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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 1,783 records · Page 99
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Analysis of Moessbauer Data from Mars: A Database and Artificial Neural Network for Identification of Iron-bearing Phases

The exploration of the planet Mars is one of the major goals within the Solar system exploration programs of the US-American space agency NASA and the European Space Agency ESA. In particular the search for water and life and understanding of the history of the surface and atmosphere will be the major tasks of the upcoming space missions to Mars. The miniaturized Moessbauer spectrometer MIMOS II has been selected for the NASA Mars-Exploration-Rover twin-mission to Mars in 2003 and the ESA 2003 Mars-Express Beagle 2 mission. Reduced in size and weight, in comparison to ordinary laboratory setup, the sensor head just weights approximately 400 g, with a volume of (50x50x90) cu mm, and holds two gamma-ray sources: the stronger for experiments and the weaker for calibrations. The collimator (in sample direction) also shields the primary radiation off the detectors. Around the drive four detectors are mounted. The detectors are made of Si-PIN-photodiodes in chip form (100 sq mm, thickness of 0.5 mm). The control unit is located in a separate electronics board. This board is responsible for the power supply, generation of the drive's velocity reference signal, read of the detector pulses to record the spectrum, data storage and communication with the host computer. After more than four decades from the discovery of the Moessbauer effect, more than 400 minerals were studied at different temperatures. Their Moessbauer parameters were reported in the literature, and have been recently collected in a data bank. Previous Mars-missions, namely Viking and Mars Pathfinder, revealed Si, Al, Fe, Mg, Ca, K, Ti, S and Cl to be the major constituents in soil and rock elemental composition of the red planet. More than 200 minerals already studied by Moessbauer spectroscopy contain significant amounts of these elements. A considerable number of Moessbauer studies were also carried out on meteorites and on Moon samples. Looking backward in the studies of the whole Moessbauer community, we have built a specific library containing Moessbauer parameters of those possible Mars minerals. The selected minerals, their Moessbauer parameter values (min. max. s.d and number of available data), main site substitution, behavior as a function of temperature and a ranking as expected to be found on Mars were organized. Mars-analogue Fe-bearing minerals not studied by Moessbauer spectroscopy are being collected and investigated. In addition, it an identification system based on Artificial Neural Networks (ANN) was implemented which enables fast and precise mineral identification from the experimental Moessbauer parameters at a given temperature.

P A de Souza Jr.↗
DOE OSTI2020

Evaluation of Hybrid FPOG Applications in Regulated and Deregulated Markets Using HERON

Recent changes in the U.S. energy market, such as low natural gas prices and increased electricity production for variable renewable energy (VRE) sources, have led to an economic crisis for existing light-water reactor (LWR) nuclear power plants (NPP). Many owners and operators of LWRs have elected to decommission these plants rather than continue using them as consistent sources of clean baseload power. This has led to exploration of various possibilities to increase the economic viability of these units, including market restructuring to monetize benefits LWRs already provide to the grid through ancillary markets, load following and economic dispatch, and possible integration of secondary systems directly to the NPP for production of additional products through technologies such as hydrogen electrolysis or water desalination. Previous studies have considered the technologies associated with these Integrated Energy Systems (IES) activities, and the analysis of markets for these secondary products. To analyze the economic viability of various system configurations including IES, especially given the uncertainty surrounding load demand, electricity prices, and the availability of VRE resources, the stochastic technoeconomic analysis package HERON (Heuristic Energy Resource Optimization Network) was released earlier this year as an extension of the risk analysis framework RAVEN (Risk Analysis Virtual Environment). HERON focuses foremost on making the complex uncertainty quantification analysis tools approachable for energy systems analysts, also providing general dispatch optimization algorithms for those workflows. HERON continues to be improved and tested as a significant part of the IES viability analyses performed in this work. HERON is not a capacity expansion model. To consider market and grid energy system development in a variety of scenarios, HERON is best used in coupling with modelling tools such as US-REGEN, which sacrifice some of the uncertainty analysis and resolution of HERON's modelling for the ability to efficiently predict the change in the grid energy system's profile due to economic drivers over decades. HERON can then use this information to explore the economic viability of introducing changes to the predicted outcomes, such as the introduction of an IES. In this work, experts at EPRI using US-REGEN provide six projection scenarios for use in HERON stochastic technoeconomic analysis (STEA) in considering the options available for increasing LWR economic viability through introduction of a hydrogen-centric IES using a high-temperature steam electrolysis plant (HTSE), hydrogen storage, and a constant-rate contracted hydrogen consumer. The results obtained are differential in nature; they do not report expected profits for any configuration, but rather report on the possible increase in the NPV of a configuration with respect to a baseline no-IES configuration. Due to the uncertainty captured in the variable net load of the systems, there is likewise uncertainty in the mean values reported. We consider this viability both in terms of a regulated market, where the energy producers and IES are owned and operated by single entity, as well as a deregulated market, where the IES chooses its bid for electricity generation and is then dispatched by the grid system operator. Results indicate that for deregulated markets, the inclusion of the IES is often statistically beneficial. This is especially true in policies that are not favorable towards nuclear, as nuclear is less often dispatched and is forced to deal with frequent idle capacity. In the nominal case as well as the case of carbon tax policies, inclusion of the IES clearly benefited the economic performance of the NPP. In the regulated case, however, there was a trend towards minimizing the IES, likely due to the optimal sizing performed by US-REGEN of the NPP within the system as well as the lack of penalty for idle capacity at the NPP in the regulated market analyses.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS1995

