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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Automated problem scheduling and reduction of synchronization delay effects

It is anticipated that in order to make effective use of many future high performance architectures, programs will have to exhibit at least a medium grained parallelism. A framework is presented for partitioning very sparse triangular systems of linear equations that is designed to produce favorable preformance results in a wide variety of parallel architectures. Efficient methods for solving these systems are of interest because: (1) they provide a useful model problem for use in exploring heuristics for the aggregation, mapping and scheduling of relatively fine grained computations whose data dependencies are specified by directed acrylic graphs, and (2) because such efficient methods can find direct application in the development of parallel algorithms for scientific computation. Simple expressions are derived that describe how to schedule computational work with varying degrees of granularity. The Encore Multimax was used as a hardware simulator to investigate the performance effects of using the partitioning techniques presented in shared memory architectures with varying relative synchronization costs.

Saltz, Joel H.↗
NASA NTRS1992

Hypertext and hypermedia systems in information retrieval

This paper opens with a brief history of hypertext and hypermedia in the context of information management during the 'information age.' Relevant terms are defined and the approach of the paper is explained. Linear and hypermedia information access methods are contrasted. A discussion of hyperprogramming in the handling of complex scientific and technical information follows. A selection of innovative hypermedia systems is discussed. An analysis of the Clinical Practice Library of Medicine NASA STI Program hypermedia application is presented. The paper concludes with a discussion of the NASA STI Program's future hypermedia project plans.

Kaye, K. M.↗
NASA NTRS1992

Crystal Growth Furnace - An overview of the system configuration and planned experiments on the First United States Microgravity Laboratory mission

The Crystal Growth Furnace (CGF) system configuration for the First United States Microgravity Laboratory (USML-1) mission is reviewed, and the planned on-orbit experiments are briefly described. The CGF is configured to accommodate four scientific experiments involving crystal growth which are based on the classical Bridgman method and CVT method, including vapor transport crystal growth of mercury cadmium telluride; crystal growth of mercury zinc telluride by directional solidification; seeded Bridgman growth of zinc-doped cadmium telluride; and Bridgman growth of selenium-doped gallium arsenide.

Srinivas, R.↗
NASA NTRS2004

Structural Design for a Neptune Aerocapture Mission

A multi-center study was conducted in 2003 to assess the feasibility of and technology requirements for using aerocapture to insert a scientific platform into orbit around Neptune. The aerocapture technique offers a potential method of greatly reducing orbiter mass and thus total spacecraft launch mass by minimizing the required propulsion system mass. This study involved the collaborative efforts of personnel from Langley Research Center (LaRC), Johnson Space Flight Center (JSFC), Marshall Space Flight Center (MSFC), Ames Research Center (ARC), and the Jet Propulsion Laboratory (JPL). One aspect of this effort was the structural design of the full spacecraft configuration, including the ellipsled aerocapture orbiter and the in-space solar electric propulsion (SEP) module/cruise stage. This paper will discuss the functional and structural requirements for each of these components, some of the design trades leading to the final configuration, the loading environments, and the analysis methods used to ensure structural integrity. It will also highlight the design and structural challenges faced while trying to integrate all the mission requirements. Component sizes, materials, construction methods and analytical results, including masses and natural frequencies, will be presented, showing the feasibility of the resulting design for use in a Neptune aerocapture mission. Lastly, results of a post-study structural mass optimization effort on the ellipsled will be discussed, showing potential mass savings and their influence on structural strength and stiffness

Dyke, R. Eric↗
NASA NTRS2008

Radiation Transport Tools for Space Applications: A Review

This slide presentation contains a brief discussion of nuclear transport codes widely used in the space radiation community for shielding and scientific analyses. Seven radiation transport codes that are addressed. The two general methods (i.e., Monte Carlo Method, and the Deterministic Method) are briefly reviewed.

