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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Determination of tungsten in geochemical reference material basalt Columbia River 2 by radiochemical neutron activation analysis and inductively coupled plasma mass spectrometry

Environmental tungsten (W) analyses are inhibited by a lack of reference materials and practical methods to remove isobaric and radiometric interferences. We present a method that evaluates the potential use of commercially available sediment, Basalt Columbia River-2 (BCR-2), as a reference material using neutron activation analysis (NAA) and mass spectrometry. Tungsten concentrations using both methods are in statistical agreement at the 95% confidence interval (92 ± 4 ng/g for NAA and 100 ±7 ng/g for mass spectrometry) with recoveries greater than 95%. These results indicate that BCR-2 may be suitable as a reference material for future studies.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2019

Materials informatics: From the atomic-level to the continuum

We report that in recent years materials informatics, which is the application of data science to problems in materials science and engineering, has emerged as a powerful tool for materials discovery and design. This relatively new field is already having a significant impact on the interpretation of data for a variety of materials systems, including those used in thermoelectrics, ferroelectrics, battery anodes and cathodes, hydrogen storage materials, polymer dielectrics, etc. Its practitioners employ the methods of multivariate statistics and machine learning in conjunction with standard computational tools (e.g., density-functional theory) to, for example, visualize and dimensionally reduce large data sets, identify patterns in hyperspectral data, parse microstructural images of polycrystals, characterize vortex structures in ferroelectrics, design batteries and, in general, establish correlations to extract important physics and infer structure-property-processing relationships. In this Overview, we critically examine the role of informatics in several important materials subfields, highlighting significant contributions to date and identifying known shortcomings. We specifically focus attention on the difference between the correlative approach of classical data science and the causative approach of physical sciences. From this perspective, we also outline some potential opportunities and challenges for informatics in the materials realm in this era of big data.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Impact of U-10Mo HALEU fuel element tolerances on the Massachusetts Institute of Technology reactor safety and operational performance – Thermal hydraulics

The U.S. is coordinating efforts for the conversion of six U.S. High Performance Research Reactors (USHPRR) including one critical facility from highly enriched uranium (HEU) to low-enriched uranium (LEU). In order to continue the mission of these reactors, including the Massachusetts of Institute of Technology Reactor (MITR), and achieve similar performance, high-assay low-enriched uranium (HALEU) with a high-density metallic alloy of uranium with 10 wt% molybdenum (U-10Mo) is being evaluated. The impact of the fabrication specification and tolerances was assessed following the preliminary design of the MITR LEU fuel elements using the U-10Mo monolithic alloy. This research focuses on the analysis of fabrication specification impact on thermal hydraulics (TH) characteristic of the MITR LEU core as a function of the variation of the relevant fuel specification parameters (e.g., coolant channel gap thickness, fuel plate thickness, etc.). The analyses are performed based on an all-fresh LEU fuel conversion plan identified in a preliminary safety analysis report submitted to the Nuclear Regulatory Commission. The reactor power margin to the onset of nucleate boiling (ONB) is assessed under the limiting safety system settings (LSSS), where a scram occurs, to ensure there is sufficient margin to the reactor safety limit, which is defined by the onset of flow instability that occurs after the ONB. The best estimate plus uncertainty approach is employed to analyze this TH characteristic, which yields realistic results while maintaining adequate conservatism, utilizing a statistical uncertainty propagation method with the STAT7 code. The TH characteristic is analyzed as a function of the variability of the specification parameters resulting from the fabrication process. The main findings of this study show that the MITR core can meet the TH safety and operational requirements at the all-LEU initial core startup (cycle 1), selected transition cycles (most reactive cycle and most limiting cycle: cycle 3 and 5, respectively) and equilibrium (cycle 14) cores under all limiting fabrication parameter combinations considered. In addition, the analyses show that the dependency of the core power margin to ONB on those specification parameters that have the most direct impact on TH performance is non-linear but monotonically decreasing within the specification tolerances. The third order polynomial fit curves are reported in detail for selected limiting cases and can serve as a powerful tool for future MITR fuel management in cases such as when HALEU supply is established that may allow additional cycle length or other operational benefits.

