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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Growth Mechanism of Nanowires: Binary and Ternary Chalcogenides

Semiconductor nanowires exhibit very exciting optical and electrical properties including high transparency and a several order of magnitude better photocurrent than thin film and bulk materials. We present here the mechanism of nanowire growth from the melt-liquid-vapor medium. We describe preliminary results of binary and ternary selenide materials in light of recent theories. Experiments were performed with lead selenide and thallium arsenic selenide systems which are multifunctional material and have been used for detectors, acousto-optical, nonlinear and radiation detection applications. We observed that small units of nanocubes and elongated nanoparticles arrange and rearrange at moderate melt undercooling to form the building block of a nanowire. Since we avoided the catalyst, we observed self-nucleation and uncontrolled growth of wires from different places. Growth of lead selenide nanowires was performed by physical vapor transport method and thallium arsenic selenide nanowire by vapor-liquid-solid (VLS) method. In some cases very long wires (>mm) are formed. To achieve this goal experiments were performed to create situation where nanowires grew on the surface of solid thallium arsenic selenide itself.

Singh, N. B.↗
NASA NTRS2021

Secondary Science Teachers’ Implementation of a Curricular Intervention When Teaching With Global Climate Models

In the past decade, emphasis on promoting “climate literacy” in K-16 science classrooms has increased. Teachers play a critical role in cultivating these opportunities, especially in secondary science classrooms. However, most prior climate education research has focused on students and student learning; little is known about how teachers implement climate-focused curricular interventions. Here, we report findings from a concurrent mixed methods, multiple-case study of four secondary science teachers’ implementation of a new, NGSS-aligned, model-centric climate curriculum module grounded in the use of a data-driven, computer-based climate modeling tool—Easy Global Climate Model (EzGCM). We employ multiple data sources, including video-recorded classroom observations, interviews, and instructional artifacts, and both qualitative and quantitative analyses, to investigate how teachers implemented the curriculum. Findings show that, overall, teachers implemented the curriculum in ways that were less model-centric than designed, placing greater emphasis on EzGCM itself rather than using the model to investigate Earth’s changing climate. Additionally, we present detailed single-case studies of each participant teacher that highlight differences in teachers’ implementation of the curriculum module and their reasoning for making observed instructional decisions. This research sheds light on the design of secondary science learning environments by illustrating the varied ways teachers implement a climate-focused curriculum to support students’ developing climate literacy. This has important implications for the design of climate-focused curriculum and supports for teachers.

Secondary science teaching↗
NASA NTRSDate not supplied

Assessment of Crew Time for Maintenance and Repair Activities for Lunar Surface Missions

NASA is currently evaluating different methods to predict how much time crewmembers will spend conducting repair and maintenance activities on future space missions. As mission scope and spacecraft architectures change, understanding how crew repair and maintenance timelines are impacted by mission operations and technology changes is vital for future mission planning. Past work has been done using historical International Space Station (ISS) data to accurately predict crew habitation and operation timelines, resulting in the development of NASA’s Exploration Crew Time Model (ECTM). However, understanding crew maintenance and repair requirements has posed a unique challenge due to the complexity of available datasets, the probabilistic nature of sub-system failures, and the impacts of reliability growth on failure rates. This paper presents a methodology to collect and condition empirical repair and maintenance time data from available datasets, to extrapolate from that data to estimate projected maintenance and repair times for a lunar Surface Habitat (SH), and to assess how uncertainty in repair time could impact utilization time on the lunar surface. NASA ISS maintenance and crew time data are logged into two central databases: the Maintenance Data Collection (MDC) and the Operations Planning Timeline Integration System (OPTimIS). Separately, each of these two datasets capture only portions of the complete set of data required to generate an accurate assessment of crew time spent on maintenance activities at a sub-system level. To create a more useful crew time estimate for maintenance timelines, the authors developed a methodology to capture relevant data from each set and combine and utilize that data by linking crew time requirements to specific components. The authors compare the failure logs in the MDC to crew activity logs pulled from OPTimIS and then process the data to estimate required repair time for each failure and repair event. The entire maintenance activity dataset is then categorized based on the class of failed component to ensure a significant sample size for each class and accurate crew time estimates for any components lacking relevant data. This resultant component repair time data can be used in the future to generate Mean Time to Repair (MTTR) estimates and confidence intervals for each class of component based on a probabilistic distribution of documented maintenance events. These improved MTTR values can then be applied to candidate element sub-system architectures, along with component Mean Time Between Failure (MTBF) data to generate distributions for potential required system crew repair time estimates for a given mission. The authors applied these modeling methods to a case study of a crewed mission to the planned SH and produced expected corrective maintenance crew time distributions. The results produced an expected corrective maintenance crew time at over 24 hours per mission, and a maintenance crew time distribution that reflects the importance of planning for sufficient maintenance requirements each mission. Repair time distributions can then be used to develop more accurate crew schedules and to assess potential available utilization time.