Cosmic Ray Effects on Micro-Electronics (CREME96)↗
NASA NTRS2006

Implementing Access to Data Distributed on Many Processors

A reference architecture is defined for an object-oriented implementation of domains, arrays, and distributions written in the programming language Chapel. This technology primarily addresses domains that contain arrays that have regular index sets with the low-level implementation details being beyond the scope of this discussion. What is defined is a complete set of object-oriented operators that allows one to perform data distributions for domain arrays involving regular arithmetic index sets. What is unique is that these operators allow for the arbitrary regions of the arrays to be fragmented and distributed across multiple processors with a single point of access giving the programmer the illusion that all the elements are collocated on a single processor. Today's massively parallel High Productivity Computing Systems (HPCS) are characterized by a modular structure, with a large number of processing and memory units connected by a high-speed network. Locality of access as well as load balancing are primary concerns in these systems that are typically used for high-performance scientific computation. Data distributions address these issues by providing a range of methods for spreading large data sets across the components of a system. Over the past two decades, many languages, systems, tools, and libraries have been developed for the support of distributions. Since the performance of data parallel applications is directly influenced by the distribution strategy, users often resort to low-level programming models that allow fine-tuning of the distribution aspects affecting performance, but, at the same time, are tedious and error-prone. This technology presents a reusable design of a data-distribution framework for data parallel high-performance applications. Distributions are a means to express locality in systems composed of large numbers of processor and memory components connected by a network. Since distributions have a great effect on the performance of applications, it is important that the distribution strategy is flexible, so its behavior can change depending on the needs of the application. At the same time, high productivity concerns require that the user be shielded from error-prone, tedious details such as communication and synchronization.

James, Mark↗
NASA NTRS2012

A Comprehensive Structural Dynamic Analysis Approach for Multi Mission Earth Entry Vehicle (MMEEV) Development

The anticipated NASA Mars Sample Return Mission (MSR) requires a simple and reliable method in which to return collected Martian samples back to earth for scientific analysis. The Multi-Mission Earth Entry Vehicle (MMEEV) is NASA's proposed solution to this MSR requirement. Key aspects of the MMEEV are its reliable and passive operation, energy absorbing foam-composite structure, and modular impact sphere (IS) design. To aid in the development of an EEV design that can be modified for various missions requirements, two fully parametric finite element models were developed. The first model was developed in an explicit finite element code and was designed to evaluate the impact response of the vehicle and payload during the final stage of the vehicle's return to earth. The second model was developed in an explicit code and was designed to evaluate the static and dynamic structural response of the vehicle during launch and reentry. In contrast to most other FE models, built through a Graphical User Interface (GUI) pre-processor, the current model was developed using a coding technique that allows the analyst to quickly change nearly all aspects of the model including: geometric dimensions, material properties, load and boundary conditions, mesh properties, and analysis controls. Using the developed design tool, a full range of proposed designs can quickly be analyzed numerically and thus the design trade space for the EEV can be fully understood. An engineer can then quickly reach the best design for a specific mission and also adapt and optimize the general design for different missions.

Perino, Scott↗
NASA NTRS2013

The Geospace Dynamics Observatory; A Paradigm Changing Geospace Mission

The Geospace Dynamics Observatory (GDO) mission observes the near-Earth region in space called Geospace with unprecedented resolution, scale and sensitivity. At a distance of 60 Earth Radii (Re) in a near-polar circular orbit and a approx. 27-day period, GDO images the earth's full disk with (1) a three-channel far ultraviolet imager, (2) an extreme ultraviolet imager of the plasmasphere, and (3) a spectrometer in the near to far ultraviolet range that probes any portion of the disk and simultaneously observes the limb. The exceptional capabilities of the GDO mission include (1) unprecedented improvement in signal to noise for globalscale imaging of Earth's space environment that enable changes in the Earth's space environment to be resolved with orders of magnitude higher in temporal and spatial resolution compared to existing data and other approaches, and (2) unrivaled capability for resolving the temporal evolution, over many days, in local time or latitude with a continuous view of Earth's global-scale evolution while simultaneously capturing the changes at scales smaller than are possible with other methods. This combination of new capabilities is a proven path to major scientific advances and discoveries. The GDO mission (1) has the first full disk imagery of the density and composition variability that exist during disturbed "storm" periods and the circulation systems of the upper atmosphere, (2) is able to image the ionosphere on a global and long time scale basis, (3) is able to probe the mechanisms that control the evolution of planetary atmospheres, and (4) is able to test our understanding of how the Earth is connected to the Sun. This paper explores the optical and technical aspects of the GDO mission and the implementation strategy. Additionally, the case will be made that GDO addresses a significant portion of the priority mission science articulated in the recent Solar and Space Physics Decadal Survey.

