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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 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Trade-Space Analysis Tool for Constellations (TAT-C)

Traditionally, space missions have relied on relatively large and monolithic satellites, but in the past few years, under a changing technological and economic environment, including instrument and spacecraft miniaturization, scalable launchers, secondary launches as well as hosted payloads, there is growing interest in implementing future NASA missions as Distributed Spacecraft Missions (DSM). The objective of our project is to provide a framework that facilitates DSM Pre-Phase A investigations and optimizes DSM designs with respect to a-priori Science goals. In this first version of our Trade-space Analysis Tool for Constellations (TAT-C), we are investigating questions such as: How many spacecraft should be included in the constellation? Which design has the best costrisk value? The main goals of TAT-C are to: Handle multiple spacecraft sharing a mission objective, from SmallSats up through flagships, Explore the variables trade space for pre-defined science, cost and risk goals, and pre-defined metrics Optimize cost and performance across multiple instruments and platforms vs. one at a time.This paper describes the overall architecture of TAT-C including: a User Interface (UI) interacting with multiple users - scientists, missions designers or program managers; an Executive Driver gathering requirements from UI, then formulating Trade-space Search Requests for the Trade-space Search Iterator first with inputs from the Knowledge Base, then, in collaboration with the Orbit Coverage, Reduction Metrics, and Cost Risk modules, generating multiple potential architectures and their associated characteristics. TAT-C leverages the use of the Goddard Mission Analysis Tool (GMAT) to compute coverage and ancillary data, streamlining the computations by modeling orbits in a way that balances accuracy and performance.TAT-C current version includes uniform Walker constellations as well as Ad-Hoc constellations, and its cost model represents an aggregate model consisting of Cost Estimating Relationships (CERs) from widely accepted models. The Knowledge Base supports both analysis and exploration, and the current GUI prototype automatically generates graphics representing metrics such as average revisit time or coverage as a function of cost.

Science Data Processing↗
NASA NTRS2011

Miniaturized Airborne Imaging Central Server System

In recent years, some remote-sensing applications require advanced airborne multi-sensor systems to provide high performance reflective and emissive spectral imaging measurement rapidly over large areas. The key or unique problem of characteristics is associated with a black box back-end system that operates a suite of cutting-edge imaging sensors to collect simultaneously the high throughput reflective and emissive spectral imaging data with precision georeference. This back-end system needs to be portable, easy-to-use, and reliable with advanced onboard processing. The innovation of the black box backend is a miniaturized airborne imaging central server system (MAICSS). MAICSS integrates a complex embedded system of systems with dedicated power and signal electronic circuits inside to serve a suite of configurable cutting-edge electro- optical (EO), long-wave infrared (LWIR), and medium-wave infrared (MWIR) cameras, a hyperspectral imaging scanner, and a GPS and inertial measurement unit (IMU) for atmospheric and surface remote sensing. Its compatible sensor packages include NASA s 1,024 1,024 pixel LWIR quantum well infrared photodetector (QWIP) imager; a 60.5 megapixel BuckEye EO camera; and a fast (e.g. 200+ scanlines/s) and wide swath-width (e.g., 1,920+ pixels) CCD/InGaAs imager-based visible/near infrared reflectance (VNIR) and shortwave infrared (SWIR) imaging spectrometer. MAICSS records continuous precision georeferenced and time-tagged multisensor throughputs to mass storage devices at a high aggregate rate, typically 60 MB/s for its LWIR/EO payload. MAICSS is a complete stand-alone imaging server instrument with an easy-to-use software package for either autonomous data collection or interactive airborne operation. Advanced multisensor data acquisition and onboard processing software features have been implemented for MAICSS. With the onboard processing for real time image development, correction, histogram-equalization, compression, georeference, and data organization, fast aerial imaging applications, including the real time LWIR image mosaic for Google Earth, have been realized for NASA fs LWIR QWIP instrument. MAICSS is a significant improvement and miniaturization of current multisensor technologies. Structurally, it has a complete modular and solid-state design. Without rotating hard drives and other moving parts, it is operational at high altitudes and survivable in high-vibration environments. It is assembled from a suite of miniaturized, precision-machined, standardized, and stackable interchangeable embedded instrument modules. These stackable modules can be bolted together with the interconnection wires inside for the maximal simplicity and portability. Multiple modules are electronically interconnected as stacked. Alternatively, these dedicated modules can be flexibly distributed to fit the space constraints of a flying vehicle. As a flexibly configurable system, MAICSS can be tailored to interface a variety of multisensor packages. For example, with a 1,024x1,024 pixel LWIR and a 8,984x6,732 pixel EO payload, the complete MAICSS volume is approximately 7x9x11 in. (=18x23x28 cm), with a weight of 25 lb (=11.4 kg).

