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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Formal Foundations for Hierarchical Safety Cases

Safety cases are increasingly being required in many safety-critical domains to assure, using structured argumentation and evidence, that a system is acceptably safe. However, comprehensive system-wide safety arguments present appreciable challenges to develop, understand, evaluate, and manage, partly due to the volume of information that they aggregate, such as the results of hazard analysis, requirements analysis, testing, formal verification, and other engineering activities. Previously, we have proposed hierarchical safety cases, hicases, to aid the comprehension of safety case argument structures. In this paper, we build on a formal notion of safety case to formalise the use of hierarchy as a structuring technique, and show that hicases satisfy several desirable properties. Our aim is to provide a formal, theoretical foundation for safety cases. In particular, we believe that tools for high assurance systems should be granted similar assurance to the systems to which they are applied. To this end, we formally specify and prove the correctness of key operations for constructing and managing hicases, which gives the specification for implementing hicases in AdvoCATE, our toolset for safety case automation. We motivate and explain the theory with the help of a simple running example, extracted from a real safety case and developed using AdvoCATE.

Hierarchy↗
DOE OSTI2024

LENS: Learning Enabled Network Synthesis

RTRC and UMD have developed novel machine learning based methods under the ARPA-E DIFFERENTIATE program for rapid acceleration of hypothesis generation in complex architecture design spaces involving both discrete choices of component inclusion and interconnection and continuous parametric decisions. The project named Learning Enabled Network Synthesis (LENS) further demonstrated the developed methods on challenging electrical power converter design problems by identifying the most suitable circuit topologies and simultaneously selecting the most appropriate components to achieve optimized design of power converter with improved performances. We demonstrated that LENS could enable exploration of very large design space of circuit topologies and components by addressing the limitations of conventional design process in non-linear, high switching speed, multi-dimensional power converter design and optimization. The key innovation developed in LENS is the seamless integration of statistical learning and logical reasoning techniques and building on the individual strengths of these techniques for rapid hypothesis discovery. The main component of LENS comprises of: 1) Graph Reasoning Engine (GRE) to enforce composition rules that rapidly reject all discrete architectures that are composed incorrectly and generates an adaptive database of feasible designs which can be used by ML modules, 2) Graph Generative Learning module which is a deep neural network based generative model for graph architectures which can enable design space exploration beyond the dataset generated by the GRE, 3) Graph Reduced Order Model (ROM) for graph domains for accelerating computation of output metrics, and 4) Active learning and Rule Discovery module for sample efficient learning and extracting logical rules from the learned ML models which will be integrated in the GRE to enhance the filtering effectiveness. LENS approach can be applied to any design domains where designs can be represented as multi-attribute graphs. The LENS team integrated the various technical innovations listed above into an optimization pipeline and exercised the optimization pipeline on the converter design problem. The LENS project demonstrated that the developed AI/ML technologies can be used to generate novel converter circuits >45x faster than experts on chosen use-cases. This can enable faster design space exploration and identification of new designs which are not considered by experts due to the increasing design space complexity. This has significant potential impact on the public and energy needs of the country. It is currently estimated that 30% of all electrical powers generated passes through power converters. The future estimate is that 80% of all power generated would be passing through converters. LENS fills a critical gap in this space since by accelerating the design process the designers would be able to generate more efficient converters which can lead to significant energy savings for the country.

42 ENGINEERING↗
DOE OSTI2023

IDAES-PSE 2.2.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. New features in the 2.2 release include: DiagnosticsToolbox DiagnosticsToolbox (idaes.core.util.model_diagnostics. DiagnosticsToolbox is a new self-guiding toolbox for assisting with diagnosing modeling issues. The toolbox contains a wide range of tools for identifying common modeling issues and conveying these to the user, and more features will be added in the coming months. To try the new toolbox, use the following steps: from idaes.core.util import DiagnosticsToolbox Build an instance of your model Ensure your models has zero degrees of freedom (as best you can) dt = DiagnosticsToolbox(model) dt.report_structural_issues() Follow the prompts Deprecations DegeneracyHunter is being deprecated in favor of the new DiagnosticsToolbox. The features and capabilities of DegeneracyHunter will be moved over to the DiagnositcsToolbox in the coming months. Removal of usage of Pyomo's expr.current due to deprecation upstream. Removed support for radial basis functions in ALAMOpy as these did not work in the current implementation. Those wishing to use radial basis functions should use Pysmo instead. New Capabilities MSContactor model for unit operations involving mass transfer between 2 or more streams, such as membrane, solvent extraction and solids leaching operations. Other Improvements Improved formulation for solubility products with additional parameters for better scaling of terms Better backward compatibility, verification and robustness testing of core models and APIs Added support for Separator models without energy balances

