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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Integrated Space Asset Management Database and Modeling

Effective Space Asset Management is one key to addressing the ever-growing issue of space congestion. It is imperative that agencies around the world have access to data regarding the numerous active assets and pieces of space junk currently tracked in orbit around the Earth. At the center of this issues is the effective management of data of many types related to orbiting objects. As the population of tracked objects grows, so too should the data management structure used to catalog technical specifications, orbital information, and metadata related to those populations. Marshall Space Flight Center's Space Asset Management Database (SAM-D) was implemented in order to effectively catalog a broad set of data related to known objects in space by ingesting information from a variety of database and processing that data into useful technical information. Using the universal NORAD number as a unique identifier, the SAM-D processes two-line element data into orbital characteristics and cross-references this technical data with metadata related to functional status, country of ownership, and application category. The SAM-D began as an Excel spreadsheet and was later upgraded to an Access database. While SAM-D performs its task very well, it is limited by its current platform and is not available outside of the local user base. Further, while modeling and simulation can be powerful tools to exploit the information contained in SAM-D, the current system does not allow proper integration options for combining the data with both legacy and new M&S tools. This paper provides a summary of SAM-D development efforts to date and outlines a proposed data management infrastructure that extends SAM-D to support the larger data sets to be generated. A service-oriented architecture model using an information sharing platform named SIMON will allow it to easily expand to incorporate new capabilities, including advanced analytics, M&S tools, fusion techniques and user interface for visualizations. In addition, tight control of information sharing policy will increase confidence in the system, which would encourage industry partners to provide commercial data. Combined with the integration of new and legacy M&S tools, a SIMON-based architecture will provide a robust environment that can be extended and expanded indefinitely.

MacLeod, Todd↗
NASA NTRS1983

Space station needs, attributes and architectural options. Volume 1: Executive summary NASA

The uses alignment plan was implemented. The existing data bank was used to define a large number of station requirements. Ten to 20 valid mission scenarios were developed. Architectural options as they are influenced by communications operations, subsystem evolvability, and required technology growth are defined. Costing of evolutionary concepts, alternative approaches, and options, was based on minimum design details.

Source record↗
NASA NTRS1986

Ada and knowledge-based systems: A prototype combining the best of both worlds

A software architecture is described which facilitates the construction of distributed expert systems using Ada and selected knowledge based systems. This architecture was utilized in the development of a Knowledge-based Maintenance Expert System (KNOMES) prototype for the Space Station Mobile Service Center (MSC). The KNOMES prototype monitors a simulated data stream from MSC sensors and built-in test equipment. It detects anomalies in the data and performs diagnosis to determine the cause. The software architecture which supports the KNOMES prototype allows for the monitoring and diagnosis tasks to be performed concurrently. The basic concept of this software architecture is named ACTOR (Ada Cognitive Task ORganization Scheme). An individual ACTOR is a modular software unit which contains both standard data processing and artificial intelligence components. A generic ACTOR module contains Ada packages for communicating with other ACTORs and accessing various data sources. The knowledge based component of an ACTOR determines the role it will play in a system. In this prototype, an ACTOR will monitor the MSC data stream.

Brauer, David C.↗
DOE OSTI2018

PIMS: Memristor-Based Processing-in-Memory-and-Storage.

