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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Space Shuttle Main Engine structural analysis and data reduction/evaluation. Volume 3A: High pressure oxidizer turbo-pump preburner pump housing stress analysis report

The model generation and structural analysis performed for the High Pressure Oxidizer Turbopump (HPOTP) preburner pump volute housing located on the main pump end of the HPOTP in the space shuttle main engine are summarized. An ANSYS finite element model of the volute housing was built and executed. A static structural analysis was performed on the Engineering Analysis and Data System (EADS) Cray-XMP supercomputer

Shannon, Robert V., Jr.↗
NASA NTRS1999

ADPAC v1.0: User's Manual

The overall objective of this study was to evaluate the effects of turbulence models in a 3-D numerical analysis on the wake prediction capability. The current version of the computer code resulting from this study is referred to as ADPAC v7 (Advanced Ducted Propfan Analysis Codes -Version 7). This report is intended to serve as a computer program user's manual for the ADPAC code used and modified under Task 15 of NASA Contract NAS3-27394. The ADPAC program is based on a flexible multiple-block and discretization scheme permitting coupled 2-D/3-D mesh block solutions with application to a wide variety of geometries. Aerodynamic calculations are based on a four-stage Runge-Kutta time-marching finite volume solution technique with added numerical dissipation. Steady flow predictions are accelerated by a multigrid procedure. Turbulence models now available in the ADPAC code are: a simple mixing-length model, the algebraic Baldwin-Lomax model with user defined coefficients, the one-equation Spalart-Allmaras model, and a two-equation k-R model. The consolidated ADPAC code is capable of executing in either a serial or parallel computing mode from a single source code.

Hall, Edward J.↗
NASA NTRS1997

Fast Context Switching in Real-Time Propositional Reasoning

The trend to increasingly capable and affordable control processors has generated an explosion of embedded real-time gadgets that serve almost every function imaginable. The daunting task of programming these gadgets is greatly alleviated with real-time deductive engines that perform all execution and monitoring functions from a single core model, Fast response times are achieved using an incremental propositional deductive database (an LTMS). Ideally the cost of an LTMS's incremental update should be linear in the number of labels that change between successive contexts. Unfortunately an LTMS can expend a significant percentage of its time working on labels that remain constant between contexts. This is caused by the LTMS's conservative approach: a context switch first removes all consequences of deleted clauses, whether or not those consequences hold in the new context. This paper presents a more aggressive incremental TMS, called the ITMS, that avoids processing a significant number of these consequences that are unchanged. Our empirical evaluation for spacecraft control shows that the overhead of processing unchanged consequences can be reduced by a factor of seven.

Nayak, P. Pandurang↗
NASA NTRS1996

A Visual Database System for Image Analysis on Parallel Computers and its Application to the EOS Amazon Project

The goal of this task was to create a design and prototype implementation of a database environment that is particular suited for handling the image, vision and scientific data associated with the NASA's EOC Amazon project. The focus was on a data model and query facilities that are designed to execute efficiently on parallel computers. A key feature of the environment is an interface which allows a scientist to specify high-level directives about how query execution should occur.

Shapiro, Linda G.↗
NASA NTRS1998

Neural Networks for Rapid Design and Analysis

Artificial neural networks have been employed for rapid and efficient dynamics and control analysis of flexible systems. Specifically, feedforward neural networks are designed to approximate nonlinear dynamic components over prescribed input ranges, and are used in simulations as a means to speed up the overall time response analysis process. To capture the recursive nature of dynamic components with artificial neural networks, recurrent networks, which use state feedback with the appropriate number of time delays, as inputs to the networks, are employed. Once properly trained, neural networks can give very good approximations to nonlinear dynamic components, and by their judicious use in simulations, allow the analyst the potential to speed up the analysis process considerably. To illustrate this potential speed up, an existing simulation model of a spacecraft reaction wheel system is executed, first conventionally, and then with an artificial neural network in place.

