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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 1,639 records · Page 91
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Implementation of a Trailing-Edge Flap Analysis Model in the NASA Langley CAMRAD.MOD1/Hires Program

Continual advances in rotorcraft performance, vibration and acoustic characteristics are being sought by rotary-wing vehicle manufacturers to improve efficiency, handling qualities and community noise acceptance of their products. The rotor system aerodynamic and dynamic behavior are among the key factors which must be addressed to meet the desired goals. Rotor aerodynamicists study how airload redistribution impacts performance and noise, and seek ways to achieve better airload distribution through changes in local aerodynamic response characteristics. One method currently receiving attention is the use of trailing-edge flaps mounted on the rotor blades to provide direct control of a portion of the spanwise lift characteristics. The following work describes the incorporation of a trailing-edge flap model in the CAMRAD.Mod1/FHUS comprehensive rotorcraft analysis code. The CAM-RAD.Mod1/HIRES analysis consists of three separate executable codes. These include the comprehensive trim analysis, CAMRAD.Mod1, the Indicial Post-Processor, IPP, for high resolution airloads, and AIRFOIL, which produces the rotor airfoil tables from input airfoil section characteristics. The modifications made to these components permitting analysis of flapped rotor configurations are documented herein along with user instructions detailing the new input variables and operational notes.

Charles, Bruce↗
NASA NTRS2000

Analysis of the DFP/AFCS Systems for Compensating Gravity Distortions on the 70-Meter Antenna

This paper presents the theoretical computations showing the expected performances for both systems. The basic analysis tool is a Physical Optics reflector analysis code that was ported to a parallel computer for faster execution times. There are several steps involved in computing the RF performance of the various systems. 1 . A model of the RF distortions of the main reflector is required. This model is based upon measured holography maps of the 70-meter antenna obtained at 3 elevation angles. The holography maps are then processed (using an appropriate gravity mechanical model of the dish) to provide surface distortion maps at all elevation angles. 2. From the surface distortion maps, ray optics is used to determine the theoretical shape of the DFP that will exactly phase compensate the distortions. 3. From the theoretical shape and a NASTRAN mechanical model of the plate, the actuator positions that generate a surface that provides the best RMS fit to the theoretical model are selected. Using the actuator positions and the NASTRAN model provides an accurate description of the actual mirror shape. 4. Starting from the mechanical drawings of the feed, a computed RF feed pattern is generated. This pattern is expanded into a set of spherical wave modes so that a complete near field analysis of the reflector system can be obtained. 5. For the array feed, the excitation coefficients that provide the maximum gain are computed using a phase conjugate technique. The basic experimental geometry consisted of a dual shaped 70-meter antenna system; a refocusing ellipse, a DFP and an array feed system. To provide physical insight to the systems performance, focal plane field plots are presented at several elevations. Curves of predicted performance are shown for the DFP system, monopulse tracking system, AFCS and combined DFP/AFCS system. The calculated results show that the combined DFP/AFCS system is capable of recovering the majority of the gain lost due to gravity distortion.

Imbriale, William A.↗
NASA NTRS2004

MEANS User Documentation

MEANS is an acronym for the MIT Extensible Air Network Simulation. MIT is the Massachusetts Institute of Technology, where MEANS was developed. MEANS models the flow of aircraft in a simulation of the US National Airspace System. It can optionally follow the flow of passengers as well. The flight model takes the form of several queues through which the aircraft pass, as well as delays at certain times; the passengers can be transported via flights. MEANS is an event-based model. This means that events are scheduled at some point in the future, and once an event has executed, the simulation t h e advances to that of the next event. This is a different approach than some simulations (particularly those of physical systems), where time is advanced in fixed increments and the state of the system is updated at each step.This documentation is a "user guide" for MEANS - it describes how to USE means, not write new modules. For programming instructions, see the programming documentation, available as a separate document.This document assumse no programming knowledge. It will describe how to run MEANS, understand the data formats, and use such utility programs as already exist. For more detailed information on the internal workings of MEANS, and how to programmatically add to it, refer to the programming documentation.

Melconian, Terran↗
NASA NTRS1988

Watchdog activity monitor (WAM) for use wth high coverage processor self-test

A high fault coverage, instruction modeled self-test for a signal processor in a user environment is disclosed. The self-test executes a sequence of sub-tests and issues a state transition signal upon the execution of each sub-test. The self-test may be combined with a watchdog activity monitor (WAM) which provides a test-failure signal in the presence of a counted number of state transitions not agreeing with an expected number. An independent measure of time may be provided in the WAM to increase fault coverage by checking the processor's clock. Additionally, redundant processor systems are protected from inadvertent unsevering of a severed processor using a unique unsever arming technique and apparatus.