Sun, Xiuhong↗
DOE OSTI2019

Measuring Rotation and Manure Effects in an Iowa Farm Soil Health Assessment

Core Ideas Farms with known management histories contribute to soil health research. Rotation‐based systems may increase soil organic carbon when combined with appropriate tillage. Inherent soil properties must be considered in soil health assessments. Rotation effects were more noticeable than manure effects on soil health groupings. Data from on‐farm sites with known management histories are needed to quantify soil biological, chemical, and physical properties influencing carbon stocks and soil health. Surface (0–15 cm) and deep core (0–122 cm) soil samples were collected from fields under two rotations in Boone County, IA. The first was a 5‐yr corn [ Zea mays (L.)], soybean [ Glycine max (L.) Merr.], corn, oat [ Avena sativa (L.)], and alfalfa [ Medicago sativa (L.)] rotation to which 0, 18, or 36 Mg ha −1 of a manure/biosolids mixture was applied prior to planting corn. The second was an 8‐yr rotation with 6 yr of mixed grass and legume pasture followed by corn and an oat crop within which the pasture mixture was reestablished. Soil samples were collected evenly across the predominant soil map units (SMUs). Bulk density (BD), soil organic C (SOC), water‐stable aggregates (WSA), microbial biomass carbon (MBC), pH, Mehlich‐3 and diethylenetriaminepentaacetic acid (DTPA) extractable nutrients, electrical conductivity (EC), and nitrogen (total‐, NH 4 –, and NO 3 –N) were measured. Surface SOC data were consistent with Soil Survey values for the various SMUs. Crop rotation effects were more noticeable than manure/biosolid application rate effects. Data from this study were combined with previously published SOC data in Iowa. Results suggest extended rotation systems or those with cover crops may increase SOC 8 ± 4 g kg −1 compared to corn–soybean rotations (33 vs. 25 g kg −1 ). This study provides on‐farm reference values for soil health assessment tools and draws attention to the importance of inherent soil properties for these assessments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Repository scale classification and decomposition of tandem mass spectral data

Various studies have shown associations between molecular features and phenotypes of biological samples. These studies, however, focus on a single phenotype per study and are not applicable to repository scale metabolomics data. Here we report MetSummarizer, a method for predicting (i) the biological phenotypes of environmental and host-oriented samples, and (ii) the raw ingredient composition of complex mixtures. We show that the aggregation of various metabolomic datasets can improve the accuracy of predictions. Since these datasets have been collected using different standards at various laboratories, in order to get unbiased results it is crucial to detect and discard standard-specific features during the classification step. We further report high accuracy in prediction of the raw ingredient composition of complex foods from the Global Foodomics Project.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Accuracy, Bias, and Improvements in Mapping Crops and Cropland across the United States Using the USDA Cropland Data Layer