Continued progress in computing has augmented the quest for higher performance with a new quest for higher energy efficiency. This has led to the re-emergence of Processing-In-Memory (PIM) ar- chitectures that offer higher density and performance with some boost in energy efficiency. Past PIM work either integrated a standard CPU with a conventional DRAM to improve the CPU- memory link, or used a bit-level processor with Single Instruction Multiple Data (SIMD) control, but neither matched the energy consumption of the memory to the computation. We originally proposed to develop a new architecture derived from PIM that more effectively addressed energy efficiency for high performance scientific, data analytics, and neuromorphic applications. We also originally planned to implement a von Neumann architecture with arithmetic/logic units (ALUs) that matched the power consumption of an advanced storage array to maximize energy efficiency. Implementing this architecture in storage was our original idea, since by augmenting storage (in- stead of memory), the system could address both in-memory computation and applications that accessed larger data sets directly from storage, hence Processing-in-Memory-and-Storage (PIMS). However, as our research matured, we discovered several things that changed our original direc- tion, the most important being that a PIM that implements a standard von Neumann-type archi- tecture results in significant energy efficiency improvement, but only about a O(10) performance improvement. In addition to this, the emergence of new memory technologies moved us to propos- ing a non-von Neumann architecture, called Superstrider, implemented not in storage, but in a new DRAM technology called High Bandwidth Memory (HBM). HBM is a stacked DRAM tech- nology that includes a logic layer where an architecture such as Superstrider could potentially be implemented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Investigation of Portable Event-Based Monte Carlo Transport Using the NVIDIA Thrust Library

Power consumption considerations are driving future high performance computing platforms toward many-core computing architectures. Los Alamos National Laboratory's Trinity machine, available in 2016, will use both Intel Xeon Haswell processors and Intel Xeon Phi Knights Landing many integrated core (MIC) architecture coprocessors. Lawrence Livermore National Laboratory's Sierra machine, available in 2018, will use an IBM PowerPC architecture along with Nvidia graphics processing unit (GPU) architecture accelerators. These different advanced architectures make the computing landscape in upcoming years complex. Traditional approaches to Monte Carlo transport do not work efficiently on these new computing platforms. MIC architectures require vectorization to operate efficiently, and vectorization is difficult to achieve in Monte Carlo transport. GPU architectures require additional code to explicitly use the hardware, requiring significant code changes or hardware specific branches in the source code. A significant challenge for Monte Carlo transport projects is to simultaneously support within a single source code base efficient simulations for both the current generation of architectures and the different advanced computing architectures. In order to address these challenges, two important changes are typically required: a new algorithmic approach for solving Monte Carlo transport, and explicit use of hardware specific software. In this paper, we describe initial research investigations of an event-based Monte Carlo transport algorithm implemented using the Nvidia Thrust library on a GPU for a Monte Carlo test code. The event-based algorithm targets many-core architectures by increasing SIMD (single instruction multiple data) parallelism, while Thrust potentially provides portable performance by allowing one source code base to compile code targeted for both CPUs and GPUs. We described preliminary investigations of portable event-based Monte Carlo algorithms implemented using the Nvidia Thrust library in a research Monte Carlo test code. We found that an explicit CUDA implementation of an event-based Monte Carlo algorithm performed significantly more efficiently than a Thrust implementation on GPU platforms, most likely as a result of additional flexibility in access to different memory spaces on the GPU. Additionally, we showed that on GPU platforms and at large enough problem sizes the event-based implementations perform more efficiently than the serial history-based implementation running on the host CPU. While investigating this problem, we also discovered that the performance of the event-based algorithm is affected by what tallies are being used. A zonal scalar flux tally requires atomic operations that significantly impacted the performance of the code, in some cases producing slowdowns instead of speedups. We decided to remove the tally in order to focus on the effectiveness of the event-based algorithm. Future work will be required to research more effective ways of handling such tallies. Additionally, we would like to consider new ways for optimizing both the Thrust and CUDA versions, in order to see how much performance we have yet to achieve. The potential trade-off between portability and performance was demonstrated in this investigation. Thrust provides both CPU and GPU versions of the code in one code base, but it does so at a cost. We discovered approximately a factor of 2-5 performance difference between Thrust and CUDA on each GPU platform we tested. Thrust provides a tool to access the GPUs with less effort and specialization, but it does so by giving up fine grained control where extra performance can be found for this application. Future work will be required to determine whether this performance differential between Thrust and CUDA can be reduced for event-based Monte Carlo transport. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2021