Tulpule, Bhalchandra R.↗
NASA NTRS2009

Bio-Inspired Neural Model for Learning Dynamic Models

A neural-network mathematical model that, relative to prior such models, places greater emphasis on some of the temporal aspects of real neural physical processes, has been proposed as a basis for massively parallel, distributed algorithms that learn dynamic models of possibly complex external processes by means of learning rules that are local in space and time. The algorithms could be made to perform such functions as recognition and prediction of words in speech and of objects depicted in video images. The approach embodied in this model is said to be "hardware-friendly" in the following sense: The algorithms would be amenable to execution by special-purpose computers implemented as very-large-scale integrated (VLSI) circuits that would operate at relatively high speeds and low power demands.

Duong, Tuan↗
NASA NTRS2011

InSAR Scientific Computing Environment

This computing environment is the next generation of geodetic image processing technology for repeat-pass Interferometric Synthetic Aperture (InSAR) sensors, identified by the community as a needed capability to provide flexibility and extensibility in reducing measurements from radar satellites and aircraft to new geophysical products. This software allows users of interferometric radar data the flexibility to process from Level 0 to Level 4 products using a variety of algorithms and for a range of available sensors. There are many radar satellites in orbit today delivering to the science community data of unprecedented quantity and quality, making possible large-scale studies in climate research, natural hazards, and the Earth's ecosystem. The proposed DESDynI mission, now under consideration by NASA for launch later in this decade, would provide time series and multiimage measurements that permit 4D models of Earth surface processes so that, for example, climate-induced changes over time would become apparent and quantifiable. This advanced data processing technology, applied to a global data set such as from the proposed DESDynI mission, enables a new class of analyses at time and spatial scales unavailable using current approaches. This software implements an accurate, extensible, and modular processing system designed to realize the full potential of InSAR data from future missions such as the proposed DESDynI, existing radar satellite data, as well as data from the NASA UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar), and other airborne platforms. The processing approach has been re-thought in order to enable multi-scene analysis by adding new algorithms and data interfaces, to permit user-reconfigurable operation and extensibility, and to capitalize on codes already developed by NASA and the science community. The framework incorporates modern programming methods based on recent research, including object-oriented scripts controlling legacy and new codes, abstraction and generalization of the data model for efficient manipulation of objects among modules, and well-designed module interfaces suitable for command- line execution or GUI-programming. The framework is designed to allow users contributions to promote maximum utility and sophistication of the code, creating an open-source community that could extend the framework into the indefinite future.

Rosen, Paul A.↗
NASA NTRSDate not supplied

TPSAS-NF1676L-32584-DND

Cryogenic, high-Reynolds number testing has been in use for approximately 40 years. This reliable testing capability is used to more closely predict full-scale flight aerodynamics for improved accuracy in estimates of aircraft performance, stability, and loads. A technical course has been created to educate industry, institutional, and research professionals in the overview of cryogenic, high-Reynolds number testing and its impact, the availability and accessibility of cryogenic testing and considerations in test planning and execution, and the discussion of other in-depth technical topics. This particular presentation supports a section of model preparation, tunnel operations, and model access procedures that are unique to cryogenic wind-tunnel testing.

Scott Goodliff↗
NASA NTRS2022

Towards Self-Adaptive Game Logic

Self-adaptive systems (SAS) can reconfigure at run time in response to changing situations to express acceptable behaviors in the face of uncertainty. With respect to game design, such situations may include user input, emergent behaviors, performance concerns, and combinations thereof. Typically an SAS is modeled as a feedback loop that functions within an existing system, with operations including monitoring, analyzing, planning, and executing (i.e., MAPE-K) to enable online reconfiguration. This paper presents a conceptual approach for extending software engineering artifacts to be self-adaptive within the context of game design. We have modified a game developed for creative coding education to include a MAPE-K self-adaptive feedback loop, comprising run-time adaptation capabilities and the software artifacts required to support adaptation.