Crew Time↗
NASA NTRSDate not supplied

Advancements in Blowing Dust Detection at Night via Machine Learning

This presentation introduces operational users to a machine-learning based Dust Probability product developed by the NASA SPoRT program for the application of detecting and monitoring blowing dust plumes at night. Advances in earth observing satellites has improved monitoring and detection of dust both day and night through derived imagery such as the Dust RGB. However, limitations of the RGB at night result in less contrast between dust and land surface features, as seen by the user. A Machine Learning (ML) model has been developed and applied to GOES-16 ABI to overcome this limitation and improve nighttime dust detection. The ML capability is a subset of Artificial Intelligence methods. In this case the Dust ML model was developed using a simple Random Forest (RF) model, typically used to solve classification challenges (or to provide regression type output). The goal was to leverage the strengths of the RF model to learn how to identify blowing dust, and hence, overcome the limitation of a user trying to detect blowing dust within the satellite imagery by eye alone. A brief description of the ML model development will be provided. However, the focus of the presentation will be on the initial user feedback from the assessment of this tool for the 2022 blowing dust events of March through April. During this time several users across the U.S. Southwest collaborated to apply this Dust ML product at night as a complement to the existing Dust RGB in order to determine if it provided greater operational efficiency and value.

Machine Learning↗
DOE OSTI2012

Kirchhoff's Integral Representation and a Cavity Wake Potential

A method is proposed for the calculation of the short-range wake field potentials of an ultra-relativistic bunch passing near some irregularities in a beam pipe. The method is based on the space-time domain integration of Maxwell's equations using Kirchhoff's formulation. We demonstrate this method on two cases where we obtain the wake potentials for the energy loss of a bunch traversing an iris-collimator in a beam pipe and for a cavity. Likewise, formulas are derived for Green's functions that describe the transverse force action of wake fields. Simple formulas for the total energy loss of a bunch with a Gaussian charge density distribution are derived as well. The derived estimates are compared with computer results and predictions of other models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Covariance Structure of Pu Isotopic Compositions Obtained Using FRAM 5.2

In the nondestructive assay of special nuclear materials, isotopic determination based on the analysis of high-purity Ge high-resolution gamma-ray spectra plays a pivotal role. The isotopic results, which are based on a relative measurement of peak intensities, are used to interpret data acquired using quantitative (mass) nondestructive assay methods. In the case of plutonium measurements, this includes both passive and active neutron techniques, and also calorimetry (heat). Historically, precision estimates on the Pu- isotopic composition have been generated, on a case-by-case basis, by least squares fitting algorithms embedded in the analysis codes, but all other sources of uncertainty have generally been estimated using a top-down approach by item category. A largely neglected consideration is the covariance between the reported abundances and derived quantities – notably Pu-239-effective mass, Pu-240-effective mass and specific thermal power. In this work we study the covariance structure by statistically evaluating repeat spectrum measurements of two known low burn-up heavily filtered Pu items reduced using the spectrum analysis code FRAM 5.2 and alternate standard parameter sets. For routine measurements, however, only a single spectrum from an item will typically be acquired. We therefore discuss how to generate the sought after covariance information from an individual spectrum by spectrum re-sampling, an approach which could be added to provide item specific reports, and compare the two approaches to test its reliability.

