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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 73 records · Page 4

Neural networks for structural design - An integrated system implementation

The development of powerful automated procedures to aid the creative designer is becoming increasingly critical for complex design tasks. In the work described here Artificial Neural Nets are applied to acquire structural analysis and optimization domain expertise. Based on initial instructions from the user an automated procedure generates random instances of structural analysis and/or optimization 'experiences' that cover a desired domain. It extracts training patterns from the created instances, constructs and trains an appropriate network architecture and checks the accuracy of net predictions. The final product is a trained neural net that can estimate analysis and/or optimization results instantaneously.

Berke, Laszlo↗

Adaptive Fault Tolerance for Many-Core Based Space-Borne Computing

This paper describes an approach to providing software fault tolerance for future deep-space robotic NASA missions, which will require a high degree of autonomy supported by an enhanced on-board computational capability. Such systems have become possible as a result of the emerging many-core technology, which is expected to offer 1024-core chips by 2015. We discuss the challenges and opportunities of this new technology, focusing on introspection-based adaptive fault tolerance that takes into account the specific requirements of applications, guided by a fault model. Introspection supports runtime monitoring of the program execution with the goal of identifying, locating, and analyzing errors. Fault tolerance assertions for the introspection system can be provided by the user, domain-specific knowledge, or via the results of static or dynamic program analysis. This work is part of an on-going project at the Jet Propulsion Laboratory in Pasadena, California.

fault tolerance↗

Near 40 Years MERRA-2 Data at NASA GES DISC -Opportunity and Challenge to Support Extremes Study

To the end of 2019, 40 years NASA climate reanalysis data sets from the Modern Era Retrospective-analysis for Research and Applications, Version 2 (MERRA-2) will be available at NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). MERRA-2 consists atmosphere, land, and ocean data, which may be used for the studies ranging from the short scale weather events to the large scale decadal vulnerabilities. The hourly products, such as precipitation, soil moisture, temperature, and aerosols etc., have been used widely to study extreme events.In supporting users from broad communities, GES DISC have developed various data access services, including subsetter for downloading only data of interest with preferred format; OPeNDAP - for machine-to-machine data access; and Giovanni- for online visualization and analysis, etc. A big challenge for extreme study is to downloading and processing long-term hourly or daily data. The data downloading performance is not very satisfied by many users with current services and the native archived data structure. Late June 2019, many people in Europe had experienced extreme heat waves. The temperatures in several countries exceeded 40°C (104°F). For example, MERRA-2 shows that the near surface daily maximum temperature of June 28 2019 over Marseille, a city in southern France, reached 41.1 °C (106°F), which is the record breaking temperature in the last 40 years. GES DISC is working together with domain science experts to improve the performance of long time series access, making analysis ready data sets in supporting application researches, such as extreme study. In this presentation, using Europe heat wave as an example, we will show prototype of the in developing service for finding extremes from near 40 years MERRA-2 data at a given location. MERRA-2 data can be accessed from NASA GES DISC(https://disc.gsfc.nasa.gov/ ) by search keyword "MERRA-2".

Shen, Suhung↗

Domain Adaptation for Measurements of Strong Gravitational Lenses

Upcoming surveys are predicted to discover galaxy-scale strong lenses on the order of 10\textsuperscript{5}, making deep learning methods necessary in lensing data analysis. Currently, there is insufficient real lensing data to train deep learning algorithms, but the alternative of training only on simulated data results in poor performance on real data. Domain Adaptation may be able to bridge the gap between simulated and real datasets. We utilize domain adaptation for the estimation of Einstein radius ($\Theta_E$) in simulated galaxy-scale gravitational lensing images with different levels of observational realism. We evaluate two domain adaptation techniques - Domain Adversarial Neural Networks (DANN) and Maximum Mean Discrepancy (MMD). We train on a source domain of simulated lenses and apply it to a target domain of lenses simulated to emulate noise conditions in the Dark Energy Survey (DES). We show that both domain adaptation techniques can significantly improve the model performance on the more complex target domain dataset. This work is the first application of domain adaptation for a regression task in strong lensing imaging analysis. Our results show the potential of using domain adaptation to perform analysis of future survey data with a deep neural network trained on simulated data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Domain Adaptation for Measurements of Strong Gravitational Lenses

