

Understanding the impacts caused by injection of large volumes of CO 2 in the deep subsurface necessitates a comprehensive monitoring strategy. While surface-based and other remote geophysical methods can provide information on the general morphology of a CO 2 plume, verification of the geochemical conditions and validation of the remote sensing data requires measurements from boreholes that penetrate the storage formation. Unfortunately, the high cost of drilling deep wellbores and deploying instrumentation systems constrains the number of dedicated monitoring borings as well as limits the technologies that can be incorporated in a borehole completion. The objective of the Modular Borehole Monitoring (MBM) Program was to develop a robust suite of well-based tools optimized for subsurface monitoring of CO 2 that could meet the needs of a comprehensive well-based monitoring program. It should have enough flexibility to be easily reconfigured for various reservoir geometries and geologies. The MBM Program sought to provide storage operators with a turn-key fully engineered design that incorporated key technologies, function over the decades long time-span necessary for post-closure reservoir monitoring, and meet industry acceptable risk profiles for deep-well installations. While still within the conceptual design phase of the MBM program, the SECARB Anthropogenic Test in Citronelle, Alabama, USA was identified as a deployment site for our engineered monitoring systems. The initial step in designing the Citronelle MBM system was to down-select from the various monitoring tools available to include technologies that we considered essential to any program. Monitoring methods selected included U-tube geochemical sampling, discrete quartz pressure and temperature gauges, an integrated fibre-optic bundle consisting of distributed temperature and heat-pulse sensing, and a sparse string of conventional 3C-geophones. While not originally planned within the initial MBM work scope, the fibre-optic cable was able to also be used for the emergent technology of distributed acoustic sensing. The MBM monitoring string was installed in March, 2012. To date, the Citronelle MBM instruments continue to operate reliably. Results and lessons learned from the Citronelle MBM deployment are addressed along with examples of data being collected.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Transferring a Molecular Foundation Model for Polymer Property Predictions

Transformer-based large language models have remarkable potential to accelerate design optimization for applications such as drug development and material discovery. Self-supervised pretraining of transformer models requires large-scale data sets, which are often sparsely populated in topical areas such as polymer science. Further, state-of-the-art approaches for polymers conduct data augmentation to generate additional samples but unavoidably incur extra computational costs. In contrast, large-scale open-source data sets are available for small molecules and provide a potential solution to data scarcity through transfer learning. In this work, we show that using transformers pretrained on small molecules and fine-tuned on polymer properties achieves comparable accuracy to those trained on augmented polymer data sets for a series of benchmark prediction tasks.

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

SPIND : a reference-based auto-indexing algorithm for sparse serial crystallography data

SPIND (sparse-pattern indexing) is an auto-indexing algorithm for sparse snapshot diffraction patterns (`stills') that requires the positions of only five Bragg peaks in a single pattern, when provided with unit-cell parameters. The capability of SPIND is demonstrated for the orientation determination of sparse diffraction patterns using simulated data from microcrystals of a small inorganic molecule containing three iodines, 5-amino-2,4,6-triiodoisophthalic acid monohydrate (I3C) [Beck & Sheldrick (2008), Acta Cryst. E 64 , o1286], which is challenging for commonly used indexing algorithms. SPIND , integrated with CrystFEL [White et al. (2012), J. Appl. Cryst. 45 , 335–341], is then shown to improve the indexing rate and quality of merged serial femtosecond crystallography data from two membrane proteins, the human δ-opioid receptor in complex with a bi-functional peptide ligand DIPP-NH 2 and the NTQ chloride-pumping rhodopsin (CIR). The study demonstrates the suitability of SPIND for indexing sparse inorganic crystal data with smaller unit cells, and for improving the quality of serial femtosecond protein crystallography data, significantly reducing the amount of sample and beam time required by making better use of limited data sets. SPIND is written in Python and is publicly available under the GNU General Public License from https://github.com/LiuLab-CSRC/SPIND.

36 MATERIALS SCIENCE↗
DOE OSTI2018

An adaptive local reduced basis method for solving PDEs with uncertain inputs and evaluating risk

Many physical systems are modeled using partial differential equations (PDEs) with uncertain or random inputs. For such systems, naively propagating a fixed number of samples of the input probability law (or an approximation thereof) through the PDE is often inadequate to accurately quantify the "risk" associated with critical system responses. In this paper, we develop a goal-oriented, adaptive sampling and local reduced basis approximation for PDEs with random inputs. Our method determines a set of samples and an associated (implicit) Voronoi partition of the parameter domain on which we build local reduced basis approximations of the PDE solution. The samples are selected in an adaptive manner using an a posteriori error indicator. A notable advantage of the proposed approach is that the computational cost of the approximation during the adaptive process remains constant. We provide theoretical error bounds for our approximation and numerically demonstrate the performance of our method when compared to widely used adaptive sparse grid techniques. In addition, we tailor our approach to accurately quantify the risk of quantities of interest that depend on the PDE solution. We demonstrate our method on an advection-diffusion example and a Helmholtz example. Keywords: stochastic partial differential equation, adaptive sampling, reduced basis, a posteriori error estimation, coherent risk measure, conditional value-at-risk.