Sparks, Dean W., Jr.↗
NASA NTRS2003

An Approach to Autonomous Operations for Remote Mobile Robotic Exploration

This viewgraph presentation addresses the use of autonomy for remote mobile robotic exploration. The contents include; 1) Why Use Autonomy?; 2) What Are Some Options? JPL (Reasoning); 3) More Options.. . (Modeling); 4) The CLEaR Control System (Closed Loop Execution and Recovery); 5) Method of Response; 6) Overall Goal; 7) CLEaR in Action; 8) Initial Scenario; 9) Initial Scenario - Planned; 10) Unforeseen Events; and 11) Ongoing Research.

rovers↗
NASA NTRS2011

An Integrated Hydrological and Water Management Study of the Entire Nile River System - Lake Victoria to Nile Delta

The Nile basin River system spans 3 million km(exp 2) distributed over ten nations. The eight upstream riparian nations, Ethiopia, Eretria, Uganda, Rwanda, Burundi, Congo, Tanzania and Kenya are the source of approximately 86% of the water inputs to the Nile, while the two downstream riparian countries Sudan and Egypt, presently rely on the river's flow for most of the their needs. Both climate and agriculture contribute to the complicated nature of Nile River management: precipitation in the headwaters regions of Ethiopia and Lake Victoria is variable on a seasonal and inter-annual basis, while demand for irrigation water in the arid downstream region is consistently high. The Nile is, perhaps, one of the most difficult trans-boundary water issue in the world, and this study would be the first initiative to combine NASA satellite observations with the hydrologic models study the overall water balance in a to comprehensive manner. The cornerstone application of NASA's Earth Science Research Results under this project are the NASA Land Data Assimilation System (LDAS) and the USDA Atmosphere-land Exchange Inverse (ALEXI) model. These two complementary research results are methodologically independent methods for using NASA observations to support water resource analysis in data poor regions. Where an LDAS uses multiple sources of satellite data to inform prognostic simulations of hydrological process, ALEXI diagnoses evapotranspiration and water stress on the basis of thermal infrared satellite imagery. Specifically, this work integrates NASA Land Data Assimilation systems into the water management decision support systems that member countries of the Nile Basin Initiative (NBI) and Regional Center for Mapping of Resources for Development (RCMRD, located in Nairobi, Kenya) use in water resource analysis, agricultural planning, and acute drought response to support sustainable development of Nile Basin water resources. The project is motivated by the recognition that accurate, frequent, and spatially distributed estimates of the water balance are necessary for effective water management. This creates a challenge for watersheds that are large, include data poor regions, and/or span multiple nations. All of these descriptors apply to the Nile River basin, yet successful management of the Nile is critical for development and political stability in the region. For this reason, improved hydrological data to support cooperative water management in the Nile basin is a priority for USAID, the US State Department, the World Bank and other international organizations. In this project, the U.S. based research team is working with partners at RCMRD, Nile Basin Initiative (NBI), and their member national-level agencies to develop satellite-based land cover maps, satellite-derived evapotranspiration estimates (using the ALEXI algorithm), and NASA's Land Data Assimilation System (LDAS) customized to match identified information needs. The cornerstone applied sciences product of the project is the development of a customized "Nile LDAS" that will produce optimal estimates of hydrological states and fluxes, as vetted against the in situ observations of NBI and RCMRD member organizations and independent satellite-derived hydrological estimates. Nile LDAS will be applied to improve the reliability of emerging Decision Support Systems in applications that include drought monitoring, reservoir management, and irrigation planning. The end-users such as RCMRD, NBI, Ethiopian and Kenya Meteorological and Famine Early Warning System Network (FEWSNet) will be the eventual benefactors of this work. There will be a capacity building process involving the above end-user organizations and transfer the models and the results for these organizations to execute for future use. The team has already initiated this study and the early results of first years' work are shown. The plan is to complete this work by late 2013.