NASA's Surface Deformation and Change Mission Study

The National Academies of Science, Engineering and Medicine 2017 Decadal Survey of Earth Science and Applications identified geodetic measurements of surface deformation and related change as one of the top five "observables" to be prioritized in NASA's program going forward. In response, NASA commissioned a multi-center Surface Deformation and Change (SDC) team to perform a five year study of mission architectures that would support SDC observables and provide the most value to the diverse science and applications communities it serves. The study is being conducted in phases, in which the science and applications capabilities identified in the Decadal Survey are interpreted and refined, candidate mission architectures and associated technologies to support these needs are identified, architectures are assessed against a science value framework specific to SDC, and recommendations to NASA are made. Ultimately, NASA will decide which amongst these recommendations will proceed to mission formulation. As synthetic aperture radar (SAR) was identified as the prime sensor technology to satisfy SDC observational needs, a key component of the SDC study is to assess the current state of the art in SAR sensor and spacecraft systems and components. The number of SAR systems, both civil and commercial, is growing rapidly, requiring that mission architectures not only consider technology, but availability of data from other missions, possible partnerships or collaborations, and even data purchase. The mechanism for assessment involves development of an end-to-end science performance evaluation tool for multi-satellite constellations, which feeds into a science value framework that considers science performance, technological programmatic risks, and cost. This paper will present an overview of the ongoing study including the architectures under consideration and the technology road map needed to achieve the objectives of the mission.

Rosen, Paul↗
DOE OSTI2022

The dark energy survey 5-yr photometrically identified type Ia supernovae

ABSTRACT As part of the cosmology analysis using Type Ia Supernovae (SN Ia) in the Dark Energy Survey (DES), we present photometrically identified SN Ia samples using multiband light curves and host galaxy redshifts. For this analysis, we use the photometric classification framework SuperNNovatrained on realistic DES-like simulations. For reliable classification, we process the DES SN programme (DES-SN) data and introduce improvements to the classifier architecture, obtaining classification accuracies of more than 98 per cent on simulations. This is the first SN classification to make use of ensemble methods, resulting in more robust samples. Using photometry, host galaxy redshifts, and a classification probability requirement, we identify 1863 SNe Ia from which we select 1484 cosmology-grade SNe Ia spanning the redshift range of 0.07 < z < 1.14. We find good agreement between the light-curve properties of the photometrically selected sample and simulations. Additionally, we create similar SN Ia samples using two types of Bayesian Neural Network classifiers that provide uncertainties on the classification probabilities. We test the feasibility of using these uncertainties as indicators for out-of-distribution candidates and model confidence. Finally, we discuss the implications of photometric samples and classification methods for future surveys such as Vera C. Rubin Observatory Legacy Survey of Space and Time.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Addressing Global Data Dependencies in Heterogeneous Asynchronous Runtime Systems on GPUs

Large-scale parallel applications with complex global data dependencies beyond those of reductions pose significant scalability challenges in an asynchronous runtime system. Internodal challenges include identifying the all-to-all communication of data dependencies among the nodes. Intranodal challenges include gathering together these data dependencies into usable data objects while avoiding data duplication. This paper addresses these challenges within the context of a large-scale, industrial coal boiler simulation using the Uintah asynchronous many-task runtime system on GPU architectures. We show significant reduction in time spent analyzing data dependencies through refinements in our dependency search algorithm. Multiple task graphs are used to eliminate subsequent analysis when task graphs change in predictable and repeatable ways. Using a combined data store and task scheduler redesign reduces data dependency duplication ensuring that problems fit within host and GPU memory. Furthermore, these modifications did not require any changes to application code or sweeping changes to the Uintah runtime system. We report results running on the DOE Titan system on 119K CPU cores and 7.5K GPUs simultaneously. Our solutions can be generalized to other task dependency problems with global dependencies among thousands of nodes which must be processed efficiently at large scale.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2001

