Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “analyzed”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Science opportunity analyzer - a multi-mission approach to science planning

In the past Science Planning for space missions has been comprised of using ad-hoc software toolscollected or reconstructed from previous missions, tools used by other groups who often speak a different 'technical' language or even 'the backs of envelopes'. In addition to the tools being rough, the work done with these tools often has had to be redone or at least re-entered when it came time to determine actual observations. Science Opportunity Analyzer (SOA), a Java-based application, has been built for scientists to enable them to identify/analyze observation opportunities and then, to create corresponding observation designs.

Science opportunity analyzer science planning SOA ↗
NASA NTRS2012

The Geometric Factor of Electrostatic Plasma Analyzers: A Case Study from the Fast Plasma Investigation for the Magnetospheric Multiscale mission

We report our findings comparing the geometric factor (GF) as determined from simulations and laboratory measurements of the new Dual Electron Spectrometer (DES) being developed at NASA Goddard Space Flight Center as part of the Fast Plasma Investigation on NASA's Magnetospheric Multiscale mission. Particle simulations are increasingly playing an essential role in the design and calibration of electrostatic analyzers, facilitating the identification and mitigation of the many sources of systematic error present in laboratory calibration. While equations for laboratory measurement of the Geometric Factpr (GF) have been described in the literature, these are not directly applicable to simulation since the two are carried out under substantially different assumptions and conditions, making direct comparison very challenging. Starting from first principles, we derive generalized expressions for the determination of the GF in simulation and laboratory, and discuss how we have estimated errors in both cases. Finally, we apply these equations to the new DES instrument and show that the results agree within errors. Thus we show that the techniques presented here will produce consistent results between laboratory and simulation, and present the first description of the performance of the new DES instrument in the literature.

Magnetospheric Multiscale Mission↗
NASA NTRS2014

Aperture Valve for the Mars Organic Molecule Analyzer (MOMA)

NASA's participation in the multi-nation ExoMars 2018 Rover mission includes a critical astrobiology Mass Spectrometer Instrument on the Rover called the Mars Organic Molecule Analyzer (MOMA). The Aperture Valve is a critical electromechanical valve used by the Mass Spectrometer to facilitate the transfer of ions from Martian soil to the Mass Spectrometer for analysis. The MOMA Aperture Valve development program will be discussed in terms of the Initial valve design and subsequent improvements that resulted from prototype testing. The Initial Aperture Valve concept seemed promising, based on calculations and perceived merits. However, performance results of this design were disappointing, due to delamination of TiN and DLC coatings applied to the Titanium base metals, causing debris from the coatings to seize the valve. While peer reviews and design trade studies are important forums to vet a concept design, results from testing should not be underestimated. Despite the lack of development progress to meet requirements, valuable information from weakness discovered in the Initial Valve design was used to develop a second, more robust Aperture valve. Based on a check-ball design, the ETU /flight valve design resulted in significantly less surface area to create the seal. Moreover, PVD coatings were eliminated in favor of hardened, nonmagnetic corrosion resistant alloys. Test results were impressive, with the valve achieving five orders of magnitude better sealing leak rate over end of life requirements. Cycle life was equally impressive, achieving 280,000 cycles without failure.

00000↗
NASA NTRSDate not supplied

Using Landsat 8 Satellite Imagery to Analyze Biogeochemical Constituents in the Waters of the San Francisco Bay Area and Beyond

The ocean's coastal zones play a key role in our planet's health and mitigate the adverse effects of climate change. Building on the success of NASA satellite imagery in mapping land cover changes around the globe in response to climate change, deforestation, and natural disasters, I have worked to determine how aquatic reflectance images from the Landsat 8 sensor could be correlated with biogeochemical constituents within the waters of the San Francisco Bay Area and California coastline. By analyzing Landsat 8 satellite imagery from the years 2013-2020 in collaboration with data from the United States Geological Survey (USGS), San Francisco Estuary Institute (SFEI), and universities involved with the Harmful Algal Bloom Monitoring and Alert Program (HABMAP), I investigated which areas of the San Francisco Bay and California coastline demonstrated significant correlations with values extracted from the satellite imagery. Using a Chlorophyll Index (CI) ratio calculated from two Landsat 8 satellite reflectance bands, it was found that there is a positive correlation between the CI and calculated oxygen, suspended particulate matter, and silicate from several USGS sampling points. In contrast, there is a significant negative correlation between the CI and salinity, nitrite, and phosphate at those same locations. Moreover, large suspected harmful algal blooms (HABs) along the California coastline seen in the Landsat 8 imagery were corroborated with HAB data from various institutions. Understanding the effectiveness of Landsat 8 aquatic reflectance images will allow scientists to predict HABs and other nutrient cycling that may be harmful to aquatic ecosystems around the world.