The U.S. Department of Agriculture’s (USDA) Cropland Data Layer (CDL) is a 30 m resolution crop-specific land cover map produced annually to assess crops and cropland area across the conterminous United States. Despite its prominent use and value for monitoring agricultural land use/land cover (LULC), there remains substantial uncertainty surrounding the CDLs’ performance, particularly in applications measuring LULC at national scales, within aggregated classes, or changes across years. To fill this gap, we used state- and land cover class-specific accuracy statistics from the USDA from 2008 to 2016 to comprehensively characterize the performance of the CDL across space and time. We estimated nationwide area-weighted accuracies for the CDL for specific crops as well as for the aggregated classes of cropland and non-cropland. We also derived and reported new metrics of superclass accuracy and within-domain error rates, which help to quantify and differentiate the efficacy of mapping aggregated land use classes (e.g., cropland) among constituent subclasses (i.e., specific crops). We show that aggregate classes embody drastically higher accuracies, such that the CDL correctly identifies cropland from the user’s perspective 97% of the time or greater for all years since nationwide coverage began in 2008. We also quantified the mapping biases of specific crops throughout time and used these data to generate independent bias-adjusted crop area estimates, which may complement other USDA survey- and census-based crop statistics. Our overall findings demonstrate that the CDLs provide highly accurate annual measures of crops and cropland areas, and when used appropriately, are an indispensable tool for monitoring changes to agricultural landscapes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1991

Exobiological implications of dust aggregation in planetary atmospheres: An experiment for the gas-grain simulation facility

The Gas-Grain Simulation Facility (GGSF) will provide a microgravity environment where undesirable environmental effects are reduced, and thus, experiments involving interactions between small particles and grains can be more suitably performed. Slated for flight aboard the Shuttle in 1992, the ESA glovebox will serve as a scientific and technological testbed for GGSF exobiology experiments as well as generating some basic scientific data. Initial glovebox experiments will test a method of generating a stable, mono-dispersed cloud of fine particles using a vibrating sprinkler system. In the absence of gravity and atmospheric turbulence, it will be possible to determine the influence of interparticle forces in controlling the rate and mode of aggregation. The experimental chamber can be purged of suspended matter to enable multiple repetitions of the experiments. Of particular interest will be the number of particles per unit volume of the chamber, because it is suspected that aggregation will occur extremely rapidly if the number exceeds a critical value. All aggregation events will be recorded on high-resolution video film. Changes in the experimental procedure as a result of surprise events will be accompanied by real-time interaction with the mission specialist during the Shuttle flight.

Huntington, J. L.↗
DOE OSTI2018

PySFD: comprehensive molecular insights from significant feature differences detected among many simulated ensembles

Motivation Many modeling analyses of molecular dynamics (MD) simulations are based on a definition of states that can be (groups of) clusters of simulation frames in a feature space composed of molecular coordinates. With increasing dimension of this feature space (due to the increasing size or complexity of a simulated molecule), it becomes very difficult to cluster the underlying MD data and estimate a statistically robust model. To mitigate this “curse of dimensionality”, one can reduce the feature space, e.g., with principal component or time-lagged independent component analysis transformations, focusing the analysis on the most important modes of transitions. In practice, however, all these reduction strategies may neglect important molecular details that are susceptible to experimental verification. Results To recover such molecular details, I have developed PySFD (Significant Feature Differences analyzer for Python), a multi-processing software package that efficiently selects significantly different features of any user-defined feature type among potentially many different simulated state ensembles, such as meta-stable states of a Markov State Model (MSM). Applying PySFD on MSMs of an aggregate of 300 microseconds MD simulations recently performed on the major histocompatibility complex class II (MHCII) protein, I demonstrate how this toolkit can extract and visualize valuable mechanistic information from big MD simulation data, e.g., in form of networks of dynamic interaction changes connecting functionally relevant sites of a protein complex.

Biochemistry & Molecular Biology↗
DOE OSTI2017

Automated Detection of Alkali-silica Reaction in Concrete using Linear Array Ultrasound Data

Alkali-silica reaction (ASR) is a chemical reaction in either concrete or mortar between hydroxyl ions of the alkalis (sodium and potassium) from hydraulic cement (or other sources), and certain siliceous minerals present in some aggregates. The reaction product, an alkali-silica gel, is hygroscopic having a tendency to absorb water and swell, which under certain circumstances, leads to abnormal expansion and cracking of the concrete. This phenomenon affects the durability and performance of concrete structures severely since it can cause significant loss of mechanical properties. Developing reliable methods and tools that can evaluate the degree of the ASR damage in existing structures, so that informed decisions can be made toward mitigating ASR progression and damage, is important to the long term operation of nuclear power plants especially if licenses are extended beyond 60 years. This paper examines an automated method of determining the extent of ASR damage in fabricated concrete specimens.