Upcoming surveys are predicted to discover galaxy-scale strong lenses on the order of $10^5$, making deep learning methods necessary in lensing data analysis. Currently, there is insufficient real lensing data to train deep learning algorithms, but the alternative of training only on simulated data results in poor performance on real data. Domain Adaptation may be able to bridge the gap between simulated and real datasets. We utilize domain adaptation for the estimation of Einstein radius ($\Theta_E$) in simulated galaxy-scale gravitational lensing images with different levels of observational realism. We evaluate two domain adaptation techniques - Domain Adversarial Neural Networks (DANN) and Maximum Mean Discrepancy (MMD). We train on a source domain of simulated lenses and apply it to a target domain of lenses simulated to emulate noise conditions in the Dark Energy Survey (DES). We show that both domain adaptation techniques can significantly improve the model performance on the more complex target domain dataset. This work is the first application of domain adaptation for a regression task in strong lensing imaging analysis. Our results show the potential of using domain adaptation to perform analysis of future survey data with a deep neural network trained on simulated data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Experience report: Using formal methods for requirements analysis of critical spacecraft software

Formal specification and analysis of requirements continues to gain support as a method for producing more reliable software. However, the introduction of formal methods to a large software project is difficult, due in part to the unfamiliarity of the specification languages and the lack of graphics. This paper reports results of an investigation into the effectiveness of formal methods as an aid to the requirements analysis of critical, system-level fault-protection software on a spacecraft currently under development. Our experience indicates that formal specification and analysis can enhance the accuracy of the requirements and add assurance prior to design development in this domain. The work described here is part of a larger, NASA-funded research project whose purpose is to use formal-methods techniques to improve the quality of software in space applications. The demonstration project described here is part of the effort to evaluate experimentally the effectiveness of supplementing traditional engineering approaches to requirements specification with the more rigorous specification and analysis available with formal methods.

Lutz, Robyn R.↗

A Joint Experimental/Computational Study of Non-Idealities in Practical Rotating Detonation Engines. Final Project Report, 10/2015 - 09/2020

The overall goal of this work is to understand non-ideal effects in practical rotating detonation engines (RDEs) that impede the realization of theoretical detonation cycle efficiencies. The main limitations in RDE operability and performance are associated with practical design considerations, where non-idealities, such as incomplete mixing, fuel leakage, secondary deflagration, instabilities and geometry-dependent effects, reduce the effectiveness of the detonation-based compression cycle. The goal of this work is to take a fundamental point of view, and use detailed experiments and simulations to understand non-ideal effects, their contribution to loss in pressure gain, and RDE operability limitations. Current research is mainly focused on design and overall operability of RDEs. Our approach is instead to look at the physics of basic processes occurring in RDEs. In particular, we consider processes associated with non-idealities and we use a combination of laser and non-laser diagnostics and large eddy simulation/direct numerical simulation computations to investigate these underlying phenomena. By advancing the fundamental science of detonation wave propagation in RDE flowfields, and subject to non-ideal conditions, will enable design to progress more rapidly. This work is a collaborative work involving experiments, simulations and external domain experts to understand the physics behind RDEs and non-idealities. Through collaboration between experimentalists and modelers, we have developed targeted experiments to investigate fundamental aspects of the operation of an RDE. Furthermore, we have developed advanced combustion models that account for non-ideal effects in the operation of an RDE and are able to capture the complexities of realistic RDE configurations. We conduct detailed physical and computational experiments to investigate the flame structure ahead and behind detonation fronts in the presence of non-uniform or incomplete mixing, leakage and secondary combustion. We use optical and laser diagnostics to identify the structure of the detonation wave. We consider different working parameters, such injection scheme, geometry, flow rates and equivalence ratios. We brought in DNS/LES capabilities to RDE design by specifically incorporating detailed kinetics and pressure-sensitive combustion physics, thus improving upon current practice based on an Euler equations description that do not take into account viscous effects, while the combustion process is introduced using an induction time based global ignition model. These current models lack the capability of capturing non-ideal mixing and fuel stratification effects — limitations that models and analysis developed in this work have been reduced. Furthermore, the significant changes in pressure within the domain due to the detonation requires combustion models with time-varying pressure effects. In this regard, using DNS with detailed chemistry, and developing LES-based combustion models for complex geometries with detonation wave has enable to understand some of the underlying phenomena controlling the operation of these devices. The main objectives developed by the project are the following ones: Objective 1. Develop canonical and operational RDE configurations, as well as imaging-based diagnostics for understanding fuel stratification, leakage, deflagration and detonation structure under non-ideal conditions in RDEs. Objective 2. Develop detailed computational tools for studying detonation wave propagation processes in RDEs. Objective 3. Develop a comprehensive picture of the fundamental physics governing non-idealities and how they impact RDE performance and operability from both experiments and simulations.