Conversion↗
DOE OSTI2018

Design of machine learning models with domain experts for automated sensor selection for energy fault detection

Data-driven techniques that extract insights from sensor data reduce the cost of improving system energy performance through fault detection and system health monitoring. To lower cost barriers to widespread deployment, a methodology is proposed that takes advantage of existing sensor data, encodes expert knowledge about the application system to create ‘virtual sensors’ and applies statistical and mathematical methods to reduce the time required for manual configurations. The approach combines sensor data points with encoded expert knowledge that is generic to the application system but independent of a particular deployment, thereby reducing the need to tailor to individual deployments. Additionally, this paper not only presents a method that detects faults from measured energy data, but also (1) describes an engagement method with experts in the energy system domain to identify data, (2) integrates domain knowledge with the data, (3) automatically selects from among the large pool of potential input data, and (4) uses machine learning to automatically build a data-driven fault detection model. Demonstration on a commercial building chiller plant shows that only a small number of virtual sensors is necessary for fault detection with high accuracy rates. This corresponds to the use of only five out of 52 original sensor data points features. With as few as four features, classification F1 scores exceed 90% on the training set and 80% on the testing set. The results are implementable and realizable using off-the-shelf tools. The goal is to design with domain experts an energy monitoring system that can be configured once and then widely deployed with little additional cost or effort.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Load response of a floating wind turbine to turbulent atmospheric flow

The two turbulence-generation models [Kaimal Spectrum Exponential Coherence (KSEC) and Mann] specified in the international standard for wind turbine design assume neutral atmospheric conditions and are based on statistical and spectral methods. Mainly due to the lack of physics, the flow fields simulated with these models ultimately differ in their underlying structure, especially in terms of the spatial coherence of longitudinal velocity perturbations. While this may not be critical for smaller wind turbine rotors, it becomes important when rotor sizes increase. Furthermore, it might be especially important in the context of floating technologies as they are more sensitive to large turbulent coherent structures. Previous work found that these differences between KSEC and Mann can propagate to loads predictions and thereby affect the design space of the entire wind turbine system. It is therefore crucial to determine in which ways these two models are underperforming. Up until now, validation of these models had only been done in the vertical direction because it is extremely difficult to obtain atmospheric turbulence measurements separated laterally, and sampled at heights relevant to wind energy. In this work, we address the lack of measurements by using high-fidelity, high-resolution simulation data as a reference. We perform hour-long, large-eddy simulations of turbulent velocity fields that are stability-dependent and contain three-dimensional coherent structures. These flow fields are then used to investigate which stochastic model is a better predictor of loads on a realistic spar-system floating offshore wind turbine, and to quantify how the assumption of neutral stratification propagates to short-term load estimates. Both stochastic turbulence models are found to overpredict fatigue loading in high-wind scenarios (in some cases, by more than 25%) and underpredict it when the wind speed is low (by as much as 20%). The KSEC model matches the high-fidelity flow fields more closely than Mann at high wind speeds, and the opposite is true at low wind speeds. Finally, turbine loading is found to be sensitive to atmospheric stability even when the turbulence intensity remains fairly constant. This sensitivity is most pronounced at low wind speeds, when fatigue load estimates on the spar system can differ by 40%.

