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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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Space ROS is a matured version of ROS 2 using flight software engineering practices to facilitate mission- and safety-critical applications. This roadmap was developed by the team to communicate the planned future evolution of Space ROS
Erosion of carbon surfaces due to oxidation does not occur uniformly but through the formation of localized etch pits because of active surface sites. These active sites are formed due to the presence of atomic defects on the carbon surface and have much higher reactivity compared to average non-defective sites. Thus, these active sites are the first to react during ablation, resulting in their removal. This causes all the neighboring atoms to be defective, and increasing their reactivity, thus leading to localized carbon removal around these “active” sites. In this manner, these highly reactive defects serve as nucleation sites for the formation and growth of etch pits, with detrimental effects on the structural integrity. In order to understand the influence of these etch pits on the material properties of carbon fiber microstructures, we have developed a new capability within direct simulation Monte Carlo (DSMC) to capture the etch pit formation process. This capability is developed within the DSMC code SPARTA (Stochastic PArallel Rarefied-gas Time-accurate Analyzer) and can model the material removal in the presence of active sites leading to the formation of etch pits. The focus of the current work will be to study the effect of etch pits on the material properties of FiberForm, the precursor substrate of the PICA Thermal Protection System (TPS) material. The microstructure of virgin FiberForm obtained directly from X-ray microtomography scans is used within SPARTA to obtain the ablated geometries with etch pits. These pitted microstructures are then imported into the Porous Microstructure Analysis (PuMA) software and various material properties such as thermal conductivity, elasticity, and permeability are computed. The variation of these properties because of the complex evolution of the surface topology due to the formation of etch pits is studied and analyzed. Furthermore, the effect of pitting is compared to the case of uniform radial shrinking of fibers, which has been the standard for modelling ablation of carbon structures, and significant differences are observed. Thus, a physically realistic model of material removal through the formation of etch pits will be helpful in predicting the degradation of carbon-based TPS more accurately during oxidation; as well as other mechanisms such as spallation, which involves the removal of chunks of material into the flow due to the growth of etch pits. This will ultimately improve our understanding of the failure modes in these materials due to ablation.
Erosion of carbon surfaces due to oxidation does not occur uniformly but through the formation of localized etch pits because of active surface sites. These active sites are formed due to the presence of atomic defects on the carbon surface and have much higher reactivity compared to average non-defective sites. Thus, these active sites are the first to react during ablation, resulting in their removal. This causes all the neighboring atoms to be defective, and increasing their reactivity, thus leading to localized carbon removal around these “active” sites. In this manner, these highly reactive defects serve as nucleation sites for the formation and growth of etch pits, with detrimental effects on the structural integrity. In order to understand the influence of these etch pits on the material properties of carbon fiber microstructures, we have developed a new capability within direct simulation Monte Carlo (DSMC) to capture the etch pit formation process. This capability is developed within the DSMC code SPARTA (Stochastic PArallel Rarefied-gas Time-accurate Analyzer) and can model the material removal in the presence of active sites leading to the formation of etch pits. The focus of the current work will be to study the effect of etch pits on the material properties of FiberForm, the precursor substrate of the PICA Thermal Protection System (TPS) material. The microstructure of virgin FiberForm obtained directly from X-ray microtomography scans is used within SPARTA to obtain the ablated geometries with etch pits. These pitted microstructures are then imported into the Porous Microstructure Analysis (PuMA) software and various material properties such as thermal conductivity, elasticity, and permeability are computed. The variation of these properties because of the complex evolution of the surface topology due to the formation of etch pits is studied and analyzed. Furthermore, the effect of pitting is compared to the case of uniform radial shrinking of fibers, which has been the standard for modelling ablation of carbon structures, and significant differences are observed. Thus, a physically realistic model of material removal through the formation of etch pits will be helpful in predicting the degradation of carbon-based TPS more accurately during oxidation; as well as other mechanisms such as spallation, which involves the removal of chunks of material into the flow due to the growth of etch pits. This will ultimately improve our understanding of the failure modes in these materials due to ablation.
The Liquid Argon Time Projection Chamber (LArTPC) technology is widely used in high energy physics experiments, including the upcoming Deep Underground Neutrino Experiment (DUNE). Accurately simulating LArTPC detector responses is essential for analysis algorithm development and physics model interpretations. Accurate LArTPC detector response simulations are computationally demanding, and can become a bottleneck in the analysis workflow. Compute devices such as General-Purpose Graphics Processing Units (GPGPUs) have the potential to substantially accelerate simulations compared to traditional CPU-only processing. The software development that requires often carries the cost of specialized code refactorization and porting to match the target hardware architecture. With the rapid evolution and increased diversity of the computer architecture landscape, it is highly desirable to have a portable solution that also maintains reasonable performance. We report our ongoing effort in evaluating Kokkos as a basis for this portable programming model using LArTPC simulations in the context of the Wire-Cell Toolkit, a C++ library for LArTPC simulations, data analysis, reconstruction and visualization.