Habib, Shahid↗
NASA NTRS2012

Low Order Modeling Tools for Preliminary Pressure Gain Combustion Benefits Analyses

Pressure gain combustion (PGC) offers the promise of higher thermodynamic cycle efficiency and greater specific power in propulsion and power systems. This presentation describes a model, developed under a cooperative agreement between NASA and AFRL, for preliminarily assessing the performance enhancement and preliminary size requirements of PGC components either as stand-alone thrust producers or coupled with surrounding turbomachinery. The model is implemented in the Numerical Propulsion Simulation System (NPSS) environment allowing various configurations to be examined at numerous operating points. The validated model is simple, yet physics-based. It executes quickly in NPSS, yet produces realistic results.

Paxson, Daniel E.↗
NASA NTRS2010

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

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

fault tolerance↗
DOE OSTI2019

Profiling at Oliktok Point to Enhance Year of Polar Prediction (YOPP) Experiments (POPEYE) Field Campaign Report

The arctic region is rapidly evolving, and enhanced predictive capabilities, for both weather and climate, are urgently required. Therefore, the international community is executing an extended period of focused observations and modeling of the arctic environment, dubbed the Year of Polar Prediction or YOPP. The YOPP featured two special observing periods (SOPs) (and will feature a third in 2020). The first of the completed SOPs occurred in the spring of 2018, and the second during the late summer and early fall of 2018. Here, we summarize the deployment of additional U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility resources to the third ARM Mobile Facility (AMF3) at Oliktok Point, Alaska during the second three-month SOP (1 July 2018–30 September 2018). This deployment, named POPEYE (Profiling at Oliktok Point to Enhance YOPP Experiments), included the launching of extra radiosondes (one more per day) and operation of ARM-operated unmanned aircraft (DataHawk 2s) and ARM-operated tethered balloons. These instruments conducted routine profiling activities over the course of the special observing period to obtain measurements of atmospheric thermodynamic structure, cloud and precipitation properties, and aerosol properties. These measurements are expected to be used for a variety of purposes, including: 1) to conduct detailed studies of arctic cloud and aerosol processes; 2) to inform YOPP modeling efforts through real-time availability for assimilation into operational and research analysis products; 3) to evaluate and improve retrieval algorithms involving ARM remote sensors; 4) to evaluate and improve a variety of modeling tools being used to forecast arctic weather and climate; and 5) to initialize and evaluate simulations associated with a potential arctic large-eddy (LES) simulation framework similar to the ongoing LES ARM Symbiotic Simulation and Observation (LASSO) project.

Oliktok Point, Arctic processes, unmanned aerial v↗
DOE OSTI2015

Developing Uintah's Runtime System for Forthcoming Architectures

The need to solve ever-more complex science and engineering problems on computer architectures that are rapidly evolving poses considerable challenges for modern simulation frameworks and packages. While the decomposition of software into a programming model that generates a task-graph for execution by a runtime system make portability and performance possible, it also imposes considerable demands on that runtime system. Here, the challenge of meeting those demands for near-term and longer term systems in the context of the Uintah framework is considered in key areas such as support for data structures, tasks on heterogeneous architectures, performance portability, power management and designing for resilience by the use of replication based upon adaptive mesh refinement processes is addressed. Examples of techniques in these areas are used to illustrate performance improvements obtained for present large-scale systems and the needs of proposed systems considered.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Integrated system failure analysis software toolchain (IS-FAST)

Systems and methods are provided for generating faults and analyzing fault propagation and its effects. Starting from the ontologies of components, functions, flows, and faults, systems and methods are provided that describe, generate and track faults in a computer system across multiple domains throughout design and/or development. In order to construct the system and fault models, a series of concepts is introduced in the form of ontologies and their dependencies. An investigation is performed into the faults, including their type, cause, life-cycle aspects, and effect. Principles and rules are created to generate various faults based on system configurations. After the modeling process, a simulation engine is described to execute actions and simulate the process of fault generation and propagation. As a result, fault paths that impact components and functions can be obtained.