17 WIND ENERGY↗
DOE OSTI2014

Distribution of chemical elements in soils and stream sediments in the area of abandoned Sb–As–Tl Allchar mine, Republic of Macedonia

The aim of this study was to investigate the distribution of some toxic elements in topsoil and subsoil, focusing on the identification of natural and anthropogenic element sources in the small region of rare As–Sb–Tl mineralization outcrop and abandoned mine Allchar known for the highest natural concentration of Tl in soil worldwide. The samples of soil and sediments after total digestion were analyzed by inductively coupled plasma–mass spectrometry (ICP–MS) and inductively coupled plasma–atomic emission spectrometry (ICP–AES). Factor analysis (FA) was used to identify and characterize element associations. Six associations of elements were determined by the method of multivariate statistics: Rb–Ta–K–Nb–Ga–Sn–Ba–Bi–Li–Be–(La–Eu)–Hf–Zr–Zn–In–Pd–Ag–Pt–Mg; Tl–As–Sb–Hg; Te–S–Ag–Pt–Al–Sc–(Gd–Lu)–Y; Fe–Cu–V–Ge–Co–In; Pd–Zr–Hf–W–Be and Ni–Mn–Co–Cr–Mg. The purpose of the assessment was to determine the nature and extent of potential contamination as well as to broadly assess possible impacts to human health and the environment. The results from the analysis of the collected samples in the vicinity of the mine revealed that As and Tl elements have the highest median values. Higher median values for Sb are obviously as a result of the past mining activities and as a result of area surface phenomena in the past. - Highlights: • Soil and river sediments were analyzed from Sb–As–Tl Allchar locality. • An increased content of certain toxic elements for environment was determined. • Highest As and Tl contents are obtained in the close vicinity of Allchar mine. • River sediments portray 160 times higher content of Sb than EU values. • The results classify Allchar as probably the highest natural Tl-deposit worldwide.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Intensity-Modulated Arc Therapy for Pediatric Posterior Fossa Tumors

Purpose: To compare intensity-modulated arc therapy (IMAT) to noncoplanar intensity-modulated radiation therapy (IMRT) in the treatment of pediatric posterior fossa tumors. Methods and Materials: Nine pediatric patients with posterior fossa tumors, mean age 9 years (range, 6-15 years), treated using IMRT were chosen for this comparative planning study because of their tumor location. Each patient's treatment was replanned to receive 54 Gy to the planning target volume (PTV) using five different methods: eight-field noncoplanar IMRT, single coplanar IMAT, double coplanar IMAT, single noncoplanar IMAT, and double noncoplanar IMAT. For each method, the dose to 95% of the PTV was held constant, and the doses to surrounding critical structures were minimized. The different plans were compared based on conformity, total linear accelerator dose monitor units, and dose to surrounding normal tissues, including the entire body, whole brain, temporal lobes, brainstem, and cochleae. Results: The doses to the target and critical structures for the various IMAT methods were not statistically different in comparison with the noncoplanar IMRT plan, with the following exceptions: the cochlear doses were higher and whole brain dose was lower for coplanar IMAT plans; the cochleae and temporal lobe doses were lower and conformity increased for noncoplanar IMAT plans. The advantage of the noncoplanar IMAT plan was enhanced by doubling the treatment arc. Conclusion: Noncoplanar IMAT results in superior treatment plans when compared to noncoplanar IMRT for the treatment of posterior fossa tumors. IMAT should be considered alongside IMRT when treatment of this site is indicated.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Flexible, cluster-based analysis of the electronic medical record of sepsis with composite mixture models

The widespread adoption of electronic medical records (EMRs) in healthcare has provided vast new amounts of data for statistical machine learning researchers in their efforts to model and predict patient health status, potentially enabling novel advances in treatment. In the case of sepsis, a debilitating, dysregulated host response to infection, extracting subtle, uncataloged clinical phenotypes from the EMR with statistical machine learning methods has the potential to impact patient diagnosis and treatment early in the course of their hospitalization. However, there are significant barriers that must be overcome to extract these insights from EMR data. First, EMR datasets consist of both static and dynamic observations of discrete and continuous-valued variables, many of which may be missing, precluding the application of standard multivariate analysis techniques. Second, clinical populations observed via EMRs and relevant to the study and management of conditions like sepsis are often heterogeneous; properly accounting for this heterogeneity is critical. Here, we describe an unsupervised, probabilistic framework called a composite mixture model that can simultaneously accommodate the wide variety of observations frequently observed in EMR datasets, characterize heterogeneous clinical populations, and handle missing observations. In conclusion, we demonstrate the efficacy of our approach on a large-scale sepsis cohort, developing novel techniques built on our model-based clusters to track patient mortality risk over time and identify physiological trends and distinct subgroups of the dataset associated with elevated risk of mortality during hospitalization.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Comparison of experimental methods for estimating matrix diffusion coefficients for contaminant transport modeling