42 ENGINEERING↗
DOE OSTI2018

Time and Frequency Domain Methods for Basis Selection in Random Linear Dynamical Systems

Polynomial chaos methods have been extensively used to analyze systems in uncertainty quantification. Furthermore, several approaches exist to determine a low-dimensional approximation (or sparse approximation) for some quantity of interest in a model, where just a few orthogonal basis polynomials are required. In this work, we consider linear dynamical systems consisting of ordinary differential equations with random variables. The aim of this paper is to explore methods for producing low-dimensional approximations of the quantity of interest further. We investigate two numerical techniques to compute a low-dimensional representation, which both fit the approximation to a set of samples in the time domain. On the one hand, a frequency domain analysis of a stochastic Galerkin system yields the selection of the basis polynomials. It follows a linear least squares problem. On the other hand, a sparse minimization yields the choice of the basis polynomials by information from the time domain only. An orthogonal matching pursuit produces an approximate solution of the minimization problem. Finally, we compare the two approaches using a test example from a mechanical application.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Ordering Polymer‐Grafted Nanoparticles at Oil–Air Interfaces under Magnetic Fields

Abstract Polymer‐grafted magnetic nanoparticles at oil–air interfaces are examined to reveal the role of chain length and anisotropy on particle packing order in thin films. It is found that particles grafted with intermediate chain lengths and sparse grafting densities exhibit enhanced packing order with increasing magnetic field strength. Voronoi tessellation results present an increase in the cell area distribution of these samples, suggesting that chain conformations are affected. For the longest graft length, particles become more disordered under magnetic fields. It is proposed that fluctuations in the bridged chains rearrange particles into less ordered packing with field application and the mechanism of packing order differs for varying graft chain lengths. Grafting anisotropy is found to determine the spatial nanoparticle organization in assembled monolayers.

Liu, Siqi↗
DOE OSTI2020

Robust framework and software implementation for fast speciation mapping

One of the greatest benefits of synchrotron radiation is the ability to perform chemical speciation analysis through X-ray absorption spectroscopies (XAS). XAS imaging of large sample areas can be performed with either full-field or raster-scanning modalities. A common practice to reduce acquisition time while decreasing dose and/or increasing spatial resolution is to compare X-ray fluorescence images collected at a few diagnostic energies. In this work, several authors have used different multivariate data processing strategies to establish speciation maps. Furthermore, the theoretical aspects and assumptions that are often made in the analysis of these datasets are focused on. A robust framework is developed to perform speciation mapping in large bulk samples at high spatial resolution by comparison with known references. Two fully operational software implementations are provided: a user-friendly implementation within the MicroAnalysis Toolkit software, and a dedicated script developed under the R environment. The procedure is exemplified through the study of a cross section of a typical fossil specimen. Additionally, the algorithm provides accurate speciation and concentration mapping while decreasing the data collection time by typically two or three orders of magnitude compared with the collection of whole spectra at each pixel. Whereas acquisition of spectral datacubes on large areas leads to very high irradiation times and doses, which can considerably lengthen experiments and generate significant alteration of radiation-sensitive materials, this sparse excitation energy procedure brings the total irradiation dose greatly below radiation damage thresholds identified in previous studies. This approach is particularly adapted to the chemical study of heterogeneous radiation-sensitive samples encountered in environmental, material, and life sciences.

47 OTHER INSTRUMENTATION↗
DOE OSTI2015

Analysis of Dust Samples Collected from an Unused Spent Nuclear Fuel Interim Storage Container at Hope Creek, Delaware