Santos-Villalobos, Hector J↗
DOE OSTI2025

Power System Feature-Based Event Classification by Means of Multiple PMU Data

Abstract—Phasor Measurement Units (PMUs) provide time synchronized measurements across the power grid, enabling data driven event detection and classification for enhanced system monitoring and situational awareness. However, variations in event duration, spatial extent, and severity, along with coincident events, pose challenges for conventional classification models that require fixed-size inputs. This paper presents a feature-based framework that aggregates diverse attributes from all available PMUs for each event into a fixed-length vector, facilitating the application of standard machine learning classifiers, including Random Forest, XGBoost, and Multilayer Perceptron. A probabilistic post-processing scheme is further introduced to enable multi-label classification in the presence of overlapping events. Experiments using real-world PMU data demonstrate that the Random Forest model achieves 95% accuracy, while the proposed post-processing method yields an additional 3% improvement.

Nematirad, Reza↗
DOE OSTI2019

Observational Study of Clogging Specimens from the Tundish Well Showing Origin and Growth of a Clog in an Al-Killed Ti-Alloyed Steel Cast

Post-mortem tundish skull samples of Ti-alloyed, Al-killed IF steel have been studied regarding the formation of clogging. By using modern characterization equipment and applied methods, we performed a detailed characterization of microstructures found in the tundish well. Aside from the primary metallographic examination, newly developed methods of particle size distribution analysis based on population density functions (PDF) and newly developed electron microscope EDS template mapping have been applied, allowing differentiation of complex particles according to phases and interphase contact relationships. Furthermore, detailed EDS analyses of individual inclusions with complex substructure regarding minor and trace elements within the alumina networks have been performed. These composite data on carefully selected clog microstructures allowed us to observe the presence of layering microstructures throughout the clog: “Coarse” layers (abundant microbubbles and internally complex alumina aggregates) and “Fine” layers (no microbubbles and complex alumina aggregates). Two populations of particles: Population I (2 to 5 µm) and a Population II (5 to 20 µm) build up the layers, both having lognormal PDF, in contrast to power-law PDF of secondary metallurgy inclusions. Coarse layers are further distinguished by selective concentrations of spinel with lognormal PDF as well as the occurrence of complex alumina particles with metal sub-inclusions often with elevated Cr, V, and Si content. Based on these observations, we derive a model for the origin and accumulation of the inclusions making up the clog, which suggests that the NMI of the two dominant populations do not come from the bulk steel, although secondary metallurgy-derived inclusions can be traced in the clog. Overall, microstructures show that the clog behaves as a coherent solid, and is able to displace and fracture.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Development and Evaluation of HVAC Operation Schedule Detection Algorithm

Small- and medium-sized (<50,000 sf) commercial buildings (SMBs) represent over 94% of the U.S. commercial building stock and consume over 1018 kJ (~1015 Btus) of total site energy. Many of these buildings use rudimentary controls that are mostly manual, with limited scheduling capability and no monitoring or failure management. Therefore, many of them are operated inefficiently and consume excess energy. SMBs typically use packaged rooftop units (RTUs) that are controlled by an individual thermostat. Studies have shown that managing set points and schedules of the RTUs will result in up to 20% energy/cost savings. This paper is focused only on the detection of the occupancy schedule using the zone temperature. The paper describes the methodology to detect the occupancy schedule, the metric used to validate the algorithm and the validation results. The algorithm has been tested and validated using field data from a number of RTUs from six buildings in different climate locations. Overall, the algorithm was successful in detecting the occupancy schedule using a symbolic aggregate approximation (SAX) technique. The results showed that the occupancy schedule detection algorithm using the SAX method performs well under summer (hot) and winter (cold) weather conditions when energy use is the highest. The algorithm can also distinguish different schedules and generate clusters of occupancy scheduling based on zone temperature measurements alone.