DiagnosticsToolbox↗
NASA NTRS1988

Using AGNESS (A Generalized Network-based Expert System Shell) for matching images

The image correspondence problem is considered the most difficult step in both stereo and motion analysis. Stereo vision is useful in determining the 3-D positions of points on visible surface in a scene. Motion analysis is useful in determining the spatial and temporal relationships of objects in an environment. Besides stereo and motion analysis, there is the image correspondence problem. Most of this work is based on point or local area properties of the observed gray level values in 2-D images. A global and general approach to this problem is described by using a knowledge-based system. The knowledge it uses consists of both physical properties and spatial relationships of the edges and regions extracted from the given images. The physical component depends on features of the edge or region) in isolation. The spatial component involves the set of edges and regions adjacent to a given edge (or region) of the first image and the set of edges and regions adjacent to each potentially matching edge (or region) of the second image; thus the spatial context of each edge or region is considered. A computational network is used to represent this knowledge, it allows the computation of the likelihood of matching two edges or regions with logical and heuristic operators. An expert system shell called AGNESS (A Generalized Network-based Expert System Shell) is used to build a prototype system.

Pong, Ting-Chuen↗
NASA NTRS2005

A Review of Diagnostic Techniques for ISHM Applications

System diagnosis is an integral part of any Integrated System Health Management application. Diagnostic applications make use of system information from the design phase, such as safety and mission assurance analysis, failure modes and effects analysis, hazards analysis, functional models, fault propagation models, and testability analysis. In modern process control and equipment monitoring systems, topological and analytic , models of the nominal system, derived from design documents, are also employed for fault isolation and identification. Depending on the complexity of the monitored signals from the physical system, diagnostic applications may involve straightforward trending and feature extraction techniques to retrieve the parameters of importance from the sensor streams. They also may involve very complex analysis routines, such as signal processing, learning or classification methods to derive the parameters of importance to diagnosis. The process that is used to diagnose anomalous conditions from monitored system signals varies widely across the different approaches to system diagnosis. Rule-based expert systems, case-based reasoning systems, model-based reasoning systems, learning systems, and probabilistic reasoning systems are examples of the many diverse approaches ta diagnostic reasoning. Many engineering disciplines have specific approaches to modeling, monitoring and diagnosing anomalous conditions. Therefore, there is no "one-size-fits-all" approach to building diagnostic and health monitoring capabilities for a system. For instance, the conventional approaches to diagnosing failures in rotorcraft applications are very different from those used in communications systems. Further, online and offline automated diagnostic applications are integrated into an operations framework with flight crews, flight controllers and maintenance teams. While the emphasis of this paper is automation of health management functions, striking the correct balance between automated and human-performed tasks is a vital concern.

Patterson-Hine, Ann↗
NASA NTRSDate not supplied

Case Study: Analysis of Autonomous Center line Tracking Neural Networks

Deep neural networks have gained widespread usage in a number of applications. However, limitations such as lack of explainability and robustness inhibit building trust in their behavior, which is crucial in safety critical applications such as autonomous driving. Therefore, techniques which aid in understanding and providing guarantees for neural network behavior are the need of the hour. In this paper, we present a case study applying a recently proposed technique, Prophecy, to analyze the behavior of a neural network model, provided by our industry partner and used for autonomous guiding of airplanes on taxi runways. This regression model takes as input an image of the runway and produces two outputs, cross-track error and heading error, which represent the position of the plane relative to the center line. We use the Prophecy tool to extract neuron activation patterns for the correctness and safety properties of the model. We show the use of these patterns to identify features of the input that explain correct and incorrect behavior. We also use the patterns to provide guarantees of consistent behavior. We explore a novel idea of using sequences of images (instead of single images) to obtain good explanations and identify regions of consistent behavior.