Spann, James↗
NASA NTRS2023

Neural-Based Compression Scheme for Solar Image Data

Studying the solar system and especially the Sun relies on the data gathered daily from space missions. These missions are data-intensive and compressing this data to make them efficiently transferable to the ground station is a twofold decision to make. Stronger compression methods, by distorting the data, can increase data throughput at the cost of accuracy which could affect scientific analysis of the data. On the other hand, preserving subtle details in the compressed data requires a high amount of data to be transferred, reducing the desired gains from compression. In this work, we propose a neural network-based lossy compression method to be used in NASA’s data-intensive imagery missions. We chose NASA’s Solar Dynamics Observatory (SDO) mission which transmits 1.4 terabytes of data each day as a proof of concept for the proposed algorithm. In this work, we propose an adversarially trained neural network, equipped with local and non-local attention modules to capture both the local and global structure of the image resulting in a better trade-off in rate-distortion (RD) compared to conventional hand-engineered codecs. The RD variational autoencoder used in this work is jointly trained with a channel-dependent entropy model as a shared prior between the analysis and synthesis transforms to make the entropy coding of the latent code more effective. We also studied how optimizing perceptual losses could help our neural compressor to preserve high-frequency details of the data in the reconstructed compressed image. Our neural image compression algorithm outperforms currently-in-use and state-of-the-art codecs such as JPEG and JPEG-2000 in terms of the RD performance when compressing extreme-ultraviolet (EUV) data. As a proof of concept for use of this algorithm in SDO data analysis, we have performed coronal hole (CH) detection using our compressed images, and generated consistent segmentations, even at a compression rate of ∼ 0.1 bits per pixel (compared to 8 bits per pixel on the original data) using EUV data from SDO.

Image coding↗
NASA NTRS1992

Sonic boom acceptability studies

The determination of the magnitude of sonic boom exposure which would be acceptable to the general population requires, as a starting point, a method to assess and compare individual sonic booms. There is no consensus within the scientific and regulatory communities regarding an appropriate sonic boom assessment metric. Loudness, being a fundamental and well-understood attribute of human hearing was chosen as a means of comparing sonic booms of differing shapes and amplitudes. The figure illustrates the basic steps which yield a calculated value of loudness. Based upon the aircraft configuration and its operating conditions, the sonic boom pressure signature which reaches the ground is calculated. This pressure-time history is transformed to the frequency domain and converted into a one-third octave band spectrum. The essence of the loudness method is to account for the frequency response and integration characteristics of the auditory system. The result of the calculation procedure is a numerical description (perceived level, dB) which represents the loudness of the sonic boom waveform.

Shepherd, Kevin P.↗
NASA NTRS2018

NASA Guidelines for Promoting Scientific and Research Integrity

The National Aeronautics and Space Administration (NASA) seeks to explore and expand human knowledge of the Earth, solar system, and universe and to enable the development of aeronautical and space exploration systems. NASA's ability to achieve these purposes depends on the integrity of the research and technology activities the agency conducts and supports. The NASA workforce - as well as all external entities who review proposals for or receive NASA funding - must maintain the highest standards of scientific integrity. These responsibilities include selecting the most meritorious research activities through open and fair competition, peer review and other appropriate merit review processes, and avoidance of actual and perceived conflicts of interest; avoiding fabrication, falsification, or plagiarism in proposing, performing, or reviewing research, or in reporting research results; openly sharing results and methods not subject to classification or privacy standards; disclosing assumptions and biases in sharing and applying scientific information and data; and acting honestly and transparently in using and serving on advisory committees and in engaging in professional development activities.