A Space Based Internet Protocol System for Launch Vehicle Tracking and Control

Personnel from the Goddard Space Flight Center Wallops Flight Facility (GSFC/WFF) in Virginia are responsible for the overall management of the NASA Sounding Rocket and Scientific Balloon Programs. Payloads are generally in support of NASA's Space Science Enterprise's missions and return a variety of scientific data as well as providing a reasonably economical means of conducting engineering tests for instruments and devices used on satellites and other spacecraft. Sounding rockets used by NASA can carry payloads of various weights to altitudes from 50 km to more than 1,300 km. Scientific balloons can carry a payload weighing as much as 3,630 Kg to an altitude of 42 km. Launch activities for both are conducted not only from established ranges, but also from remote locations worldwide requiring mobile tracking and command equipment to be transported and set up at considerable expense. The advent of low earth orbit (LEO) commercial communications satellites provides an opportunity to dramatically reduce tracking and control costs of these launch vehicles and Unpiloted Aerial Vehicles (UAVs) by reducing or eliminating this ground infrastructure. Additionally, since data transmission is by packetized Internet Protocol (IP), data can be received and commands initiated from practically any location. A low cost Commercial Off The Shelf (COTS) system is currently under development for sounding rockets that also has application to UAVs and scientific balloons. Due to relatively low data rate (9600 baud) currently available, the system will first be used to provide GPS data for tracking and vehicle recovery. Range safety requirements for launch vehicles usually stipulate at least two independent tracking sources. Most sounding rockets flown by NASA now carry GP~ receivers that output position data via the payload telemetry system to the ground station. The Flight Modem can be configured as a completely separate link thereby eliminating the requirement for tracking radar. The system architecture that integrates antennas, GPS receiver, commercial satellite packet data modem, and a single board computer with custom software is described along with the technical challenges and the plan for their resolution. These include antenna development, high Doppler rates, reliability, environmental ruggedness, hand over between satellites, and data security. An aggressive test plan is included which, in addition to environmental testing, measures bit error rate, latency and antenna patterns. Actual launches on a sounding rocket and various aircraft flights have taken place. Flight tests are planned for the near future on aircraft, long duration balloons and sounding rockets. These results, as well as the current status of the project, are reported.

Bull, Barton↗
DOE OSTI2021

Enabling discovery data science through cross-facility workflows

Experimental and observational instruments for scientific research (such as light sources, genome sequencers, accelerators, telescopes and electron microscopes) increasingly require High Performance Computing (HPC) scale capabilities for data analysis and workflow processing. Next-generation instruments are being deployed with higher resolutions and faster data capture rates, creating a big data crunch that cannot be handled by modest institutional computing resources. Often these big data analysis pipelines also require near real-time computing and have higher resilience requirements than the simulation and modeling workloads more traditionally seen at HPC centers. While some facilities have enabled workflows to run at a single HPC facility, there is a growing need to integrate capabilities across HPC facilities to enable cross-facility workflows, either to provide resilience to an experiment, increase analysis throughput capabilities, or to better match a workflow to a particular architecture. In this paper we describe the barriers to executing complex data analysis workflows across HPC facilities and propose an architectural design pattern for enabling scientific discovery using cross-facility workflows that includes orchestration services, application programming interfaces (APIs), data access and co-scheduling.

Antypas, Katerina B.↗
DOE OSTI2019

Extracting SIMD Parallelism from Recursive Task-Parallel Programs

The pursuit of computational efficiency has led to the proliferation of throughput-oriented hardware, from GPUs to increasingly wide vector units on commodity processors and accelerators. This hardware is designed to efficiently execute data-parallel computations in a vectorized manner. However, many algorithms are more naturally expressed as divide-and-conquer, recursive, task-parallel computations. In the absence of data parallelism, it seems that such algorithms are not well suited to throughput-oriented architectures. This paper presents a set of novel code transformations that expose the data parallelism latent in recursive, task- parallel programs. These transformations facilitate straightforward vectorization of task-parallel programs on commodity hardware. We also present scheduling policies that maintain high utilization of vector resources while limiting space usage. Across several task-parallel benchmarks, we demonstrate both efficient vector resource utilization and substantial speedup on chips using Intel’s SSE4.2 vector units, as well as accelerators using Intel’s AVX512 units. We then show through rigorous sampling that, in practice, our vectorization techniques are effective for a much larger class of programs.