03 NATURAL GAS↗

Stability analysis and discretization of A –$Φ$ time domain integral equations for multiscale electromagnetics

The growth of applications at the intersection between electromagnetic and quantum physics is necessitating the creation of novel computational electromagnetic solvers. Work in this paper presents a new set of time domain integral equations (TDIEs) formulated directly in terms of the magnetic vector and electric scalar potentials that can be used to meet many of the requirements of this emerging area. Stability for this new set of TDIEs is achieved by leveraging an existing rigorous functional framework that can be used to determine suitable discretization approaches to yield stable results in practice. The basics of this functional framework are reviewed before it is shown in detail how it may be applied in developing the TDIEs of this work. Numerical results are presented which validate the claims of stability and accuracy of this method over a wide range of frequencies where traditional methods would fail.

97 MATHEMATICS AND COMPUTING↗

Structural Dynamics of the GW182 Silencing Domain Including its RNA Recognition motif (RRM) Revealed by Hydrogen-Deuterium Exchange Mass Spectrometry

The human GW182 protein plays an essential role in micro(mi)RNA-dependent gene silencing. miRNA silencing is mediated, in part, by a GW182 C-terminal region called the silencing domain, which interacts with the poly(A) binding protein and the CCR4-NOT deadenylase complex to repress protein synthesis. Structural studies of this GW182 fragment are challenging due to its predicted intrinsically disordered character, except for its RRM domain. However, detailed insights into the properties of proteins containing disordered regions can be provided by hydrogen–deuterium exchange mass spectrometry (HDX/MS). In this work, we applied HDX/MS to define the structural state of the GW182 silencing domain. HDX/MS analysis revealed that this domain is clearly divided into a natively unstructured part, including the CCR4-NOT interacting motif 1, and a distinct RRM domain. The GW182 RRM has a very dynamic structure, since water molecules can penetrate the whole domain in 2 h. The finding of this high structural dynamics sheds new light on the RRM structure. Though this domain is one of the most frequently occurring canonical protein domains in eukaryotes, these results are – to our knowledge – the first HDX/MS characteristics of an RRM. The HDX/MS studies show also that the α2 helix of the RRM can display EX1 behavior after a freezing-thawing cycle. This means that the RRM structure is sensitive to environmental conditions and can change its conformation, which suggests that the state of the RRM containing proteins should be checked by HDX/MS in regard of the conformational uniformity. .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Damping constant estimation in magnetoresistive readers

The damping constant is a key design parameter in magnetic reader design. Its value can be derived from bulk or sheet film ferromagnetic resonance (FMR) line width. However, dynamics of nanodevices is usually defined by presence of non-uniform modes. It triggers new damping mechanisms and produces stronger damping than expected from traditional FMR. This work proposes a device-level technique for damping evaluation, based on time-domain analysis of thermally excited stochastic oscillations. The signal is collected using a high bandwidth oscilloscope, by direct probing of a biased reader. Recorded waveforms may contain different noise signals, but free layer FMR is usually a dominating one. The autocorrelation function is a reflection of the damped oscillation curve, averaging out stochastic contributions. The damped oscillator formula is fitted to autocorrelation data, producing resonance frequency and damping constant values. Restricting lag range allows for mitigation of the impact of other phenomena (e.g., reader instability) on the damping constant. For a micromagnetically modeled reader, the technique proves to be much more accurate than the stochastic FMR line width approach. Application to actual reader waveforms yields a damping constant of ∼0.03.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Applications of finite element and wave envelope element approximations to turbofan engine noise radiation including flight effects