Guidelines↗
DOE OSTI2020

Numerical methods for nonlocal and fractional models

Partial differential equations (PDEs) are used with huge success to model phenomena across all scientific and engineering disciplines. However, across an equally wide swath, there exist situations in which PDEs fail to adequately model observed phenomena, or are not the best available model for that purpose. On the other hand, in many situations, nonlocal models that account for interaction occurring at a distance have been shown to more faithfully and effectively model observed phenomena that involve possible singularities and other anomalies. Here, we consider a generic nonlocal model, beginning with a short review of its definition, the properties of its solution, its mathematical analysis and of specific concrete examples. We then provide extensive discussions about numerical methods, including finite element, finite difference and spectral methods, for determining approximate solutions of the nonlocal models considered. In that discussion, we pay particular attention to a special class of nonlocal models that are the most widely studied in the literature, namely those involving fractional derivatives. The article ends with brief considerations of several modelling and algorithmic extensions, which serve to show the wide applicability of nonlocal modelling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Electronic structure simulations in the cloud computing environment

The transformative impact of modern computational paradigms and technologies, such as high-performance computing, quantum computing, and cloud computing, has opened up profound new opportunities for scientific simulations. Scalable computational chemistry is one beneficiary of this technological progress. The main focus of this paper is on the performance of various quantum chemical formulations, ranging from low-order methods to high-accuracy approaches, implemented in different computational chemistry packages, such as NWChem, NWChemEx, SPEC, ExaChem, and FLOSIC codes on the Azure Quantum Element (AQE) Microsoft cloud services. We pay particular attention to the intricate workflows for performing composite chemistry simulations, associated data curation, and mechanisms for accuracy assessment, as defined by the enabling cloud Computational Chemistry as a Service (CCaaS). Our focus also extends to Arrows' automated workflow for high throughput simulations. Finally, we provide a perspective on the role of cloud computing in supporting the mission of leadership computational facilities (LCFs).

computational chemistry, electronic structure, Clo↗
NASA NTRSDate not supplied

A Novel Machine Learning Method for Surface PM2.5 Estimations from Geostationary Satellites

Particulate matter (PM) with a diameter of less or equal to 2.5 μm, known as PM , affects human health as it penetrates the respiratory system. The Environmental Protection Agency (EPA) measures the atmospheric concentration of PM using air quality monitors stationed throughout the Continental United States (CONUS). Such measurements are points on a spatial domain and therefore, might not be representative of the air quality at nearby areas considering that the composition of the atmosphere is highly variable from place to place. Satellite based AOD permits a spatially uniform means of estimating PM and new geostationary satellites provide high temporal and spatial resolution estimation of AOD. However, the concentration of PM is non-linearly dependent on other atmospheric parameters that include relative humidity, temperature, and height of the planetary boundary layer. This information may be estimated at similar spatial and temporal resolutions as AOD from numerical modeling such as from the National Oceanic and Atmospheric Administration’s (NOAA) High Resolution Rapid Refresh (HRRR) model which resolves near real-time atmospheric conditions over the CONUS. The estimation of PM concentration is a multi-parametric problem that considers the effect of temporal dependencies among the different parameters. Deep learning approaches are appropriate for such complex estimation problems as they intrinsically capture relations among multiple non-linear parameters. This study compares deep-learning methods to traditional regression analysis to demonstrate the capabilities of these methods in predicting PM2.5 concentrations. Additionally, a novel ensemble learning approach is employed to identify scientific processes that could further improve the estimation of PM concentration. Utilizing Long Short-Term Memory (LSTM) neural networks, which are suitable for multivariate time series estimation problems as they are capable of learning long-term dependencies, individual models are created for each EPA station and trained on the aforementioned dataset collocated over each station. Individual station models are merged if the model's performance is improved by reducing the root mean squared error (RMSE) metric. This ensemble training method ultimately reduces the RMSE value. Evaluation of these results provide insights into physical processes and related observable parameters that may contribute to PM concentrations. Identified parameters evaluated to be statistically different between the merged and unmerged models are expected to improve overall performance. These new parameters are then utilized for reevaluation of the deep learning methods with an extreme gradient boosting model with an RMSE of 5.5 providing the best results.