Landsat↗
DOE OSTI2022

Analyzing count data with measurement error

In this article, we analyze observed count data such as the number of defects in a steel product where the observed counts are the true counts measured with errors. We account for the measurement error by using a measurement error model based on a latent lognormal (LLN) distribution. We consider making inference about a single population (e.g., from samples of a production lot) and a regression model (e.g., from runs of a designed experiment), where the measurement system properties are known, that is, the parameters of the LLN distribution are known. Then, we consider simultaneous inference for the single population and regression model as well as the measurement system. We demonstrate the proposed methodology with both simulated and real observed counts.

42 ENGINEERING↗
DOE OSTI2026

Analyzing the Free States of one Quantum Resource Theory as Resource States of Another

In the context of quantum resource theories (QRTs), free states are defined as those that can be obtained at no cost under a certain restricted set of conditions. However, when taking a free state from one QRT and evaluating it through the optics of another QRT, it might well turn out that the state is now extremely resourceful. Such realization has recently prompted numerous works characterizing states across several QRTs. Here, in this work, we contribute to this body of knowledge by analyzing the resourcefulness in free states for—and across witnesses of—the QRTs of multipartite entanglement, fermionic non-Gaussianity, imaginarity, realness, spin coherence, Clifford non-stabilizerness, $S_n$-equivariance, and non-uniform entanglement. We provide rigorous theoretical results as well as present numerical studies that showcase the rich and complex behavior that arises in this type of cross-examination.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

A thermochemical database from high-throughput first-principles calculations and its application to analyzing phase evolution in AM-fabricated IN718

A comprehensive thermochemical database is constructed based on high–throughput first-principles phonon calculations of over 3000 atomic structures in limited concentrations in Ni, Fe, and Co alloys involving a total of 26 elements including Al, B, C, Cr, Cu, Hf, La, Mn, Mo, N, Nb, O, P, Re, Ru, S, Si, Ta, Ti, V, W, Y, and Zr, providing thermochemical data largely unavailable from existing experiments. Here, the database can be employed to predict the equilibrium phase compositions and fractions directly from first-principles by minimizing the chemical potential of a multicomponent system with a fixed overall chemical composition and a fixed temperature. It is applied to the additively manufactured nickel-based IN718 superalloy to analyze the phase evolution with temperature. IN718 is known for its great performance in tensile, fatigue, creep, and rupture strength, combined with easy fabrication and corrosion resistance. In particular, we successfully predicted the formation of L1 0 -FeNi, γ’-Ni 3 (Fe,Al), α-Cr, δ-Ni 3 (Nb,Mo), γ”-Ni 3 Nb, and η-Ni 3 Ti at low temperatures (below 680 K), γ’-Ni 3 Al, δ-Ni 3 Nb, γ”-Ni 3 Nb, α-Cr, and γ-Ni(Fe,Cr,Mo) at intermediate temperatures (between 680 and 1140 K), and δ-Ni 3 Nb and γ-Ni(Fe,Cr,Mo) at high temperatures (above 1140 K) in IN718. These predictions are validated by EDS mapping of compositional distributions and corresponding identifications of phase distributions. The database is expected to be a valuable source for future thermodynamic analysis and microstructure prediction of alloys involving the 26 elements.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A multiscale approach to analyze the effect of radial void fraction distributions on BWR neutronic lattice parameters

The goal of this study is to analyze the effect of radial void fraction distribution, considered at three degrees of fidelity, on lattice and 2-group homogenized cross sections. The novelty of this work lies in its use of experimentally-determined radial void fraction distributions which allows for fine-scale coolant density data with no predictive uncertainty and which could be prohibitively expensive to simulate over the range of operational parameters used in the experiments. The experimentally-determined radial void fraction distributions are also characterized using a statistical tool called a semi-variogram in order to determine the scale of continuity. Void fraction distributions are averaged at the lattice and subchannel level for comparison with the fine-scale model. Models averaged at the subchannel and lattice level show a difference in about 68% of the time. Also, by comparing 2-group homogenized cross-sections, the differences between the subchannel and homogeneous cases are measurable.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Development of a conduction-based model for analyzing frozen startup of alkali-metal heat pipes