Diao, Xiaoxu↗
NASA NTRS2000

Present State of Knowledge of the Upper Atmosphere 1999: An Assessment Report

This document is issued in response to the Clean Air Act Amendment of 1990, Public Law 101-549, which mandates that the National Aeronautics and Space Administration (NASA) and other key agencies submit triennial reports to the Congress and the Environmental Protection Agency. NASA specifically is charged with the responsibility of reporting on the state of our knowledge of the Earth's upper atmosphere, particularly the stratosphere. Part l of this report summarizes the objectives, status, and accomplishments of the research tasks supported under NASA's Upper Atmosphere Research Program and Atmospheric Chemistry Modeling and Analysis Program for the period of 1997-1999. Part 2 (this document) is a compilation of several scientific assessments, reviews, and summaries. Section B (Scientific Assessment of Ozone Depletion: 1998), Section C (a summary of the 1998 Stratospheric Processes and their Role in Climate, SPARC, ozone trends report), Section D (the policymakers summary of the Intergovernmental Panel on Climate Change, IPCC, report on Aviation and the Global Atmosphere), and Section E (the executive summary of the NASA Assessment of the Effects of High-Speed Aircraft in the Stratosphere: 1998) are summaries of the most recent assessments of our current understanding of the chemical composition and the physical structure of the stratosphere, with particular emphasis on how the abundance and distribution of ozone is predicted to change in the future. Section F (the executive summary of NASA's Second Workshop on Stratospheric Models and Measurements, M&M 11) and Section G (the end-of-mission statement for the Photochemistry of ozone Loss in the Arctic Region in Summer, POLARIS, campaign) describe the scientific results for a comprehensive modeling intercomparison exercise and an aircraft and balloon measurement campaign, respectively. Section H (Chemical Kinetics and Photochemical Data for Use in Stratospheric Modeling: Update to Evaluation Number 12 of the NASA Panel for Data Evaluation) highlights the latest of NASA's reviews of this important aspect of the atmospheric sciences. A list of contributors to each of the included documents appears in Section I of this report.

Kurylo, M. J.↗
NASA NTRS2017

GEOS Atmospheric Model: Challenges at Exascale

The Goddard Earth Observing System (GEOS) model at NASA's Global Modeling and Assimilation Office (GMAO) is used to simulate the multi-scale variability of the Earth's weather and climate, and is used primarily to assimilate conventional and satellite-based observations for weather forecasting and reanalysis. In addition, assimilations coupled to an ocean model are used for longer-term forecasting (e.g., El Nino) on seasonal to interannual times-scales. The GMAO's research activities, including system development, focus on numerous time and space scales, as detailed on the GMAO website, where they are tabbed under five major themes: Weather Analysis and Prediction; Seasonal-Decadal Analysis and Prediction; Reanalysis; Global Mesoscale Modeling, and Observing System Science. A brief description of the GEOS systems can also be found at the GMAO website. GEOS executes as a collection of earth system components connected through the Earth System Modeling Framework (ESMF). The ESMF layer is supplemented with the MAPL (Modeling, Analysis, and Prediction Layer) software toolkit developed at the GMAO, which facilitates the organization of the computational components into a hierarchical architecture. GEOS systems run in parallel using a horizontal decomposition of the Earth's sphere into processing elements (PEs). Communication between PEs is primarily through a message passing framework, using the message passing interface (MPI), and through explicit use of node-level shared memory access via the SHMEM (Symmetric Hierarchical Memory access) protocol. Production GEOS weather prediction systems currently run at 12.5-kilometer horizontal resolution with 72 vertical levels decomposed into PEs associated with 5,400 MPI processes. Research GEOS systems run at resolutions as fine as 1.5 kilometers globally using as many as 30,000 MPI processes. Looking forward, these systems can be expected to see a 2 times increase in horizontal resolution every two to three years, as well as less frequent increases in vertical resolution. Coupling these resolution changes with increases in complexity, the computational demands on the GEOS production and research systems should easily increase 100-fold over the next five years. Currently, our 12.5 kilometer weather prediction system narrowly meets the time-to-solution demands of a near-real-time production system. Work is now in progress to take advantage of a hybrid MPI-OpenMP parallelism strategy, in an attempt to achieve a modest two-fold speed-up to accommodate an immediate demand due to increased scientific complexity and an increase in vertical resolution. Pursuing demands that require a 10- to 100-fold increases or more, however, would require a detailed exploration of the computational profile of GEOS, as well as targeted solutions using more advanced high-performance computing technologies. Increased computing demands of 100-fold will be required within five years based on anticipated changes in the GEOS production systems, increases of 1000-fold can be anticipated over the next ten years.