Here, diffusion cell and diffusion wafer experiments were conducted to compare methods for estimating effective matrix diffusion coefficients in rock core samples from Pahute Mesa at the Nevada Nuclear Security Site (NNSS). A diffusion wafer method, in which a solute diffuses out of a rock matrix that is pre-saturated with water containing the solute, is presented as a simpler alternative to the traditional through-diffusion (diffusion cell) method. Both methods yielded estimates of effective matrix diffusion coefficients that were within the range of values previously reported for NNSS volcanic rocks. The difference between the estimates of the two methods ranged from 14 to 30%, and there was no systematic high or low bias of one method relative to the other. From a transport modeling perspective, these differences are relatively minor when one considers that other variables (e.g., fracture apertures, fracture spacings) influence matrix diffusion to a greater degree and tend to have greater uncertainty than effective matrix diffusion coefficients. For the same relative random errors in concentration measurements, the diffusion cell method yields effective matrix diffusion coefficient estimates that have less uncertainty than the wafer method. However, the wafer method is easier and less costly to implement and yields estimates more quickly, thus allowing a greater number of samples to be analyzed for the same cost and time. Given the relatively good agreement between the methods, and the lack of any apparent bias between the methods, the diffusion wafer method appears to offer advantages over the diffusion cell method if better statistical representation of a given set of rock samples is desired.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Hybrid framework for the simulation of stochastic chemical kinetics

Stochasticity plays a fundamental role in various biochemical processes, such as cell regulatory networks and enzyme cascades. Isothermal, well-mixed systems can be modelled as Markov processes, typically simulated using the Gillespie Stochastic Simulation Algorithm (SSA) [25]. While easy to implement and exact, the computational cost of using the Gillespie SSA to simulate such systems can become prohibitive as the frequency of reaction events increases. This has motivated numerous coarse-grained schemes, where the “fast” reactions are approximated either using Langevin dynamics or deterministically. While such approaches provide a good approximation when all reactants are abundant, the approximation breaks down when one or more species exist only in small concentrations and the fluctuations arising from the discrete nature of the reactions become significant. This is particularly problematic when using such methods to compute statistics of extinction times for chemical species, as well as simulating non-equilibrium systems such as cell-cycle models in which a single species can cycle between abundance and scarcity. In this paper, a hybrid jump-diffusion model for simulating well-mixed stochastic kinetics is derived. It acts as a bridge between the Gillespie SSA and the chemical Langevin equation. For low reactant reactions the underlying behaviour is purely discrete, while purely diffusive when the concentrations of all species are large, with the two different behaviours coexisting in the intermediate region. A bound on the weak error in the classical large volume scaling limit is obtained, and three different numerical discretisations of the jump-diffusion model are described. The benefits of such a formalism are illustrated using computational examples.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

The effect of grain boundary character evolution on the intergranular corrosion behavior of advanced Al-Mg-3wt%Zn alloy with Mg variation