Kim, Woohyun↗
DOE OSTI2022

Residential Demand Flexibility: Modeling Occupant Behavior using Sociodemographic Predictors

Demand flexibility (DF) has the potential to increase the saturation of renewables in the grid and reduce operating costs for both utilities and customers. However, less than 8% of U.S. residential electric customers are enrolled in DF programs. A major research gap on this topic is an uneven understanding of behavioral drivers of electricity use and DF program participation at the household level. In this study, we employ machine learning models to predict residential occupant behavior in activities relevant to DF. We model occupants' extensive decisions (i.e., choice of action) and intensive behaviors (i.e., amount of time spent) during peak and off-peak time periods using the publicly available American Time Use Survey, which includes activities data for approximately 200,000 respondents. In our machine learning models, predictions for both extensive and intensive behavior fell within a +/-20% error margin at the aggregate level. We identify 13 key sociodemographic predictors of DF-related intensive behavior using LASSO inference and beta coefficient ranking. However, these top predictors differ by activity, suggesting potential scope for differential user targeting for DF events and technologies during program design. This work also contributes to understanding when and who might adopt these DF technologies based on their daily routine activities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Simple spatial scaling rules behind complex cities

Although most of wealth and innovation have been the result of human interaction and cooperation, we are not yet able to quantitatively predict the spatial distributions of three main elements of cities: population, roads, and socioeconomic interactions. By a simple model mainly based on spatial attraction and matching growth mechanisms, we reveal that the spatial scaling rules of these three elements are in a consistent framework, which allows us to use any single observation to infer the others. All numerical and theoretical results are consistent with empirical data from ten representative cities. In addition, our model can also provide a general explanation of the origins of the universal super- and sub-linear aggregate scaling laws and accurately predict kilometre-level socioeconomic activity. Our work opens a new avenue for uncovering the evolution of cities in terms of the interplay among urban elements, and it has a broad range of applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs with Hybrid Parallelism

Here, we present scalable hybrid-parallel algorithms for training large-scale 3D convolutional neural networks. Deep learning-based emerging scientific workflows often require model training with large, high-dimensional samples, which can make training much more costly and even infeasible due to excessive memory usage. We solve these challenges by extensively applying hybrid parallelism throughout the end-to-end training pipeline, including both computations and I/O. Our hybrid-parallel algorithm extends the standard data parallelism with spatial parallelism, which partitions a single sample in the spatial domain, realizing strong scaling beyond the mini-batch dimension with a larger aggregated memory capacity. We evaluate our proposed training algorithms with two challenging 3D CNNs, CosmoFlow and 3D U-Net. Our comprehensive performance studies show that good weak and strong scaling can be achieved for both networks using up to 2K GPUs. More importantly, we enable training of CosmoFlow with much larger samples than previously possible, realizing an order-of-magnitude improvement in prediction accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Determination of the Disperse Composition of a PbO Suspension Containing Aggregates of Particles of Lamellar Shape by the Laser-Polarimetry Method

The results of measurements of the scattering matrix at a wavelength of 0.63 μm in the range of scattering angles of 10°–155° are presented for an aqueous suspension of lead oxide containing particles of plate form and their aggregates of monomers with dimensions of ~5 nm. The results of the measurements are compared with the results of calculations for axially symmetric scatterers (ellipsoids of rotation, cylinders). It is shown that the presence of aggregates affects the scattering properties of such a medium. The results of reconstructing the distribution of particles of a disperse medium in sizes from the measurements data of the scattering matrix are presented. The reconstruction of the distributions was carried out by solving the problem of optimizing the sum of the squared deviations of the experimental and calculated values of matrix elements in the framework of the model of axially symmetric scatterers. It is shown that the distribution of particles by sizes is more accurately reconstructed by minimizing the sum of the squares of the deviations for the sum of the diagonal elements. The obtained distribution is compared with the distribution measured by the method of dynamic light scattering.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