Croft, Stephen↗
DOE OSTI2018

Muon-Computed Tomography using POCA Trajectory for Imaging Spent Nuclear Fuel in Dry Storage Casks

Cosmic ray muon-computed tomography (μCT) is a new imaging modality with unique characteristics that can be particularly important for applications in nuclear proliferation detection and international treaty verification. Using cosmic ray muons for nuclear security presents several potential advantages. Among others, muons are generated naturally in the atmosphere, can penetrate high-density materials, and are freely available. No radiological sources are required and consequently there is no associated radiological dose. Recently, the feasibility of using muons for imaging spent nuclear fuel stored in shielded casks has been explored and has been proved beneficial. However, challenges in μCT imaging include low muon flux of ~10,000 muons/m2/min, the effects of multiple Coulomb scattering (MCS) blurring the image, and inefficiency in being able to use all recorded muons for imaging. In this paper, we argue that the use of muon tracing should produce tomographic muon images with improved quality – or more quickly for the same image quality – compared to the case where conventional methods are used. In our paper, we report on the development and assessment of a novel muon tracing method for μCT. The proposed method back projects the muon’s scattering angle into each pixel crossed by its PoCA trajectory then forward projects the variance of the scattering angle in each pixel to detector bins along the muon’s incident horizontal direction. Two scenarios were simulated to assess the expected detection capability of this proposed method. GEANT4 was used to model the main characteristics of 1-60 GeV muons through matter. The simulated images showed an expected improvement in resolution and a reduced the reliance on the muon momentum information compared to a more conventional muon tomography method.

Liu, Zhengzhi↗
DOE OSTI2019

WBS 4.2.1.31 Integrated Life Cycle Sustainability Analysis

The main objective of this project (following the 2017 BETO Program Peer-Review) is to provide BETO with a coherent methodology and modeling framework based on life cycle (LCA) and economic input-output analysis, tested and vetted with stakeholders, to better understand the effects of an expanding U.S. bioeconomy at specific levels including economic dimensions such as jobs, gross domestic product (value added), and environmental criteria such as air pollutant emissions, water and land use, ecotoxicity, and others. This supports a 2019 milestone in the BETO MYP and simultaneously addresses a gap previously identified in the BETO program and highlighted, e.g., by BETO Program Peer Reviewers in 2015 and 2017. To fill in this gap, NREL started collaborating with developers of the national level U.S. Environmentally-Extended Input-Output (USEEIO) model in FY18. This peer-reviewed, open source model is based on publicly available data, and developed and maintained by U.S. EPA where it has been the basis for EPA's Sustainable Material Management analyses and program. Also, USEEIO was initially designed for use with EPA's Tool for the Reduction and Assessment of Chemicals and Other Environmental Impacts (TRACI). It aligns well with similar efforts including the Federal LCA Commons and the Sustainable Acquisition Program at the Department of Defense (DoD). In FY18, NREL convened stakeholders from federal agencies, universities, and private sector entities to identify and form a common understanding of (1) models describing the U.S. economy and how to build off of them, (2) a bioeconomy definition for the next modeling steps, (3) the (sub)sectors that can/should be used in the framework, and (4) opportunities for collaboration and data sharing. NREL then created a first order model by disaggregating, i.e., separating the economic activities associated with and attributed to a selected product of the bioeconomy: corn ethanol. Corn ethanol is well studied and has a significant amount of public, validated economic industry information. This is important as disaggregation is a critical step in creating a representative and accurate framework. Corn ethanol served as a validation test case for the method and framework. By the end of FY18, the team was able to generate several scenarios for corn ethanol on national and selected regional levels providing results on the absolute and relative (Megajoule) effects in several environmental and economic impact categories. A second objective of the project is to inform BETO's strategic decision making, specifically its sustainability research portfolio, by engaging in, evaluating, and synthesizing selected global multi-year activities from the perspectives of a variety of complex multilateral and multi-stakeholder processes that pertain to bioenergy. This helps to (1) identify gaps, barriers, and critical areas for future BETO bioenergy and sustainability assessments; (2) identify opportunities and challenges to a sustainable U.S. bioeconomy; and (3) contribute to and disseminate BETO information to and from major global bioenergy and sustainability efforts. This project's work with multilateral organizations ensures that U.S. science-based viewpoints are considered and helps to balance the influence of the large number of countries with the same centralized philosophy. This engagement builds a more solid foundation for sustainable biomass and bioenergy systems in the U.S. and in the world. Working with multi-lateral, international platforms including IEA Bioenergy, Global Bio-Energy Partnership (GBEP), and their partner organizations also allows engagement with important stakeholders including policy makers, industry, NGOs, and academia.