Highlights: • The relationship among GBCD, GBPs, IGC behavior and Mg content in the age hardenable Al-Mg-3.0wt%Zn alloy is established. • None of grain boundaries are found to be immune to the GBPs. • GBPs along low angle grain boundaries are more discontinuous than high angle grain boundaries. • The IGC mechanism of Al-Mg-3.0wt%Zn alloy is clarified. • A new method for the statistical analysis of the continuity of GBPs is used in this paper. The characteristics of grain boundary and grain boundary precipitates of Al-Mg-3wt%Zn alloy with different Mg contents were systematically investigated. Observation by optical microscopy, transmission electron microscopy, scanning transmission electron microscopy and energy-dispersive X-ray spectroscopy show that the morphology and composition of the grain boundary precipitates change little with the variation in the Mg content. Based on the statistical analysis of the spacings between two adjacent grain boundary precipitates, we find that the decrease in the Mg content reduces the continuity of the grain boundary precipitates. Comparing this result with the electron backscattered diffraction analysis, it can be concluded that the continuity of grain boundary precipitates is affected by the proportion of low angle grain boundaries. The observations of grain boundary precipitates at both low angle grain boundaries and high angle grain boundaries using transmission electron microscopy indicate that low angle grain boundaries have more discontinuous grain boundary precipitates. Combining these results with the results of the intergranular corrosion test, the microscopic features of this corrosion behavior and corrosion mechanism are revealed.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Rapid neutron and gamma-ray source localization using machine learning

Rapid localization of radiation sources is critical for applications including nuclear emergency response, safeguards, and security. However, conventional imaging systems such as neutron scatter cameras and Compton cameras depend on rare coincidence events, which often result in long acquisition times. In this work, we address the challenge of rapid source localization by developing a machine learning approach to predict the direction of a single radiation source using only count rates from an array of neutron and gamma-ray detectors. The proposed model is a fully connected neural network (FCNN) trained using Monte Carlo simulation data from a 252 Cf source. The model hyperparameters are optimized with a small set of routine 252 Cf measurements. We benchmarked the performance of the trained and optimized machine learning model using additional 252 Cf , 137 Cs , and PuBe measurements under laboratory conditions with varying source-detector configurations. For these measurements, the machine learning model achieved a mean localization error smaller than 30° with 3 x 10 3 system counts, corresponding to 8 s measurement time for the imaging system used in this work. In this low-statistics regime, the method outperformed traditional scatter-based imaging by more than 75% in localization accuracy for the evaluated measurement configurations. These results demonstrate that a machine learning-based approach can significantly reduce the time required for accurate single-source localization, providing a robust and computationally efficient alternative to traditional imaging systems in time-critical nuclear security and emergency response scenarios.

Gamma-ray imaging↗
DOE OSTI2022

Quantification and evaluation of plastic waste in the United States

To develop viable solutions for reducing plastic waste, spatially explicit data on the management of these materials are critical. Here we employ statistical and geospatial methods to present a comprehensive assessment of plastic waste in the United States by resin type at the state, county, and local levels. Of the estimated 44 Mt of plastic waste managed in 2019 domestically, approximately 86% was landfilled, 9% was combusted, and 5% was recycled. Landfilled plastics represented significant losses to the country's economy in 2019: an average of US$7.2 billion in market value, about 3.4 EJ as embodied energy (equivalent to 12% of energy consumption by the industrial sector), and 1.5 EJ as an energy source (equivalent to 5.5% and 5% of energy consumption by the industrial and transportation sectors, respectively). Lastly, we posit that substantial amount of landfilled plastic waste could be recovered through advanced sorting, existing, and emerging recycling processes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Molecular dynamics simulations of hydrogen diffusion in aluminum