Deep Neural Networks↗
DOE OSTI2023

Characterizing Quantum Classifier Utility in Natural Language Processing Workflows

Quantum Natural Language Processing (QNLP) develops natural language processing (NLP) models for deployment on quantum computers. We explore feature and data prototype selection techniques to address challenges posed by encoding high dimensional features. Our study builds quantum circuit classifiers that includes classical feature pre-processing, quantum embedding and quantum model training. The quantum models are built on 4 or 6 qubits and the quantum neural network (QNN) uses the established bricklayer design. We compare the dependence of model performance (in terms of accuracy and F1 scores) on feature length, embedding gates and parameterized unitary design. We compare the performance of quantum machine learning models to classical convolution neural network model (CNN) on binary and multi-class classification tasks using two datasets of synthetic features and labels. The first is the ECP-CANDLE P3B3 dataset a corpus of synthetically generated cancer pathology reports. The second dataset is extracted from well-known benchmark dataset (MADELON) - features are generated with a combination of informative, repeated and uninformative features. Both datasets are used for binary classification and multi-class classification with 3 classes. We observe robust, accurate performance from all models on the binary classification tasks, but multiclass classification is a challenge for the quantum models-there is a notable decrease in accuracy when using 3 classes. Overall the performance is comparable in terms of recall and accuracy between QNNs and CNNs, even with large datasets. These results provide a point of comparison between quantum and classical models on real-world datasets.

Hamilton, Kathleen↗
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↗
NASA NTRS1980

Solar heating, cooling, and domestic hot water system installed at Kaw Valley State Bank and Trust Company, Topeka, Kansas

The building has approximately 5600 square feet of conditioned space. Solar energy was used for space heating, space cooling, and preheating domestic hot water (DHW). The solar energy system had an array of evacuated tube-type collectors with an area of 1068 square feet. A 50/50 solution of ethylene glycol and water was the transfer medium that delivered solar energy to a tube-in-shell heat exchanger that in turn delivered solar heated water to a 1100 gallon pressurized hot water storage tank. When solar energy was insufficient to satisfy the space heating and/or cooling demand, a natural gas-fired boiler provided auxiliary energy to the fan coil loops and/or the absorption chillers. Extracts from the site files, specification references, drawings, and installation, operation and maintenance instructions are presented.

Source record↗
DOE OSTI2025

Vitamin E Acetate Causes Softening of Pulmonary Surfactant Membrane Models

The popularity of electronic cigarettes and vaping products has launched the outbreak of a condition affecting the respiratory system of users, known as electronic-cigarette/vaping-associated lung injury (EVALI). The build-up of vitamin E acetate (VEA), a diluent of some illicit vaping oils, in the bronchoalveolar lavage of patients with EVALI provided circumstantial evidence as a target for investigation. In this work, we provide a fundamental characterization of the interaction of VEA with lung cells and pulmonary surfactant (PS) models to explore the mechanisms by which vaping-related lung injuries may be present. We first confirm the localization and uptake of VEA in pulmonary epithelial cells. Further, as PS is vitally responsible for the biophysical functions of the lungs, we explore the effect of added VEA on three increasingly complex models of PS: dipalmitoylphosphatidylcholine (DPPC), a lipid-only synthetic PS, and the biologically derived extract Curosurf. Using high-resolution techniques of small-angle X-ray scattering, small-angle neutron scattering, neutron spin–echo spectroscopy, and neutron reflectometry, we compare the molecular-scale behaviors of these membranes to the bulk viscoelastic properties of surfactant monolayer films as studied by Langmuir monolayer techniques. While VEA does not obviously alter the structure or organization of PS membranes, a consistent softening of membrane systems—regardless of compositional complexity—provides a biophysical explanation for the respiratory distress associated with EVALI and yields a new perspective on the behavior of the PS system.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A dictionary learning algorithm for compression and reconstruction of streaming data in preset order

There has been an emerging interest in developing and applying dictionary learning (DL) to process massive datasets in the last decade. Many of these efforts, however, focus on employing DL to compress and extract a set of important features from data, while considering restoring the original data from this set a secondary goal. On the other hand, although several methods are able to process streaming data by updating the dictionary incrementally as new snapshots pass by, most of those algorithms are designed for the setting where the snapshots are randomly drawn from a probability distribution. In this paper, we present a new DL approach to compress and denoise massive dataset in real time, in which the data are streamed through in a preset order (instances are videos and temporal experimental data), so at any time, we can only observe a biased sample set of the whole data. Here, our approach incrementally builds up the dictionary in a relatively simple manner: if the new snapshot is adequately explained by the current dictionary, we perform a sparse coding to find its sparse representation; otherwise, we add the new snapshot to the dictionary, with a Gram-Schmidt process to maintain the orthogonality. To compress and denoise noisy datasets, we apply the denoising to the snapshot directly before sparse coding, which deviates from traditional dictionary learning approach that achieves denoising via sparse coding. Compared to full-batch matrix decomposition methods, where the whole data is kept in memory, and other mini-batch approaches, where unbiased sampling is often assumed, our approach has minimal requirement in data sampling and storage: i) each snapshot is only seen once then discarded, and ii) the snapshots are drawn in a preset order, so can be highly biased. Through experiments on climate simulations and scanning transmission electron microscopy (STEM) data, we demonstrate that the proposed approach performs competitively to those methods in data reconstruction and denoising.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Learning and Predicting Photonic Responses of Plasmonic Nanoparticle Assemblies via Dual Variational Autoencoders