09 BIOMASS FUELS↗
DOE OSTI2021

A Two-Step Restoration Scheme with DER Controllability for Resilient Distribution Systems

Modern distribution systems have increasing integration of distributed energy resources (DERs). A large number of distribution system DERs are interfaced through a power electronic converter. During grid contingency scenarios these resources have been traditionally disconnected, without any fault ride-through capabilities. However, with new regulations and better technology, it is feasible for these resources to contribute to the grid's restoration after an adverse event and hence enhancing resilience. In distribution systems, system restoration is an important problem, especially in face of high-impact low-frequency events. This paper proposes a two-step restoration scheme for the power system restoration process by leveraging additional degrees of freedom in DERs for mitigating voltage problems. Simulation studies are performed on the IEEE-123 node feeder system to demonstrate the effectiveness of the proposed method for various cases.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Low level coupling scheme between neutronics and thermal-hydraulics based on Anderson acceleration

The simulation of nuclear reactors is a multiphysics problem mixing, amongst other fields, neutron transport and thermal-hydraulics. The simplest and most used approach in multiphysics simulation is based on the coupling of single-physics codes in a black-box fashion. However, in order to reduce the computational time needed for such simulations, case-dependent optimizations are often required. In this paper, we aim at reducing the computational time required to solve a coupled neutronic/thermal-hydraulic steady-state problem on a simplified Pressurized Water Reactor (PWR) core. The idea is to deal simultaneously with the coupling of the energy groups of the deterministic neutronic description of the core and its thermal-hydraulic description with the Anderson acceleration. By doing so, the fission source terms are directly accelerated instead of the power map as done in most cases. The power method used to solve the k-eigenvalue problem inside the neutronic solver is thus accelerated with the Anderson acceleration. The numerical experimentations conducted in this work are performed using APOLLO3 and THEDI, and indicate that such coupling strategy improves the convergence rates in terms of number of iterations required and the total computational time. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1990

Synthesis of individual rotor blade control system for gust alleviation

The utilization of rotor flapping in synthesizing an Individual Blade Control (IBC) system for gust alleviation is demonstrated. The objective is to illustrate and seek to improve Ham's IBC method. A sensor arrangement with two accelerometers mounted on the root and tip of a blade is proposed for estimating of flapping states for feedback control. Equivalent swash plate implementation of IBC is also deliberated. The study concludes by addressing the concept of general rotor states feedback, of which the IBC method is a special case. The blade flapping equation of motion is derived. Ham's original IBC method and a modified IBC scheme called Model Reference (MRIBC) are examined, followed by simulation study with ideal measurements and relative performances of the two methods. The practical aspects of IBC implementation are presented. Different configuration of sensors and their merits are considered. The realization of IBC using equivalent swash plate instead of direct actuator motion is discussed. It is shown that IBC is a particular case of rotor states feedback. The idea of general rotor states feedback is further elaborated. Finally, major conclusions are given.

Wang, Ji C.↗
NASA NTRS1993

Establishment of computerized numerical databases on thermophysical and other properties of molten as well as solid materials and data evaluation and validation for generating recommended reliable reference data