The problem of acoustic radiation from turbofan engine inlets in flow has not lent itself fully to analysis by numerical means because of the large domains and high frequencies involved. The current work has extended the use of finite elements and wave envelope elements, elements which simulate decay and wavelike behaviour in their interpolation functions, from the no-flow case in which they have been proven, to cases incorporating mean flow. By employing an irrotational mean flow assumption, the acoustics problem has been posed in an axisymmetric formulation in terms of acoustic velocity potential, thus minimizing computer solution storage requirements. The results obtained from the numerical procedures agree well with known analytical solutions, static experimental jet engines inflow data, and also with flight test results.

Parrett, A. V.↗

2004 Space Report: Environment and Strategy for Space Research at NATO's Research and Technology Organisation (RTO)

This report describes the motivation for and a strategy to enhance the NATO Research and Technology Organisation's (RTO) current space research effort to reflect NATO's growing military dependence on space systems. Such systems and services provided by these systems are critical elements of military operations. NATO uses space systems for operational planning and support, communication, radio navigation, multi-sensor and multi-domain demonstrations. Such systems are also used to promote regional stability. A quantitative analysis of work related to space in the NATO RTO showed that during the period of 1998 - 2004, 5% of the research pursued in the NATO RTO has been clearly focused on space applications. Challenging environmental and organizational barriers for increasing RTO space research were identified. In part, these include lack of sufficient space expertise representation on panels, the military sensitivity of space, current panel work loads and the need for specific technical recommendations from peers. A strategy for enhancing space research in the RTO is to create a limited-life Space Advisory Group (SAG) composed of Space Expert Consultants who are panel members with appropriate expertise and additional expertise from the nations. The SAG will recommend and find support in the nations for specific technical activities related to space in the areas of Space Science, Remote Sensing Data Analysis, Spacecraft Systems, Surveillance and Early Warning, Training and Simulation and Policy. An RTO Space Advisory Group will provide an organizational mechanism to gain recognition of RTO as a forum for trans-Atlantic defence space research and to enhance space research activities.

Woods-Vedeler, Jessica A.↗

A meta-analysis of semantic classification of citations

The aim of this literature review is to examine the current state of the art in the area of citation classification. In particular, we investigate the approaches for characterizing citations based on their semantic type. We conduct this literature review as a meta-analysis covering 60 scholarly articles in this domain. Although we included some of the manual pioneering works in this review, more emphasis is placed on the later automated methods, which use Machine Learning and Natural Language Processing (NLP) for analyzing the fine-grained linguistic features in the surrounding text of citations. The sections are organized based on the steps involved in the pipeline for citation classification. Specifically, we explore the existing classification schemes, data sets, pre-processing methods, extraction of contextual and non-contextual features, and the different types of classifiers and evaluation approaches. The review highlights the importance of identifying the citation types for research evaluation, the challenges faced by the researchers in the process, and the existing research gaps in this field.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Through-thickness microstructural evolution during grain boundary engineering type thermomechanical processing and its implication on sensitization behavior in austenitic stainless steel