George Priftis↗
DOE OSTI2023

Progressive Tree-Based Compression of Large-Scale Particle Data

Scientific simulations and observations using particles have been creating large datasets that require effective and efficient data reduction to store, transfer, and analyze. However, current approaches either compress only small data well while being inefficient for large data, or handle large data but with insufficient compression. Toward effective and scalable compression/decompression of particle positions, we introduce new kinds of particle hierarchies and corresponding traversal orders that quickly reduce reconstruction error while being fast and low in memory footprint. Our solution to compression of large-scale particle data is a flexible block-based hierarchy that supports progressive, random-access, and error-driven decoding, where error estimation heuristics can be supplied by the user. For low-level node encoding, we introduce new schemes that effectively compress both uniform and densely structured particle distributions. Our proposed methods thus target all three phases of a tree-based particle compression pipeline, namely tree construction, tree traversal, and node encoding. In conclusion, the improved efficacy and flexibility of these methods over existing compressors are demonstrated through extensive experimentation, using a wide range of scientific particle datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Customizable adaptive regularization techniques for B-spline modeling

B-spline models are a powerful way to represent scientific data sets with a functional approximation. However, these models can suffer from spurious oscillations when the data to be approximated are not uniformly distributed. Model regularization (i.e., smoothing) has traditionally been used to minimize these oscillations; unfortunately, it is sometimes impossible to sufficiently remove unwanted artifacts without smoothing away key features of the data set. In this article, we present a method of model regularization that preserves significant features of a data set while minimizing artificial oscillations. Our method varies the strength of a smoothing parameter throughout the domain automatically, removing artifacts in poorly-constrained regions while leaving other regions unchanged. Further, the proposed method selectively incorporates regularization terms based on first and second derivatives to maintain model accuracy while minimizing numerical artifacts. The behavior of our method is validated on a collection of two- and three-dimensional data sets produced by scientific simulations. In addition, a key tuning parameter is highlighted and the effects of this parameter are presented in detail. This paper is an extension of our previous conference paper at the 2022 International Conference on Computational Science (ICCS) (Lenz et al., 2022).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Randomized Federated Learning Methods for Nonsmooth, Nonconvex, and Hierarchical Optimization (Final Technical Report)

This final technical report summarizes the outcomes of a DOE-funded project on federated scientific machine learning (FL) under nonsmooth, nonconvex, and hierarchical optimization settings. The project develops new mathematical models, algorithms, and theoretical guarantees for decentralized stochastic, bilevel, and minimax optimization problems arising in DOE mission-relevant applications. A unified framework of randomized and zeroth-order federated optimization methods is introduced, providing provable convergence, communication efficiency, and sample-complexity guarantees. The report documents algorithmic design, theoretical analysis, and empirical validation of the proposed federated learning methods. The project also contributes to workforce development through graduate training and dissemination of results via publications and seminars.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1976

Operational plans for life science payloads - From experiment selection through postflight reporting

Key features of operational plans developed in a study of the Space Shuttle era life science payloads program are presented. The data describes the overall acquisition, staging, and integration of payload elements, as well as program implementation methods and mission support requirements. Five configurations were selected as representative payloads: (a) carry-on laboratories - medical emphasis experiments, (b) mini-laboratories - medical/biology experiments, (c) seven-day dedicated laboratories - medical/biology experiments, (d) 30-day dedicated laboratories - Regenerative Life Support Evaluation (RLSE) with selected life science experiments, and (e) Biomedical Experiments Scientific Satellite (BESS) - extended duration primate (Type I) and small vertebrate (Type II) missions. The recommended operational methods described in the paper are compared to the fundamental data which has been developed in the life science Spacelab Mission Simulation (SMS) test series. Areas assessed include crew training, experiment development and integration, testing, data-dissemination, organization interfaces, and principal investigator working relationships.

Mccollum, G. W.↗