The Center for Information and Numerical Data Analysis and Synthesis, (CINDAS), measures and maintains databases on thermophysical, thermoradiative, mechanical, optical, electronic, ablation, and physical properties of materials. Emphasis is on aerospace structural materials especially composites and on infrared detector/sensor materials. Within CINDAS, the Department of Defense sponsors at Purdue several centers: the High Temperature Material Information Analysis Center (HTMIAC), the Ceramics Information Analysis Center (CIAC) and the Metals Information Analysis Center (MIAC). The responsibilities of CINDAS are extremely broad encompassing basic and applied research, measurement of the properties of thin wires and thin foils as well as bulk materials, acquisition and search of world-wide literature, critical evaluation of data, generation of estimated values to fill data voids, investigation of constitutive, structural, processing, environmental, and rapid heating and loading effects, and dissemination of data. Liquids, gases, molten materials and solids are all considered. The responsibility of maintaining widely used databases includes data evaluation, analysis, correlation, and synthesis. Material property data recorded on the literature are often conflicting, diverging, and subject to large uncertainties. It is admittedly difficult to accurately measure materials properties. Systematic and random errors both enter. Some errors result from lack of characterization of the material itself (impurity effects). In some cases assumed boundary conditions corresponding to a theoretical model are not obtained in the experiments. Stray heat flows and losses must be accounted for. Some experimental methods are inappropriate and in other cases appropriate methods are carried out with poor technique. Conflicts in data may be resolved by curve fitting of the data to theoretical or empirical models or correlation in terms of various affecting parameters. Reasons (e.g. phase transitions) must be found for unusual dependence or any anomaly. Such critical evaluation involves knowledge of theory, experience in measurement, familiarity with metallurgy (microstructural behavior) and not inconsiderable judgment. An examination of typical data compiled and analyzed by CINDAS shows that the thermal conductivity of a material reported in the literature may vary by a factor of two of more; the range of reported values increases as temperature increases reflecting the difficulty of high temperature measurements. Often only estimates of melt behavior are available, despite the importance of melt properties in modeling, welding, or other solidification processes. There may be only a few measurements available for properties such as kinematic viscosity, even for widely used materials such as stainless steel. In the face of such a paucity of existing data and in a national environment where too few new data are being generated it is nonetheless the responsibility of CINDAS to select and disseminate recommended values of a wide variety of thermophysical properties.

Ho, C. Y.↗
DOE OSTI2015

Linking local impacts to changes in climate: a guide to attribution

© 2015, Springer-Verlag Berlin Heidelberg. Assessing past impacts of observed climate change on natural, human and managed systems requires detailed knowledge about the effects of both climatic and other drivers of change, and their respective interaction. Resulting requirements with regard to system understanding and long-term observational data can be prohibitive for quantitative detection and attribution methods, especially in the case of human systems and in regions with poor monitoring records. To enable a structured examination of past impacts in such cases, we follow the logic of quantitative attribution assessments, however, allowing for qualitative methods and different types of evidence. We demonstrate how multiple lines of evidence can be integrated in support of attribution exercises for human and managed systems. Results show that careful analysis can allow for attribution statements without explicit end-to-end modeling of the whole climate-impact system. However, care must be taken not to overstate or generalize the results and to avoid bias when the analysis is motivated by and limited to observations considered consistent with climate change impacts.

Hansen, Gerrit↗
DOE OSTI2023

On the Approximability of Random-Hypergraph MAX-3-XORSAT Problems with Quantum Algorithms

Constraint satisfaction problems are an important area of computer science. Many of these problems are in the complexity class NP which is exponentially hard for all known methods, both for worst cases and often typical. Fundamentally, the lack of any guided local minimum escape method ensures the hardness of both exact and approximate optimization classically, but the intuitive mechanism for approximation hardness in quantum algorithms based on Hamiltonian time evolution is poorly understood. We explore this question using the prototypically hard MAX-3-XORSAT problem class. We conclude that the mechanisms for quantum exact and approximation hardness are fundamentally distinct. We qualitatively identify why traditional methods such as quantum adiabatic optimization are not good approximation algorithms. We propose a new spectral folding optimization method that does not suffer from these issues and study it analytically and numerically. We consider random rank-3 hypergraphs including extremal planted solution instances, where the ground state satisfies an anomalously high fraction of constraints compared to truly random problems. We show that, if we define the energy to be $E = N_{unsat}-N_{sat}$, then spectrally folded quantum optimization will return states with energy $E \leq A E_{GS}$ (where $E_{GS}$ is the ground state energy) in polynomial time, where conservatively, $A \simeq 0.6$. We thoroughly benchmark variations of spectrally folded quantum optimization for random classically approximation-hard (planted solution) instances in simulation, and find performance consistent with this prediction. We do not claim that this approximation guarantee holds for all possible hypergraphs, though our algorithm's mechanism can likely generalize widely. These results suggest that quantum computers are more powerful for approximate optimization than had been previously assumed.