ESMF↗
DOE OSTI2020

Accelerating Hanford Site Cleanup through Operations Research Modeling - 20238

The Hanford Site cleanup effort will require the integration of dozens of unique facilities and processes, many of which will be first-of-a-kind in implementation and design. Each facility will be governed by its own set of operating logic, configured with a unique array of unit operations, and subject to a set of constraints that will affect its behavior. The collection of facilities have multiple points of interface, making the operations of any one facility potentially significant to the operations of other up- or downstream processes. It is therefore highly desirable to accurately predict these operations, as it allows for Site officials to identify and preempt bottlenecks and vulnerabilities before they unexpectedly inhibit the cleanup mission. With the quantity and complexity of the processes that will be on Site, building a pen-and-paper or even a spreadsheet-assisted model of the cleanup mission quickly becomes overwhelming in scope and inaccurate in execution. The Engineering organization for the Site's Tank Operations Contract (TOC) has therefore implemented the use of operations research (OR) modeling to simulate and predict future operations of Site facilities. These models are created using a discrete event simulation tool that allows for the development of detailed, versatile, and robust models. Not only can these models account for complex logical behaviors, but they can also simulate process details down to the level of vessel sizing, labor utilization, equipment reliability, and resource availability. To date, the TOC has developed OR models for several facilities on Site, including for single-shell tank (SST) farms, double-shell tank (DST) farms, the Effluent Treatment Facility (ETF), and the waste transfer system. These models have focused on identifying bottlenecks and operational constraints, and have been used to quantify the effects of implementing process changes. This latter point is particularly valuable, as it allows for several alternatives to be studied in a virtual setting before committing resources to making a change in the field. The decision to develop OR models has gained tremendous support from the Site's stakeholders and the U.S. Department of Energy (DOE) management, and has prompted the use of the tool to support additional internal and external initiatives. Recently, an initiative was proposed to use the models to help identify and provide quantitative backing for risks and opportunities for the TOC. This application of OR could not only help inform how the TOC manages its risks (e.g. quantities and types of spare parts), but could also help drive process improvements whose benefits might otherwise be hard to quantify. The models have also been used to drive the TOC's cloud computing, artificial intelligence (AI), and machine learning (ML) initiatives. These initiatives will not only improve the ability of the TOC to more rapidly respond to the needs of its customers, but it will also aid in the ability of the TOC to analyze and improve the processes it studies. Partnership with two external software development and consulting companies (Lanner and Ynformed) has furthered not only the application of AI and ML within the TOC, but has also spurred the development of new/improved software tools and platforms used by the companies. These partnerships have proven to be mutually beneficial and productive, and have set a precedent for the types of gains that can be made by exploring such options. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2006

Deep Impact Sequence Planning Using Multi-Mission Adaptable Planning Tools With Integrated Spacecraft Models

The Deep Impact mission was ambitious and challenging. JPL's well proven, easily adaptable multi-mission sequence planning tools combined with integrated spacecraft subsystem models enabled a small operations team to develop, validate, and execute extremely complex sequence-based activities within very short development times. This paper focuses on the core planning tool used in the mission, APGEN. It shows how the multi-mission design and adaptability of APGEN made it possible to model spacecraft subsystems as well as ground assets throughout the lifecycle of the Deep Impact project, starting with models of initial, high-level mission objectives, and culminating in detailed predictions of spacecraft behavior during mission-critical activities.

Deep Impact Mission↗