Voids are expansive regions in the universe containing significantly fewer galaxies than surrounding galaxy clusters and filaments. They are a fundamental feature of the cosmic web and provide important information about galaxy physics and cosmology. For example, correlations between voids and luminous tracers of large-scale structure improve constraints on the expansion of the universe as compared to using tracers alone, and numerous studies have shown that the void environment influences the evolution of galaxies. However, what constitutes a void is vague and formulating a concrete definition to use in a void-finding algorithm is not trivial. As a result, several different algorithms exist to identify these cosmic underdensities. Our Void Analysis Software Toolkit, or VAST, provides Python 3 implementations of two such algorithms: VoidFinder and V 2 . This consolidation of two popular void-finding algorithms allows the user to, for example, easily compare the results of their analysis using different void definitions.
Accurate prediction of metallurgical phase transformations is an essential basis for autonomous optimization and rapid part qualification. Several methods can be used to estimate the evolution of phase fractions such as JMAK kinetics-based models, phase-field models, thermodynamic models, and data-driven machine learning models. Thermodynamic and phase-field-based methodologies solve multiphysics equations requiring numerous calibration parameters and significant computational resources. As a result, the computation domain is limited to a point or on order of micron-meters. The data-driven models rely on large datasets from experiments and simulations. While the JMAK model only provides information about phase fraction evolution, it can predict this evolution in near real-time using thermal history and thermodynamic data without restriction on the domain. JMAK models have been popularly used by researchers to model phase transformations occuring during additive manufacturing or over arbitrary temperature profiles. Commercial proprietary software such as Abaqus and Ansys or closed-source in-house implementations offer the ability to model JMAK based kinetics to predict phase transformation. However, these software packages are not open-source or freely available for use and development in conjunction with manufacturing machines, sensors, and machine learning algorithms. In addition, the use of the model is restricted by a license token. In contrast, given temperature profiles at multiple points in the domain, this Python-based PyJMAK model can compute phase evolution in parallel due to its stand-alone modular, voxel-based structure, and it can be executed on high-performance computing resources without any license restrictions.
Over the last three decades, there has been a significant evolution in storage technologies supporting archival of remote sensing data. This section provides a brief survey of how these technologies have evolved. Three main technologies are considered - tape, hard disk and solid state disk. Their historical evolution is traced, summarizing how reductions in cost have helped being able to store larger volumes of data on faster media. The cost per GB of media is only one of the considerations in determining the best approach to archival storage. Active archives generally require faster response to user requests for data than permanent archives. The archive costs have to consider facilities and other capital costs, operations costs, software licenses, utilities costs, etc. For meeting requirements in any organization, typically a mix of technologies is needed.
Over the last few years, the field of protein structure prediction has been transformed by increasingly-accurate contact prediction software. These methods are based on the detection of coevolutionary relationships between residues from multiple sequence alignments. However, despite speculation, there is little evidence of a link between contact prediction and the physico-chemical interactions which drive amino-acid coevolution. Furthermore, existing protocols predict only a fraction of all protein contacts and it is not clear why some contacts are favoured over others. Using a dataset of 863 protein domains, we assessed the physico-chemical interactions of contacts predicted by CCMpred, MetaPSICOV, and DNCON2, as examples of direct coupling analysis, meta-prediction, and deep learning. We considered correctly-predicted contacts and compared their properties against the protein contacts that were not predicted. Predicted contacts tend to form more bonds than non-predicted contacts, which suggests these contacts may be more important than contacts that were not predicted. Comparing the contacts predicted by each method, we found that metaPSICOV and DNCON2 favour accuracy whereas CCMPred detects contacts with more bonds. This suggests that the push for higher accuracy may lead to a loss of physico-chemically important contacts. These results underscore the connection between protein physico-chemistry and the coevolutionary couplings that can be derived from multiple sequence alignments. This relationship is likely to be relevant to protein structure prediction and functional analysis of protein structure and may be key to understanding their utility for different problems in structural biology. We use publicly-available databases. Our code is available for download at http://opig.stats.ox.ac.uk/.
Here, we introduce a novel open-source software package QForte, a comprehensive development tool for new quantum simulation algorithms. QForte incorporates functionality for handling molecular Hamiltonians, fermionic encoding, ansatz construction, time evolution, and state-vector emulation, requiring only a classical electronic structure package as a dependency. QForte also contains black-box implementations of a wide variety of quantum algorithms, including variational and projective quantum eigensolvers, adaptive eigensolvers, quantum imaginary time evolution, and quantum Krylov methods. We highlight two features of QForte: (i) how the Python class structure of QForte enables the facile implementation of new algorithms, and (ii) how existing algorithms can be executed in just a few lines of code.
The structural biology program at the National Synchrotron Light Source II presents a coordinated set of instruments, software and research opportunities for the interested user. We describe in some detail the research capabilities enabled by the Center for BioMolecular Structure. The evolution of the resources is described in detail, considering three major themes: automation, micro-focusing and computation prediction.