One key area of interest in heat pipe modeling/simulation is to analyze the startup behavior of the liquid-metal heat pipes (LMHPs) from a frozen state. This so-called ‘frozen startup’ process involves a complex set of nonlinear mass and heat transport phenomena, including phase transitions from solid to liquid and vapor, multiphase interactions, microporous wick flow, and compressible vapor dynamics. The complexity of these processes makes it challenging to simulate LMHP’s frozen startup using conventional numerical methods or commercial computational fluid dynamics (CFD) software. This paper presents a simplified conduction-based modeling approach that can provide practical insights into the entire LMHP frozen startup process, while alleviating the challenges of modeling its complex physics. The theoretical foundation and physical assumptions of the proposed model are based solely on heat-conduction equation, allowing for a more tractable simulation without sacrificing essential physical accuracy. The proposed model was implemented in a commercial CFD software, and its prediction was compared with the experimental data obtained from sodium heat-pipe startup experiments. The comparison highlights the proposed model's ability to capture the transient thermal behavior of LMHP during frozen startup. This study not only validates the conduction-based frozen startup modeling method but also shows its potential as a practical and efficient tool for understanding the startup performance of the LMHP systems.

Microreactor↗
DOE OSTI2024

PlasmoData.jl — A Julia framework for modeling and analyzing complex data as graphs

Datasets encountered in scientific and engineering applications appear in complex formats (e.g., images, multivariate time series, molecules, video, text strings, networks). Graph theory provides a unifying framework to model such datasets and enables the use of powerful tools that can help analyze, visualize, and extract value from data. In this work, we present PlasmoData.jl, an open-source, Julia framework that uses concepts of graph theory to facilitate the modeling and analysis of complex datasets. The core of our framework is a general data modeling abstraction, which we call a DataGraph. We show how the abstraction and software implementation can be used to represent diverse data objects as graphs and to enable the use of tools from topology, graph theory, and machine learning (e.g., graph neural networks) to conduct a variety of tasks. We illustrate the versatility of the framework by using real datasets: (i) an image classification problem using topological data analysis to extract features from the graph model to train machine learning models; (ii) a disease outbreak problem where we model multivariate time series as graphs to detect abnormal events; and (iii) a technology pathway analysis problem where we highlight how we can use graphs to navigate connectivity. Further, our discussion also highlights how PlasmoData.jl leverages native Julia capabilities to enable compact syntax, scalable computations, and interfaces with diverse packages. Overall, we show that the DataGraph abstraction and PlasmoData.jl Julia package are able to model data within graphs and enable useful analysis.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

Incorporating space and time into random forest models for analyzing geospatial patterns of drug-related crime incidents in a major U.S. metropolitan area

The opioid crisis has hit American cities hard, and research on spatial and temporal patterns of drug-related activities including detecting and predicting clusters of crime incidents involving particular types of drugs is useful for distinguishing hot zones where drugs are present that in turn can further provide a basis for assessing and providing related treatment services. In this study, we investigated spatiotemporal patterns of more than 52,000 reported incidents of drug-related crime at block group granularity in Chicago, IL between 2016 and 2019. We applied a space-time analysis framework and machine learning approaches to build a model using training data that identified whether certain locations and built environment and sociodemographic factors were correlated with drug-related crime incident patterns, and establish the top contributing factors that underlaid the trends. Space and time, together with multiple driving factors, were incorporated into a random forest model to analyze these changing patterns. We accommodated both spatial and temporal autocorrelation in the model learning process to assist with capturing the changes over time and tested the capabilities of the space-time random forest model by predicting drug-related activity hot zones. Overall, we focused particularly on crime incidents that involved heroin and synthetic drugs as these have been key drug types that have highly impacted cities during the opioid crisis in the U.S.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Analyzing acoustic emission data to identify cracking modes in cement paste using an artificial neural network

This research is focused on the identification of cracking mechanisms for cement paste using acoustic emission data, recorded from compression and notched four-point bending tests. A procedure is developed for analyzing the data by employing an agglomerative hierarchical clustering method, an artificial neural network, and a ray-tracing source location algorithm. An agglomerative hierarchical clustering method is utilized to cluster the AE data from a compression test using frequency-dependent features. A neural network is trained using the compression test data and applied to the AE data emitted during the four-point bending test. The clustered data from the four-point bending test is localized using a ray-tracing algorithm. Based on the occurrence and locations of the clustered events and signal feature analyses, potential cracking mechanisms are identified and assigned.

36 MATERIALS SCIENCE↗