Reducing neural network training time with parallel processing

Obtaining optimal solutions for engineering design problems is often expensive because the process typically requires numerous iterations involving analysis and optimization programs. Previous research has shown that a near optimum solution can be obtained in less time by simulating a slow, expensive analysis with a fast, inexpensive neural network. A new approach has been developed to further reduce this time. This approach decomposes a large neural network into many smaller neural networks that can be trained in parallel. Guidelines are developed to avoid some of the pitfalls when training smaller neural networks in parallel. These guidelines allow the engineer: to determine the number of nodes on the hidden layer of the smaller neural networks; to choose the initial training weights; and to select a network configuration that will capture the interactions among the smaller neural networks. This paper presents results describing how these guidelines are developed.

Rogers, James L., Jr.↗
NASA NTRS2011

Biochip microsystem for bioinformatics recognition and analysis

A system with applications in pattern recognition, or classification, of DNA assay samples. Because DNA reference and sample material in wells of an assay may be caused to fluoresce depending upon dye added to the material, the resulting light may be imaged onto an embodiment comprising an array of photodetectors and an adaptive neural network, with applications to DNA analysis. Other embodiments are described and claimed.

Lue, Jaw-Chyng↗
DOE OSTI2021

User Role Identification in Software Vulnerability Discussions over Social Networks

Understanding and early awareness of software vulnerabilities is vital for preventing and mitigating potential impacts from cybersecurity events. One step toward early characterization of software vulnerabilities may involve analyzing discussion and spread of information in online social networks. Prior work has used information from such discussions over multiple online forums to develop dynamic networks among users followed by analysis of structure, spread, and information evolution. In this work, we advance the state-of-the-art by focusing on data-driven learning of types, roles, and transition of roles exhibited by users over time. In social networks, users take on particular roles based on their actions and structure of the network. Identifying “meaningful” roles can help separate potential users of interest from the larger community, and identify patterns in a network. We will identify and compare roles found in online forums (e.g., Twitter) using techniques such as feature-based Non-negative Matrix Factorization coupled with topological and influence-based measures of centrality. Since users’ activities change over time, we also analyze role evolution in dynamic networks.