In July, 2014, the Electric Power Research Institute and industry partners sampled dust on the surface of an unused canister that had been stored in an overpack at the Hope Creek Nuclear Generating Station for approximately one year. The foreign material exclusion (FME) cover that had been on the top of the canister during storage, and a second recently-removed FME cover, were also sampled. This report summarizes the results of analyses of dust samples collected from the unused Hope Creek canister and the FME covers. Both wet and dry samples of the dust/salts were collected, using SaltSmart(TM) sensors and Scotch-Brite(TM) abrasive pads, respectively. The SaltSmart(TM) samples were leached and the leachate analyzed chemically to determine the composition and surface load per unit area of soluble salts present on the canister surface. The dry pad samples were analyzed by X-ray fluorescence and by scanning electron microscopy to determine dust texture and mineralogy; and by leaching and chemical analysis to deter mine soluble salt compositions. The analyses showed that the dominant particles on the canister surface were stainless steel particles, generated during manufacturing of the canister. Sparse environmentally - derived silicates and aluminosilicates were also present. Salt phases were sparse, and consisted of mostly of sulfates with rare nitrates and chlorides. On the FME covers, the dusts were mostly silicates/aluminosilicates; the soluble salts were consistent with those on the canister surface, and were dominantly sulfates. It should be noted that the FME covers were w ashed by rain prior to sampling, which had an unknown effect of the measured salt loads and compositions. Sulfate salts dominated the assemblages on the canister and FME surfaces, and included Ca-SO 4 , but also Na-SO 4 , K-SO 4 , and Na-Al-SO 4 . It is likely that these salts were formed by particle - gas conversion reactions, either prior to, or after, deposition. These reactions involve reaction of carbonate, chloride, or nitrate salts with at mospheric SO 2 , sulfuric acid, or a mmonium sulfate to form sulfate minerals. The Na-Al-SO 4 phase is unusual, and may have formed by reaction of Na-Al containing phases in aluminum smelter emissions with SO 2 , also present in smelter emissions. An aluminum smelter is located in Camden, NJ, 40 miles NE of the Hope Creek Site.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2014

Selection of polynomial chaos bases via Bayesian model uncertainty methods with applications to sparse approximation of PDEs with stochastic inputs

Generalized polynomial chaos (gPC) expansions allow us to represent the solution of a stochastic system using a series of polynomial chaos basis functions. The number of gPC terms increases dramatically as the dimension of the random input variables increases. When the number of the gPC terms is larger than that of the available samples, a scenario that often occurs when the corresponding deterministic solver is computationally expensive, evaluation of the gPC expansion can be inaccurate due to over-fitting. We propose a fully Bayesian approach that allows for global recovery of the stochastic solutions, in both spatial and random domains, by coupling Bayesian model uncertainty and regularization regression methods. It allows the evaluation of the PC coefficients on a grid of spatial points, via (1) the Bayesian model average (BMA) or (2) the median probability model, and their construction as spatial functions on the spatial domain via spline interpolation. The former accounts for the model uncertainty and provides Bayes-optimal predictions; while the latter provides a sparse representation of the stochastic solutions by evaluating the expansion on a subset of dominating gPC bases. Moreover, the proposed methods quantify the importance of the gPC bases in the probabilistic sense through inclusion probabilities. We design a Markov chain Monte Carlo (MCMC) sampler that evaluates all the unknown quantities without the need of ad-hoc techniques. The proposed methods are suitable for, but not restricted to, problems whose stochastic solutions are sparse in the stochastic space with respect to the gPC bases while the deterministic solver involved is expensive. We demonstrate the accuracy and performance of the proposed methods and make comparisons with other approaches on solving elliptic SPDEs with 1-, 14- and 40-random dimensions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Adaptive polynomial chaos techniques for uncertainty quantification of a gas cooled fast reactor transient

Uncertainty quantification plays an increasingly important role in the nuclear community, especially with the rise of Best Estimate Plus Uncertainty methodologies. Sensitivity analysis, surrogate models, Monte Carlo sampling and several other techniques can be used to propagate input uncertainties. In recent years however polynomial chaos expansion has become a popular alternative providing high accuracy at affordable computational cost. This paper presents such polynomial chaos (PC) methods using adaptive sparse grids and adaptive basis set construction, together with an application to a Gas Cooled Fast Reactor transient. Comparison is made between a new sparse grid algorithm and the traditionally used technique proposed by Gerstner. An adaptive basis construction method is also introduced and is proved to be advantageous both from an accuracy and a computational point of view. As a demonstration the uncertainty quantification of a 50% loss of flow transient in the GFR2400 Gas Cooled Fast Reactor design was performed using the CATHARE code system. The results are compared to direct Monte Carlo sampling and show the superior convergence and high accuracy of the polynomial chaos expansion. Since PC techniques are easy to implement, they can offer an attractive alternative to traditional techniques for the uncertainty quantification of large scale problems. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Covariance operator estimation via adaptive thresholding

This paper studies sparse covariance operator estimation for nonstationary processes with sharply varying marginal variance and small correlation lengthscale. We introduce a covariance operator estimator that adaptively thresholds the sample covariance function using an estimate of the variance component. Building on recent results from empirical process theory, we derive an operator norm bound on the estimation error in terms of the sparsity level of the covariance and the expected supremum of a normalized process. Furthermore, our theory and numerical simulations demonstrate the advantage of adaptive threshold estimators over universal threshold and sample covariance estimators in nonstationary settings.