Recursive Programs, task parallelism, vectorizatio↗
NASA NTRS2000

Shuttle Radar Topography Mission (SRTM) Flight System Design and Operations Overview

This paper provides an overview of the Shuttle Radar Topography Mission (SRTM), with emphasis on flight system implementation and mission operations from systems engineering perspective. Successfully flown in February, 2000, the SRTM's primary payload consists of several subsystems to form the first spaceborne dual-frequency (C-band and X-band) fixed baseline interferometric synthetic aperture radar (InSAR) system, with the mission objective to acquire data sets over 80% of Earth's landmass for height reconstruction. The paper provides system architecture, unique design features, engineering budgets, design verification, in-flight checkout and data acquisition of the SRTM payload, in particular for the C-band system. Mission operation and post-mission data processing activities are also presented. The complexity of the SRTM as a system, the ambitious mission objective, the demanding requirements and the high interdependency between multi-disciplined subsystems posed many challenges. The engineering experience and the insight thus gained have important implications for future spaceborne interferometric SAR mission design and implementation.

Shen, Yuhsyen↗
NASA NTRS2015

Operational Concepts for a Generic Space Exploration Communication Network Architecture

This document is one of three. It describes the Operational Concept (OpsCon) for a generic space exploration communication architecture. The purpose of this particular document is to identify communication flows and data types. Two other documents accompany this document, a security policy profile and a communication architecture document. The operational concepts should be read first followed by the security policy profile and then the architecture document. The overall goal is to design a generic space exploration communication network architecture that is affordable, deployable, maintainable, securable, evolvable, reliable, and adaptable. The architecture should also require limited reconfiguration throughout system development and deployment. System deployment includes: subsystem development in a factory setting, system integration in a laboratory setting, launch preparation, launch, and deployment and operation in space.

networking↗
DOE OSTI2020

In-Situ Inference: Bringing Advanced Data Science Into Exascale Simulations

As simulations generate ever-increasing amounts of data, there are correspondingly richer opportunities for analysis and scientific discovery - discoveries that will be missed if most of the data must be discarded before it is analyzed. Because future exascale architectures will be increasingly storage-limited, it will not be possible to save the vast majority of simulation data for later analysis, requiring analysis to occur “in-situ” within the simulation. However, existing in-situ data analysis frameworks provide little or no support for one of the most sophisticated forms of data science: probabilistic statistical modeling or uncertainty quantification (UQ), and the accompanying challenge of inference - fitting those statistical models to massive simulation output. Our goal is to develop the fundamental statistical algorithms and computer science needed to perform statistical inference in-situ (in HPC simulations) to the full stream of data those simulations generate.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1996

Long-term archiving and data access: modelling and standardization

This paper reports on the multiple difficulties inherent in the long-term archiving of digital data, and in particular on the different possible causes of definitive data loss. It defines the basic principles which must be respected when creating long-term archives. Such principles concern both the archival systems and the data. The archival systems should have two primary qualities: independence of architecture with respect to technological evolution, and generic-ness, i.e., the capability of ensuring identical service for heterogeneous data. These characteristics are implicit in the Reference Model for Archival Services, currently being designed within an ISO-CCSDS framework. A system prototype has been developed at the French Space Agency (CNES) in conformance with these principles, and its main characteristics will be discussed in this paper. Moreover, the data archived should be capable of abstract representation regardless of the technology used, and should, to the extent that it is possible, be organized, structured and described with the help of existing standards. The immediate advantage of standardization is illustrated by several concrete examples. Both the positive facets and the limitations of this approach are analyzed. The advantages of developing an object-oriented data model within this contxt are then examined.