Jones, Rebecca D.↗
DOE OSTI2016

On the degelation of networks – Case of the radiochemical degradation of methyl methacrylate – ethylene glycol dimethacrylate copolymers

Methyl methacrylate networks were synthetized and submitted to radiochemical degradation. Ageing was monitored by means of sol-gel analysis and glass transition temperature measurements. Networks were shown to undergo exclusively chain scission process leading to the degelation of network. The critical conversion degree corresponding to degelation (loss of all elastically active chains) is discussed regarding a statistical theory.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2024

Static and Dynamic Thermomechanical Properties of Phase-Separated Epoxy Networks with Tuned Microstructures

Here, polymerization-induced phase separation is a useful method for the construction of heterogeneous epoxy networks with properties exceeding their homogeneous counterparts. In this work, we examine the static and dynamic thermomechanical properties of phase-separated epoxy networks salient to their application as encapsulants. Three heterogeneous epoxy-amine networks with nano-, meso-, and macro-phase-separated morphologies comprised of hard and soft domains are compared to a rigid, unstructured network. The glass transition profiles of the heterogeneous networks are complex, spanning many decades in the frequency domain. The nanophase-separated morphology leads to higher coefficient of thermal expansion, yet surprisingly is characterized by reduced residual stress. Under both quasi-static and dynamic compression (strain rates of order 10 –3 and 10 3 s –1 , respectively), the nanophase-separated network also exhibits higher modulus and strength. In split-Hopkinson bar experiments, the energy dissipation characteristics of the epoxy networks were nearly identical. Curiously, however, the Hugoniot response of the macro-phase-separated network determined by ballistic shockwave analysis indicates a remarkable ability of this material to mitigate shockwave propagation in comparison to many homogeneous and heterogeneous polymer materials. Collectively, this work reveals several previously unreported phenomena with respect to structure–property relationships in phase-separated epoxy networks, illustrating the potential value of systematically tuned microstructures for optimization of application-specific physical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Revisiting the Al/Al 2 O 3 Interface: Coherent Interfaces and Misfit Accommodation

We report the coherent and semi-coherent Al/α-Al 2 O 3 interfaces using molecular dynamics simulations with a mixed, metallic-ionic atomistic model. For the coherent interfaces, both Al-terminated and O-terminated nonstoichiometric interfaces have been studied and their relative stability has been established. To understand the misfit accommodation at the semi-coherent interface, a 1-dimensional (1D) misfit dislocation model and a 2-dimensional (2D) dislocation network model have been studied. For the latter case, our analysis reveals an interface dislocation structure with a network of three sets of parallel dislocations, each with pure-edge character, giving rise to a pattern of coherent and stacking-fault-like regions at the interface. Structural relaxation at elevated temperatures leads to a further change of the dislocation pattern, which can be understood in terms of a competition between the stacking fault energy and the dislocation interaction energy at the interface. In conclusion, our results are expected to serve as an input for the subsequent dislocation dynamics models to understand and predict the macroscopic mechanical behavior of Al/α-Al 2 O 3 composite heterostructures.

36 MATERIALS SCIENCE↗
DOE OSTI2011

Diagnosing Anomalous Network Performance with Confidence

Variability in network performance is a major obstacle in effectively analyzing the throughput of modern high performance computer systems. High performance interconnec- tion networks offer excellent best-case network latencies; how- ever, highly parallel applications running on parallel machines typically require consistently high levels of performance to adequately leverage the massive amounts of available computing power. Performance analysts have usually quantified network performance using traditional summary statistics that assume the observational data is sampled from a normal distribution. In our examinations of network performance, we have found this method of analysis often provides too little data to under- stand anomalous network performance. Our tool, Confidence, instead uses an empirically derived probability distribution to characterize network performance. In this paper we describe several instances where the Confidence toolkit allowed us to understand and diagnose network performance anomalies that we could not adequately explore with the simple summary statis- tics provided by traditional measurement tools. In particular, we examine a multi-modal performance scenario encountered with an Infiniband interconnection network and we explore the performance repeatability on the custom Cray SeaStar2 interconnection network after a set of software and driver updates.