Al-Ghattas, Omar [University of Chicago, IL (Unite↗
DOE OSTI2018

Accelerated vortex dynamics across the magnetic 3D-to-2D crossover in disordered superconductors

Disorder can have remarkably disparate consequences in superconductors, driving superconductor–insulator transitions in ultrathin films by localizing electron pairs and boosting the supercurrent carrying capacity of thick films by localizing vortices (magnetic flux lines). Though the electronic 3D-to-2D crossover at material thicknesses d ~ ξ (coherence length) is well studied, a similarly consequential magnetic crossover at d ~ L c (pinning length) that should drastically alter material properties remains largely underexamined. According to collective pinning theory, vortex segments of length L c bend to adjust to energy wells provided by point defects. Consequently, if d truncates L c , a change from elastic to rigid vortex dynamics should increase the rate of thermally activated vortex motion S. Here, we characterize the dependence of S on sample thickness in Nb and cuprate films. The results for Nb are consistent with collective pinning theory, whereas creep in the cuprate is strongly influenced by sparse large precipitates. In conclusion, we leverage the sensitivity of S to d to determine the generally unknown scale L c , establishing a new route for extracting pinning lengths in heterogeneously disordered materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Estimating Highway Volumes Using Vehicle Probe Data - Proof of Concept: Preprint

This paper examines the feasibility of using sampled commercial probe data in combination with validated continuous counter data to accurately estimate vehicle volume across the entire roadway network, for any hour during the year. Currently either real time or archived volume data for roadways at specific times are extremely sparse. Most volume data are average annual daily traffic (AADT) measures derived from the Highway Performance Monitoring System (HPMS). Although methods to factor the AADT to hourly averages for typical day of week exist, actual volume data is limited to a sparse collection of locations in which volumes are continuously recorded. This paper explores the use of commercial probe data to generate accurate volume measures that span the highway network providing ubiquitous coverage in space, and specific point-in-time measures for a specific date and time. The paper examines the need for the data, fundamental accuracy limitations based on a basic statistical model that take into account the sampling nature of probe data, and early results from a proof of concept exercise revealing the potential of probe type data calibrated with public continuous count data to meet end user expectations in terms of accuracy of volume estimates.

25 ENERGY STORAGE↗
DOE OSTI2020

Combined Force-Frequency Sampling for Simulation of Systems Having Rugged Free Energy Landscapes.

An adaptive, machine learning-based sampling method is presented for simulation of systems having rugged, multidimensional free energy landscapes. The method's main strength resides in its ability to learn both from the frequency of visits to distinct states and the generalized force estimates that arise in a system as it evolves in phase space. This is accomplished by introducing a self-integrating artificial neural network, which generates an estimate of the free energy directly from its derivatives. The usefulness of the proposed combined approach is examined in the context of two concrete examples, namely, an alanine dipeptide molecule in water and a polymer diffusing through a narrow pore. This new method is found to be robust, faster, and more accurate than approaches that rely only on frequency-based or generalized force-based estimations. After combining the proposed approach with overfill protection and support for sparse data storage and training, the method is shown to be more effective than comparable, previously available techniques and capable of scaling efficiently to larger numbers of collective variables.

Sevgen, Emre↗
DOE OSTI2022

Probing eddy size and its effective mixing length in stably stratified roughness sublayer flows

Stably stratified roughness sublayer flows are ubiquitous yet remain difficult to represent in models and to interpret using field experiments. Here, continuous high-frequency potential temperature profiles from the forest floor up to 6.5 times the canopy height observed with distributed temperature sensing (DTS) are used to link eddy topology to roughness sublayer stability correction functions and coupling between air layers within and above the canopy. The experiments are conducted at two forest stands classified as hydrodynamically sparse and dense. Near-continuous profiles of eddy sizes (length scales) and effective mixing lengths for heat are derived from the observed profiles using a novel conditional sampling approach. The approach utilizes potential temperature isoline fluctuations from a statically stable background state. The transport of potential temperature by an observed eddy is assumed to be conserved (adiabatic movement) and we assume that irreversible heat exchange between the eddy and the surrounding background occurs along the (vertical) periphery of the eddy. This assumption is analogous to Prandtl's mixing-length concept, where momentum is transported rapidly vertically and then equilibrated with the local mean velocity gradient. A distinct dependence of the derived length scales on background stratification, height above ground, and canopy characteristics emerges from the observed profiles. Implications of these findings for (1) the failure of Monin–Obukhov similarity in the roughness sublayer and (2) above-canopy flow coupling to the forest floor are examined. The findings have practical applications in terms of analysing similar DTS data sets with the proposed approach, modelling roughness sublayer flows, and interpreting nocturnal eddy covariance measurements above tall forested canopies.

54 ENVIRONMENTAL SCIENCES↗