In this work, the application of machine learning is demonstrated for rapid and accurate extraction of plasmonic particles cluster geometries from hyperspectral image data via a dual variational autoencoder (dual-VAE). In this approach, the information is shared between the latent spaces of two VAEs acting on the particle shape data and spectral data, respectively, but enforcing a common encoding on the shape-spectra pairs. It is shown that this approach can establish the relationship between the geometric characteristics of nanoparticles and their far-field photonic responses, demonstrating that hyperspectral darkfield microscopy can be used to accurately predict the geometry (number of particles, arrangement) of a multiparticle assemblies below the diffraction limit in an automated fashion with high fidelity (for monomers (0.96), dimers (0.86), and trimers (0.58). This approach of building structure-property relationships via shared encoding is universal and should have applications to a broader range of materials science and physics problems in imaging of both molecular and nanomaterial systems.

variational autoencoder↗
DOE OSTI2020

Manufacturing Process Design of a Micro-Scale Liquid-Liquid Extractor and Multi-phase Separator

Advances in process innovation within the chemical process industry are impeded by the cost of producing new innovative components. In this paper, various manufacturing process routes were evaluated for scaling up a micro-scale liquid-liquid extractor and multi-phase separator capable of separating immiscible phases that are used for the extraction of a chemical product into a lower boiling point solvent to make downstream distillation easier. A manufacturing process design methodology was used to evaluate both photochemical machining and binder jetting as a means for producing the micropost arrays within the separator. Cost curves show that for a 0.3 mm deep micropost array, photochemical machining was found to be much cheaper than binder jetting. However, because binder jetting processes enable microposts with over 13 times higher depth-to-width aspect ratios, binder jetting was found to decrease the overall costs of the extractor/separator by more than half for a 500 barrel per day plant, with breakeven as low as 15 barrels per day. These economics were confirmed by building extractor/separator plates and characterizing them dimensionally and functionally. The binder jet plates were found to perform better than photochemical plates leading to the complete separation of the organic phase from water. The utilization of slower sintering cycles combined with surface grinding provided acceptable dimensional tolerances for the binder jet plates. Using pressurized air during depowdering improved the economics for the binder jetting process.

42 ENGINEERING↗
DOE OSTI2021

The Baghdad Atlas: A relational database of inelastic neutron-scattering (n,n ' γ) data

A relational database has been developed based on the original (n,n'γ) work carried out by A. M. Demidov et al., at the Nuclear Research Institute in Baghdad, Iraq (Demidov et al., 1978) for 105 independent measurements comprising 76 elemental samples of natural composition and 29 isotopically-enriched samples. The information from this Atlas includes: γ-ray energies and relative intensities; nuclide and level data corresponding to the residual nucleus and meta data associated with the target sample that allows for the extraction of the flux-weighted (n,n'γ) cross sections for a given transition relative to a defined value. The optimized angular-distribution-corrected fast-neutron flux-weighted partial γ-ray cross section for the production of the 846.8-keV 21+→0gs+γ-ray transition in 56Fe, determined to be $\langle$σγ$\rangle$=143(29) mb, is used for this purpose. However, different values for the adopted cross section can be readily implemented to accommodate user preference based on revised determinations of this quantity. The Atlas (n,n'γ) data has been compiled into a series of CSV-style ASCII data sets and a suite of Python scripts have been developed to build and install the database locally. The database can then be accessed directly through the SQLite engine, or using alternative methods such as the Jupyter Notebook Python-browser interface. Several examples exploiting different interaction methodologies are distributed with the complete software package.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2022

A Global Evaluation of IMERG Precipitation Occurrence Using SMAP Detected Soil Moisture Change