In this study, hydrogen diffusion impacts the performance of solid-state hydrogen storage materials and contributes to the embrittlement of structural materials under hydrogen-containing environments. In atomistic simulations, the diffusion energy barriers are usually calculated using molecular statics simulations where a nudged elastic band method is used to constrain a path connecting the two end points of an atomic jump. This approach requires prior knowledge of the “end points”. For alloy and defective systems, the number of possible atomic jumps with respect to local atomic configurations is tremendous. Even when these jumps can be exhaustively studied, it is still unclear how they can be combined to give an overall diffusion behavior seen in experiments. Here we describe the use of molecular dynamics simulations to determine the overall diffusion energy barrier from the Arrhenius equation. This method does not require information about atomic jumps, and it has additional advantages, such as the ability to incorporate finite temperature effects and to determine the pre-exponential factor. As a test case for a generic method, we focus on hydrogen diffusion in bulk aluminum. We find that the challenge of this method is the statistical variation of the results. However, highly converged energy barriers can be achieved by an appropriate set of temperatures, output time intervals (for tracking hydrogen positions), and a long total simulation time. Our results help elucidate the inconsistencies of the experimental diffusion data published in the literature. The robust approach developed here may also open up future molecular dynamics simulations to rapidly study diffusion properties of complex material systems in multidimensional spaces involving composition and defects.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Parsimonious Potential Energy Surface Expansions Using Dictionary Learning with Multipass Greedy Selection

Potential energy surfaces fit with basis set expansions have been shown to provide accurate representations of electronic energies and have enabled a variety of high-accuracy dynamics, kinetics, and spectroscopy applications. The number of terms in these expansions scales poorly with system size, a drawback that challenges their use for systems with more than similar to 10 atoms. A solution is presented here using dictionary learning. Subsets of the full set of conventional basis functions are optimized using a newly developed multipass greedy regression method inspired by forward and backward selection methods from the statistics, signal processing, and machine learning literatures. Here, the optimized representations have accuracies comparable to the full set but are 1 or more orders of magnitude smaller, and notably, the number of terms in the optimized multipass greedy expansions scales approximately linearly with the number of atoms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Interference of interchromophoric energy-transfer pathways in π-conjugated macrocycles

The interchromophoric energy-transfer pathways between weakly coupled units in a π-conjugated phenylene–ethynylene macrocycle and its half-ring analogue have been investigated using the nonadiabatic excited-state molecular dynamics approach. To track the flow of electronic transition density between macrocycle units, we formulate a transition density flux analysis adapted from the statistical minimum flow method previously developed to investigate vibrational energy flow. Following photoexcitation, transition density is primarily delocalized on two chromophore units and the system undergoes ultrafast energy transfer, creating a localized excited state on a single unit. In the macrocycle, distinct chromophore units donate transition density to a single acceptor unit but do not interchange transition density among each other. We find that energy transfer in the macrocycle is slower than in the corresponding half ring because of the presence of multiple interfering energy-transfer pathways. Finally, simulation results are validated by modeling the fluorescence anisotropy decay.

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

Improving Precipitation Estimation Using Convolutional Neural Network

Precipitation process is generally considered to be poorly represented in numerical weather/climate models. Statistical downscaling (SD) methods, which relate precipitation with model resolved dynamics, often provide more accurate precipitation estimates compared to model's raw precipitation products. Herein we introduce the convolutional neural network model to foster this aspect of SD for daily precipitation prediction. Specifically, we restrict the predictors to the variables that are directly resolved by discretizing the atmospheric dynamics equations. In this sense, our model works as an alternative to the existing precipitation-related parameterization schemes for numerical precipitation estimation. We train the model to learn precipitation-related dynamical features from the surrounding dynamical fields by optimizing a hierarchical set of spatial convolution kernels. We test the model at 14 geogrid points across the contiguous United States. Results show that provided with enough data, precipitation estimates from the convolutional neural network model outperform the reanalysis precipitation products, as well as SD products using linear regression, nearest neighbor, random forest, or fully connected deep neural network. Evaluation for the test set suggests that the improvements can be seamlessly transferred to numerical weather modeling for improving precipitation prediction. Based on the default network, we examine the impact of the network architectures on model performance. Also, we offer simple visualization and analyzing approaches to interpret the models and their results. Our study contributes to the following two aspects: First, we offer a novel approach to enhance numerical precipitation estimation; second, the proposed model provides important implications for improving precipitation-related parameterization schemes using a data-driven approach.

54 ENVIRONMENTAL SCIENCES↗