The Jefferson Lab KL experiment will run at the Continuous Electron Beam Accelerator Facility with a much lower bunch repetition rate (7.80 or 15.59 MHz) than nominally used (249.5 or 499 MHz). While the proposed average current of 2.5 - 5.0 muA is relatively low compared to the maximum CEBAF current of approximately 180 muA, the corresponding bunch charge is atypically high for CEBAF injector operation. In this work, we investigated the evolution and transmission of low-rep-rate, high-bunch-charge (0.32 to 0.64 pC) beams through the CEBAF injector. Using the commercial software General Particle Tracer, we have simulated and analyzed the beam characteristics for both values of bunch charge. We performed these simulations with the existing injector using a 130 kV gun voltage. We have calculated and measured the transmission as a function of the photocathode laser spot size and pulse length. We report on the findings of these simulations and optimum parameters for operating the experiment.
The goal of this project was to develop a tool for facilitating simulation, validation and discovery of multiscale dynamical processes in microbial ecosystems. This led to the development of an open-source software platform for Computation Of Microbial Ecosystems in Time and Space (COMETS). COMETS performs spatially distributed time-dependent flux balance based simulations of microbial metabolism. Our plan involved building the software platform itself, calibrating and testing it through comparison with experimental data, and integrating simulations and experiments to address important open questions on the evolution and dynamics of cross-feeding interactions between microbial species.
Information is given in viewgraph form on the Space Station Freedom (SSF) Thermal Control System Automation Project (TCSAP). Topics covered include the assembly of the External Thermal Control System (ETCS); the ETCS functional schematic; the baseline Fault Detection, Isolation, and Recovery (FDIR), including the development of a knowledge based system (KBS) for application of rule based reasoning to the SSF ETCS; TCSAP software architecture; the High Fidelity Simulator architecture; the TCSAP Runtime Object Database (RODB) data flow; KBS functional architecture and logic flow; TCSAP growth and evolution; and TCSAP relationships.
The Office of Space Communications (OSC) is tasked by NASA to conduct a planning process to meet NASA's science mission and other communications and data processing requirements. A set of technology trend studies was undertaken by Science Applications International Corporation (SAIC) for OSC to identify quantitative data that can be used to predict performance of electronic equipment in the future to assist in the planning process. Only commercially available, off-the-shelf technology was included. For each technology area considered, the current state of the technology is discussed, future applications that could benefit from use of the technology are identified, and likely future developments of the technology are described. The impact of each technology area on NASA operations is presented together with a discussion of the feasibility and risk associated with its development. An approximate timeline is given for the next 15 to 25 years to indicate the anticipated evolution of capabilities within each of the technology areas considered. This volume contains four chapters: one each on technology trends for database systems, computer software, neural and fuzzy systems, and artificial intelligence. The principal study results are summarized at the beginning of each chapter.
SAND2023-06684O GSAS Tools is a web application that manages user access to modeling and simulation tools and promotional material. This software, which is a spiking neural network (SNN) simulator, represents an SNN as a graph of stochastic differential equations and simulates the time-evolution of these equations. It has the capability of reading inputs from file and writing outputs to file, and generally supports experimentation, analysis, and algorithm development using SNNs. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
Since the beginning of the Space Station Freedom Program (SSFP), the Lewis Research Center (LeRC) and the Rocketdyne Division of Rockwell International have had extensive efforts underway to develop test beds to support the definition of the detailed electrical power system design. Because of the extensive redirections that have taken place in the Space Station Freedom Program in the past several years, the test bed effort was forced to accommodate a large number of changes. A short history of these program changes and their impact on the LeRC test beds is presented to understand how the current test bed configuration has evolved. The current test objectives and the development approach for the current DC Test Bed are discussed. A description of the test bed configuration, along with its power and controller hardware and its software components, is presented. Next, the uses of the test bed during the mature design and verification phase of SSFP are examined. Finally, the uses of the test bed in operation and evolution of the SSF are addressed.
In today's changing satellite mission operations landscape, the need for mission automation and interoperability while containing costs is more important than ever. Enter GMSEC – a flexible and extendible set of software automation tools designed to automate repetitive processes and off-hours alerting, among others. GMSEC is also an API that is open source and supports the OMG C2MS message specification, allowing dissimilar components to communicate with each other. Attendees will learn about the possible use cases for automation that the GMSEC architecture and suite of components enable. The presentation will provide an overview of specific GMSEC software products that provide these automation features such as the GMSEC Generic Extendible Message Utility (GEMU) that enables automation, and GMSEC Services Suite OpenMCT (GSS) that offers enhanced telemetry visualization. This presentation will outline how GMSEC can enable and enhance mission operations automation, enabling efficiencies that can help bring down costs.
Since the beginning of the Space Station Freedom Program (SSFP), the NASA Lewis Research Center (LeRC) and the Rocketdyne Division of Rockwell International have had extensive efforts underway to develop testbeds to support the definition of the detailed electrical power system design. Because of the extensive redirections that have taken place in the Space Station Freedom Program in the past several years, the test bed effort was forced to accommodate a large number of changes. A short history of these program changes and their impact on the LeRC test beds is presented to understand how the current test bed configuration has evolved. The current test objectives and the development approach for the current DC test bed are discussed. A description of the test bed configuration, along with its power and controller hardware and its software components, is presented. Next, the uses of the test bed during the mature design and verification phase of SSFP are examined. Finally, the uses of the test bed in the operation and evolution of the SSF are addressed.