A Pattern Language for High-Performance Computing Resilience

High-performance computing systems (HPC) provide powerful capabilities for modeling, simulation, and data analytics for a broad class of computational problems. They enable extreme performance of the order of quadrillion floating-point arithmetic calculations per second by aggregating the power of millions of compute, memory, networking and storage components. With the rapidly growing scale and complexity of HPC systems for achieving even greater performance, ensuring their reliable operation in the face of system degradations and failures is a critical challenge. System fault events often lead the scientific applications to produce incorrect results, or may even cause their untimely termination. The sheer number of components in modern extreme-scale HPC systems and the complex interactions and dependencies among the hardware and software components, the applications, and the physical environment makes the design of practical solutions that support fault resilience a complex undertaking. To manage this complexity, we developed a methodology for designing HPC resilience solutions using design patterns. We codified the well-known techniques for handling faults, errors and failures that have been devised, applied and improved upon over the past three decades in the form of design patterns. In this paper, we present a pattern language to enable a structured approach to the development of HPC resilience solutions. The pattern language reveals the relations among the resilience patterns and provides the means to explore alternative techniques for handling a specific fault model that may have different efficiency and complexity characteristics. Using the pattern language enables the design and implementation of comprehensive resilience solutions as a set of interconnected resilience patterns that can be instantiated across layers of the system stack.

Hukerikar, Saurabh↗
DOE OSTI2016

Bayesian optimization of the Community Land Model simulated biosphere-atmosphere exchange using CO 2 observations from a dense tower network and aircraft campaigns over Oregon

Here, the vast forests and natural areas of the Pacific Northwest comprise one of the most productive ecosystems in the northern hemisphere. The heterogeneous landscape of Oregon poses a particular challenge to ecosystem models. We present a framework using a scaling factor Bayesian inversion to improve the modeled atmosphere-biosphere exchange of carbon dioxide. Observations from 5 CO/CO 2 towers, eddy covariance towers, and airborne campaigns were used to constrain the Community Land Model CLM4.5 simulated terrestrial CO 2 exchange at a high spatial and temporal resolution (1/24°, 3-hourly). To balance aggregation errors and the degrees of freedom in the inverse modeling system, we applied an unsupervised clustering approach for the spatial structuring of our model domain. Data from flight campaigns were used to quantify the uncertainty introduced by the Lagrangian particle dispersion model that was applied for the inversions. The average annual statewide net ecosystem productivity (NEP) was increased by 32% to 29.7 TgC per year by assimilating the tropospheric mixing ratio data. The associated uncertainty was decreased by 28.4% to 29%, on average over the entire Oregon model domain with the lowest uncertainties of 11% in western Oregon. The largest differences between posterior and prior CO 2 fluxes were found for the Coast Range ecoregion of Oregon that also exhibits the highest availability of atmospheric observations and associated footprints. In this area, covered by highly productive Douglas-fir forest, the differences between the prior and posterior estimate of NEP averaged 3.84 TgC per year during the study period from 2012 through 2014.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Silicon photonic chip for 16-channel wavelength division (de-)multiplexing in the O-band

We experimentally demonstrate a silicon photonic chip-scale 16-channel wavelength division multiplexer (WDM) operating in the O-band. The silicon photonic chip consists of a common-input bus waveguide integrated with a sequence of 16 spectral add-drop filters implemented by 4-port contra-directional Bragg couplers and resonant cladding modulated perturbations. The combination of these features reduces the spectral bandwidth of the filters and improves the crosstalk. An apodization of the cladding modulated perturbations between the bus and the add/drop waveguides is used to optimize the strength of the coupling coefficient in the propagation direction to reduce the intra-channel crosstalk on adjacent channels. The fabricated chip was validated experimentally with a measured intra-channel crosstalk of ∼−18.9 dB for a channel spacing of 2.6 nm. The multiplexer/demultiplexer chip was also experimentally tested with a 10 Gbps data waveform. The resulting eye-pattern indicates that this approach is suitable for datacenter WDM-based interconnects in the O-band with large aggregate bandwidths.

Davis, Jordan A. (ORCID:0000000343483295)↗