Hoc, Claude↗
DOE OSTI2020

Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations

We developed a new class of physics-informed generative adversarial networks (PI-GANs) to solve forward, inverse, and mixed stochastic problems in a unified manner based on a limited number of scattered measurements. Unlike standard GANs relying solely on data for training, here we encoded into the architecture of GANs the governing physical laws in the form of stochastic differential equations (SDEs) using automatic differentiation. In particular, we applied Wasserstein GANs with gradient penalty (WGAN-GP) for its enhanced stability compared to vanilla GANs. We first tested WGAN-GP in approximating Gaussian processes of different correlation lengths based on data realizations collected from simultaneous reads at sparsely placed sensors. We obtained good approximation of the generated stochastic processes to the target ones even if there is a mismatch between the input noise dimensionality and the effective dimensionality of the target stochastic processes. We also studied the overfitting issue for both the discriminator and the generator, and we found that overfitting occurs also in the generator in addition to the discriminator as previously reported. Subsequently, we considered the solution of elliptic SDEs requiring approximations of three stochastic processes, namely the solution, the forcing, and the diffusion coefficient. Here again, we assumed data collected from simultaneous reads at a limited number of sensors for the multiple stochastic processes. Furthermore, three generators were used for the PI-GANs: two of them were feed forward deep neural networks (DNNs), while the other one was the neural network induced by the SDE. For the case where we have one group of data, we employed one feed forward DNN as the discriminator, while for the case of multiple groups of data we employed multiple discriminators in PI-GANs. We solved forward, inverse, and mixed problems without changing the framework of PI-GANs, obtaining both the means and the standard deviations of the stochastic solution and the diffusion coefficient in good agreement with benchmarks. In this work, we have demonstrated the effectiveness of PI-GANs in solving SDEs for about 120 dimensions. In principle, PI-GANs could tackle very high dimensional problems given more sensor data with low-polynomial growth in computational cost.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2012

Security Data Warehouse Application

The Security Data Warehouse (SDW) is used to aggregate and correlate all JSC IT security data. This includes IT asset inventory such as operating systems and patch levels, users, user logins, remote access dial-in and VPN, and vulnerability tracking and reporting. The correlation of this data allows for an integrated understanding of current security issues and systems by providing this data in a format that associates it to an individual host. The cornerstone of the SDW is its unique host-mapping algorithm that has undergone extensive field tests, and provides a high degree of accuracy. The algorithm comprises two parts. The first part employs fuzzy logic to derive a best-guess host assignment using incomplete sensor data. The second part is logic to identify and correct errors in the database, based on subsequent, more complete data. Host records are automatically split or merged, as appropriate. The process had to be refined and thoroughly tested before the SDW deployment was feasible. Complexity was increased by adding the dimension of time. The SDW correlates all data with its relationship to time. This lends support to forensic investigations, audits, and overall situational awareness. Another important feature of the SDW architecture is that all of the underlying complexities of the data model and host-mapping algorithm are encapsulated in an easy-to-use and understandable Perl language Application Programming Interface (API). This allows the SDW to be quickly augmented with additional sensors using minimal coding and testing. It also supports rapid generation of ad hoc reports and integration with other information systems.

Vernon, Lynn R.↗
DOE OSTI2021

Implementing a neural network interatomic model with performance portability for emerging exascale architectures

The two main thrusts of computational science are increasingly accurate predictions and faster calculations; to this end, the zeitgeist in molecular dynamics (MD) simulations is pursuing machine learned and data driven interatomic models, e.g. neural network potentials, and novel hardware architectures, e.g. GPUs. Current implementations of neural network potentials are orders of magnitude slower than traditional interatomic models and while looming exascale computing offers the ability to run large, accurate simulations with these models, achieving portable performance for MD with new and varied exascale hardware requires rethinking traditional algorithms, using novel data structures, and library solutions. We re-implement a neural network interatomic model in CabanaMD, an MD proxy application, built on libraries developed for performance portability. Our implementation shows significantly improved thread scaling in this complex kernel as compared to a current LAMMPS implementation, across both strong and weak scaling. Our single-source solution enables simulations up to 20 million atoms on a single CPU node and 4 million atoms with improved performance on a single GPU. Furthermore, we also explore parallelism and data layout choices (using flexible data structures called AoSoAs) and their effect on performance, seeing up to ~50% and ~5% improvements in performance on a GPU by choosing the right level of parallelism and data layout respectively.

97 MATHEMATICS AND COMPUTING↗