A globally consistent ground validation method for remotely sensed precipitation products is crucial for building confidence in these products. This study develops a new methodology to validate the IMERG precipitation products through the use of SMAP soil moisture changes as a proxy for precipitation occurrence. Using a standard 2x2 contingency table method, preliminary results provide confidence in SMAP’s ability to be utilized as a validation tool for IMERG as results are comparable to previous validation studies. However, the method allows for an overestimate of false alarm frequency due to light precipitation events that can evaporate before the subsequent SMAP overpass and changes in overpass-to-overpass SMAP soil moisture that are within the range of SMAP uncertainty. To counter these issues, a 3x3 contingency table is used to reduce noise and extract more signal from the detection method. Through the use of this novel approach, the validation method produces a global mean POD of 0.64 and global mean FAR of 0.40, the first global-scale ground validation skill scores for the IMERG products. Advancing the method to validate precipitation quantity and the development of a real-time validation for the IMERG Early product are the crucial next developments.

Precipitation↗
DOE OSTI2024

MICROREACTOR APPLICATIONS, RESEARCH, VALIDATION, AND EVALUATION (MARVEL) REACTOR ? STATUS, CONSTRUCTION, AND TESTING

The paper presents the current status of the Microreactor Applications, Research, Validation, and Evaluation (MARVEL) microreactor design, qualification testing, fabrication, and high-level construction schedule. An overview of initial criticality, low power physics testing, and start-up testing is included, as well as an overview of the envisioned processes in which end-users can engage the project for access to operational data or specific demonstrations. Designed by the Idaho National Laboratory (INL) under the auspices of the US Department of Energy’s Microreactor Program for construction and operation at the INL, MARVEL is a small, fully functional advanced reactor with UZrH fuel and thermal output of 85 kW. It offers a unique opportunity for scaled demonstrations that can dramatically accelerate the design, licensing, and deployment of commercial microreactors for power production or process heat applications. MARVEL’s objective is to build a small liquid-metal thermal reactor at the INL to demonstrate design and operating processes for microreactors, microgrid integration, and process heat applications. MARVEL finished 90%-final-design in September 2023 and completed an independent project assessment in early 2024. Fabrication of long-lead components and fuel, safety analysis review, and procurement for construction are underway. MARVEL assembly and construction will start in 2025 and fuel loading is expected in mid- 2027. Initial criticality will be performed in a dry condition in late 2027, followed by loading of NaK coolant and start-up testing. Approximately six months later, release for unrestricted operations will enable subsequent testing of microreactor characteristics, microgrid integration and select heat extraction applications.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1988

A generic expert system for materials processing in space

A generic expert system is described for inspecting materials processed in space (MPS). The system may be applied, with the appropriate knowledge base, to any of the nondestructive testing methods (NDT) which are appropriate to MPS. Regardless of the method being used, the inspection process consists of three tasks: (1) signal or image processing of the NDT output and feature extraction, (2) interpretation of features in terms of MPS discontinuities, and (3) evaluation of the quality of the MPS based upon industry standards. In contrast to rule based systems, this system represents its knowledge as multidimensional vectors and appropriate functions on them. Currently, the expert system accepts manual input of observed features. Once the expert system has been tested and compared to human expert inspectors, a vision front-end will be developed to complete automation of the expert MPS inspection system, based on visual discontuities. Then the data base will be extended to include a variety of other NDT methods. In addition to functional performance, ease of use was established through menu window driven input as well as flexibility in building, using and modifying data bases for different applications.

Andersen, Kristinn↗
DOE OSTI2021

Qualification of 3-D Printed Mortar With Electrical Conductivity Measurements

Additive manufacturing (AM) or 3D printing of concrete allows for construction of arbitrary shape structures without a mold. Since reproducibility of 3D printed concrete is lower than that of conventional fabrication, each 3D printed structure should be monitored for proper curing. Conventional qualification of concrete is based on several tests, including destructive compressive strength measurements. Because a structure is 3-D printed layer-by-layer, the surfaces of AM concrete structures have significant surface roughness. This limits the applicability of conventional nondestructive testing methods. We investigated qualification of 3D printed mortar by monitoring curing with nondestructive electrical conductivity measurements. Bulk resistance of concrete was extracted from electrochemical impedance spectroscopy (EIS measurements made using custom flexible self-adhesive electrodes, which contour to rough surfaces. We show that bulk resistivity of concrete increases linearly with time. This allows for developing a calibration curve for compressive strength lookup from nondestructive electrical conductivity measurements. Conductivity measurements also allow for estimation of formation factor, which is an indicator of mortar permeability.

36 MATERIALS SCIENCE↗