Settlemyer, Bradley W↗
DOE OSTI2022

A Roadmap for Edge Computing Enabled Automated Multidimensional Transmission Electron Microscopy

The advent of modern, high-speed electron detectors has made the collection of multidimensional hyperspectral transmission electron microscopy datasets, such as 4D-STEM, a routine. However, many microscopists find such experiments daunting since analysis, collection, long-term storage, and networking of such datasets remain challenging. Some common issues are their large and unwieldy size that often are several gigabytes, non-standardized data analysis routines, and a lack of clarity about the computing and network resources needed to utilize the electron microscope. The existing computing and networking bottlenecks introduce significant penalties in each step of these experiments, and thus, real-time analysis-driven automated experimentation for multidimensional TEM is challenging. One solution is to integrate microscopy with edge computing, where moderately powerful computational hardware performs the preliminary analysis before handing off the heavier computation to high-performance computing (HPC) systems. In this work, we trace the roots of computation in modern electron microscopy, demonstrate deep learning experiments running on an edge system, and discuss the networking requirements for tying together microscopes, edge computers, and HPC systems.

47 OTHER INSTRUMENTATION↗
DOE OSTI2013

Architectural Analysis of a LLNL LWIR Sensor System

The architecture of an LLNL airborne imaging and detection system is considered in this report. The purpose of the system is to find the location of substances of interest by detecting their chemical signatures using a long-wave infrared (LWIR) imager with geo-registration capability. The detection system consists of an LWIR imaging spectrometer as well as a network of computer hardware and analysis software for analyzing the images for the features of interest. The system has been in the operations phase now for well over a year, and as such, there is enough use data and feedback from the primary beneficiary to assess the current successes and shortcomings of the LWIR system architecture. LWIR system has been successful in providing reliable data collection and the delivery of a report with results. The weakness of the architecture has been identified in two areas: with the network of computer hardware and software and with the feedback of the state of the system health. Regarding the former, the system computers and software that carry out the data acquisition are too complicated for routine operations and maintenance. With respect to the latter, the primary beneficiary of the instrument’s data does not have enough metrics to use to filter the large quantity of data to determine its utility. In addition to the needs in these two areas, a latent need of one of the stakeholders is identified. This report documents the strengths and weaknesses, as well as proposes a solution for enhancing the architecture that simultaneously addresses the two areas of weakness and leverages them to meet the newly identified latent need.

42 ENGINEERING↗
DOE OSTI2020

Application of a long short-term memory for deconvoluting conductance contributions at charged ferroelectric domain walls

Ferroelectric domain walls are promising quasi-2D structures that can be leveraged for miniaturization of electronics components and new mechanisms to control electronic signals at the nanoscale. Despite the significant progress in experiment and theory, however, most investigations on ferroelectric domain walls are still on a fundamental level, and reliable characterization of emergent transport phenomena remains a challenging task. Here, we apply a neural-network-based approach to regularize local I ( V )-spectroscopy measurements and improve the information extraction, using data recorded at charged domain walls in hexagonal (Er 0.99 ,Zr 0.01 )MnO 3 as an instructive example. Using a sparse long short-term memory autoencoder, we disentangle competing conductivity signals both spatially and as a function of voltage, facilitating a less biased, unconstrained and more accurate analysis compared to a standard evaluation of conductance maps. The neural-network-based analysis allows us to isolate extrinsic signals that relate to the tip-sample contact and separating them from the intrinsic transport behavior associated with the ferroelectric domain walls in (Er 0.99 ,Zr 0.01 )MnO 3 . Our work expands machine-learning-assisted scanning probe microscopy studies into the realm of local conductance measurements, improving the extraction of physical conduction mechanisms and separation of interfering current signals.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A New Modeling Approach for a Priori Uncertainties of Laser Tracker Angle Measurements

Methods for modeling the uncertainty in laser tracker angle measurements vary within the metrology industry, leading to confusion and questionable stochastic modeling for survey network adjustments and error propagation analysis. Interpreting the published laser tracker manufacturer performance specifications to determine an a priori sigma value for weighting azimuth and zenith angle measurements can be confusing and has led to differing implementations. Furthermore, this paper proposes a unique way to model survey network a priori laser tracker angular uncertainties based on laser tracker manufacturers’ published maximum permissible error (MPE) values referenced to current standards for weighting survey network measurements. This paper’s proposed model takes into account the disparate effects that pointing errors, target centering errors, and leveling errors have on azimuth and zenith angular uncertainties for measurements with steep sightings and at near ranges.

42 ENGINEERING↗