Kapit, Eliot↗
DOE OSTI2016

Comparison of fluid-fluid interfacial areas measured with X-ray microtomography and interfacial partitioning tracer tests for the same samples

Two different methods are currently used for measuring interfacial areas between immiscible fluids within 3-D porous media, high-resolution microtomographic imaging and interfacial partitioning tracer tests (IPTT). Both methods were used in this study to measure nonwetting/wetting interfacial areas for a natural sand. The microtomographic imaging was conducted on the same packed columns that were used for the IPTTs. This is in contrast to prior studies comparing the two methods, for which in all cases different samples were used for the two methods. In addition, the columns were imaged before and after the IPTTs to evaluate the potential impacts of the tracer solution on fluid configuration and attendant interfacial area. The interfacial areas measured using IPTT are ~5 times larger than the microtomographic-measured values, which is consistent with previous work. Analysis of the image data revealed no significant impact of the tracer solution on NAPL configuration or interfacial area. Furthermore, other potential sources of error were evaluated, and all were demonstrated to be insignificant. The disparity in measured interfacial areas between the two methods is attributed to the limitation of the microtomography method to characterize interfacial area associated with microscopic surface roughness due to resolution constraints.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

A Deep Learning Filter for the Intraseasonal Variability of the Tropics

Abstract This paper presents a novel application of convolutional neural network (CNN) models for filtering the intraseasonal variability of the tropical atmosphere. In this deep learning filter, two convolutional layers are applied sequentially in a supervised machine learning framework to extract the intraseasonal signal from the total daily anomalies. The CNN-based filter can be tailored for each field similarly to fast Fourier transform filtering methods. When applied to two different fields (zonal wind stress and outgoing longwave radiation), the index of agreement between the filtered signal obtained using the CNN-based filter and a conventional weight-based filter is between 95% and 99%. The advantage of the CNN-based filter over the conventional filters is its applicability to time series with the length comparable to the period of the signal being extracted. Significance Statement This study proposes a new method for discovering hidden connections in data representative of tropical atmosphere variability. The method makes use of an artificial intelligence (AI) algorithm that combines a mathematical operation known as convolution with a mathematical model built to reflect the behavior of the human brain known as artificial neural network. Our results show that the filtered data produced by the AI-based method are consistent with the results obtained using conventional mathematical algorithms. The advantage of the AI-based method is that it can be applied to cases for which the conventional methods have limitations, such as forecast (hindcast) data or real-time monitoring of tropical variability in the 20–100-day range.

Stan, Cristiana↗
NASA NTRS1981

Solar Maximum Mission experiment - Early results from the soft X-ray polychromator experiment

It is pointed out that the X-ray polychromator experiment has been in operation on the Solar Maximum Mission satellite for more than three months. Using a number of different modes, the polychromator is observing flares and active regions in the wavelength range 1-23 A. These modes include polychromatic imaging, high resolution line profiles, high dispersion spectra, and light curves with high time-resolution. Data are described and some of the early analysis and interpretation is presented. All the interpretations are based on simple approximate methods; it is noted, however, that in most cases more elaborate and reliable methods are close to being applied.

Gabriel, A. H.↗
NASA NTRS1921

Analysis of wing truss stresses including the effect of redundancies

Airplane wing trusses are generally designed to contain redundant members (stagger wires and external drag wires) which, according to common practice, are not taken into account in calculations, so as to simplify the stress analysis by rendering the structure statically determinate. A more accurate method, in which the redundancies are included, involves a solution by means of Castigliano's method of least work. For the purpose of demonstrating the practical application of the method of least work this report presents examples for stresses of several cases of loading worked out for a structure similar to that of the Curtiss JN-4h. Case 1 was taken as the condition of velocity of 100 miles per hour combined with the angle of attack of maximum lift. Case 1a assumed the same loading but neglected the distortion of wooden members in the least-work analysis. So little error was involved in case 1a that this simplified method was employed for each succeeding case. Case 2 assumed a diving speed of 120 miles per hour and an angle of attack of no lift. Case 3 was worked out for the conditions imposed by the sand load recommended in NACA technical note no. 6.

Warner, E P↗