Highlights: • GBE-treated 304L steel exhibits thickness-dependent microstructural evolution. • Degree of sensitization increases from surface to interior in GBE specimen. • Low fractal dimension and large TRD size lead to lesser sensitization at surface. • As-received specimen shows high albeit thickness-independent sensitization behavior. • Higher residual strain leads to higher sensitization at surface of deformed sample. - Abstract: Through-thickness microstructural variations and their synergistic influence on degree of sensitization in grain boundary engineering (GBE) treated 304L stainless steel was compared with solution-annealed and marginally cold-worked (~ 3% thickness reduction) specimens. The solution-annealed specimen has shown higher degree of sensitization due to the presence of lower fraction of Σ3 (as well as other low Σ CSL) boundaries and dominant random high-angle boundaries (RHAGBs) connectivity. However, it has shown thickness-independent sensitization behavior as grain boundary character distribution, residual strain and grain boundary network topology towards thickness direction are similar. Owing to higher residual strain, as revealed by local misorientation analysis, the cold-worked specimen showed maximum degree of sensitization (higher than solution-annealed condition) at surface. The GBE specimen has shown much lower degree of sensitization at surface due to higher-fraction of Σ3 boundaries and discontinuous RHAGBs connectivity. However, increasing fraction of RHAGBs and their connectivity, as confirmed by twinning-related domain and fractal analysis, cause more sensitization at interior of this specimen. In spite of this, the degree of sensitization at the interior of GBE specimen is lower than the solution-annealed or cold-worked specimen. Even though the residual strain in GBE specimen is higher than the solution-annealed condition, the lower degree of sensitization indicates that the grain boundary character distribution and grain boundary network topology have higher implication than residual strain in controlling sensitization.

36 MATERIALS SCIENCE↗

A data analysis assistant

This paper discusses the use of a domain-independent planner, COLLAGE, as a software assistant to Earth scientists working with remotely-sensed and ground based data sets. The planner can be viewed as an advisory agent that helps scientists select appropriate data and creates a suitable plan for data-processing that meets stated scientific requirements.

Lansky, Amy L.↗

High-Order Entropy Stable Finite Difference Schemes for Nonlinear Conservation Laws: Finite Domains

Developing stable and robust high-order finite difference schemes requires mathematical formalism and appropriate methods of analysis. In this work, nonlinear entropy stability is used to derive provably stable high-order finite difference methods with formal boundary closures for conservation laws. Particular emphasis is placed on the entropy stability of the compressible Navier-Stokes equations. A newly derived entropy stable weighted essentially non-oscillatory finite difference method is used to simulate problems with shocks and a conservative, entropy stable, narrow-stencil finite difference approach is used to approximate viscous terms.

Fisher, Travis C.↗

Machine Learning For Planetary Mining Applications

Robotic mining could prove to be an efficient method of mining resources for extended missions on the Moon or Mars. One component of robotic mining is scouting an area for resources to be mined by other robotic systems. Writing controllers for scouting can be difficult due to the need for fault tolerance, inter-agent cooperation, and agent problem solving. Reinforcement learning could solve these problems by enabling the scouts to learn to improve their performance over time. This work is divided into two sections, with each section addressing the use of machine learning in this domain. The first contribution of this work focuses on the application of reinforcement learning to mining mission analysis. Various mission parameters were modified and control policies were learned. Then agent performance was used to assess the effect of the mission parameters on the performance of the mission. The second contribution of this work explores the potential use of reinforcement learning to learn a controller for the scouts. Through learning, these scouts would improve their ability to map their surroundings over time.

Cook, Joshua↗

Survey of Cyber Risk Analysis Techniques for Use in the Nuclear Industry

Using traditional probabilistic risk analysis methods for severe accident safety risk management on non-digital systems, structures, and components at nuclear power plants is well-established. In contrast, cyber risk analysis of digital assets is still an immature field with unproven techniques due, in part, to the continuously changing threat environment and the challenge of digital assets failing in unexpected ways. As the nuclear fleet continues to adopt digital instrumentation and control systems, it is increasingly important to have effective and efficient cyber risk analysis techniques to support risk management decisions, such as risk elimination by system redesign or risk mitigation by implementation of prioritized security controls. To understand the state of the art in cyber risk analysis for future research, we surveyed 36 publications across ten application domains. We describe our survey methodology and rate each technique based upon scope, adoptability, and repeatability. In this work, we examine the unique constraints of the nuclear industry and outline the strengths and weaknesses of using the cyber risk analysis techniques in the industry, highlighting gaps with current techniques. We also discuss challenges and potential research directions for advancing the science for both existing and new advanced reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