Erik M. Fredericks↗
NASA NTRSDate not supplied

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.

machine learning↗
NASA NTRS2023

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be easily expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.

machine learning↗
DOE OSTI2016

Building America Case Study: Savannah Gardens, Savannah, Georgia

Southface Energy Institute (Southface) partnered with owners and/or builders with various market constraints and ultimate goals for three projects in different climate zones: Savannah, GA (CZ 2), Clemson, SC (CZ 3), and LaFayette, GA (CZ 4). This report documents the design process, computational energy modeling, construction, envelope performance metrics, long-term monitoring results, and successes and failures of the design and execution of these high performance homes. The three bedroom/two bathroom test home in Savannah Gardens on an elevated slab foundation has a semi-conditioned, encapsulated attic. A neighboring home built to EarthCraft specifications was also monitored as a control for exterior foam insulation and a heat pump water heater (HPWH). For the JMC Patrick Square, a single-story project in Clemson, the small-scale production builder wanted to increase their level of energy efficiency beyond their current green building practices, including bringing ducts into conditioned space. Through a combination of upgrade measures the team met this goal and achieved many Zero Energy Ready Home requirements. LaFayette Housing Authority undertook a development of 30 affordable rental housing units in 15 duplexes in LaFayette, GA. Because they would be long-term owners, their priorities were low utility bills for the residents and durable, maintainable buildings. The team employed BEopt to optimize building envelope and systems choices, including 2x6 advanced framed walls, insulated slab, and heat pump water heater in a utility closet which was ducted to/from an encapsulated attic.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Uncertainty Quantification and Sensitivity Analysis of Nuclear Construction Cost Reduction Pathways

High construction costs have plagued recent nuclear projects and they hamper the widespread deployment of nuclear technology. The Nuclear Cost Reduction Tool is a plant economic framework that quantifies the impact that various plant design and construction attributes have on construction costs and cost overruns and shows the positive effects of learning over a series of deployments. However, a downside of the current model is that all model outputs and capabilities are deterministic. To provide a more comprehensive view, this study evaluated the impact of model parameter uncertainty through a sensitivity analysis applied to 18 model parameters. This approach quantified the impact of model uncertainty on the output variables of Net Overnight Capital Cost (Net OCC), Construction Duration (CD), and Levelized Cost of Electricity (LCOE). Monte Carlo analysis revealed uncertainty distributions for these variables, showing that absolute uncertainty decreases over a series of deployments. A local sensitivity analysis showed that even small parameter perturbations (5%) can have a significant impact on project execution, highlighting areas that could reduce costs by billions across an order book of plants. The results of this study have improved the understanding of the model and identified the most impactful model parameters and construction attributes.

Levelized Cost of Electricity↗
NASA NTRS1981

Applications systems verification and transfer project. Volume 1: Operational applications of satellite snow cover observations: Executive summary

Both LANDSAT and NOAA satellite data were used in improving snowmelt runoff forecasts. When the satellite snow cover data were tested in both empirical seasonal runoff estimation and short term modeling approaches, a definite potential for reducing forecast error was evident. A cost benefit analysis run in conjunction with the snow mapping indicated a $36.5 million annual benefit accruing from a one percent improvement in forecast accuracy using the snow cover data for the western United States. The annual cost of employing the system would be $505,000. The snow mapping has proven that satellite snow cover data can be used to reduce snowmelt runoff forecast error in a cost effective manner once all operational satellite data are available within 72 hours after acquisition. Executive summaries of the individual snow mapping projects are presented.

Rango, A.↗
DOE OSTI2015

The CPAT Domain Model - How CPAT "Thinks" from an Analyst Perspective

To help effectively plan the management and modernization of its large and diverse fleet of vehicles, the Program Executive Office Ground Combat Systems (PEO GCS) commissioned the development of a large-scale portfolio planning optimization tool. This software, the Capability Portfolio Analysis Tool (CPAT), creates a detailed schedule that optimally prioritizes the modernization or replacement of vehicles within the fleet - respecting numerous business rules associated with fleet structure, budgets, industrial base, research and testing, etc., while maximizing overall fleet performance through time. This report contains a description of the organizational fleet structure and a thorough explanation of the business rules that the CPAT formulation follows involving performance, scheduling, production, and budgets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1989

Modeling the missile-launch tube problem in DYSCO

DYSCO is a versatile, general purpose dynamic analysis program which assembles equations and solves dynamics problems. The executive manages a library of technology modules which contain routines that compute the matrix coefficients of the second order ordinary differential equations of the components. The executive performs the coupling of the equations of the components and manages the solution of the coupled equations. Any new component representation may be added to the library if, given the state vector, a FORTRAN program can be written to compute M, C, K, and F. The problem described demonstrates the generality of this statement.

Berman, Alex↗
NASA NTRS1994

National facilities study. Volume 3: Mission and requirements model report

The National Facility Study (NFS) was initiated in 1992 by Daniel S. Goldin, Administrator of NASA as an initiative to develop a comprehensive and integrated long-term plan for future facilities. The resulting, multi-agency NFS consisted of three Task Groups: Aeronautics, Space Operations, and Space Research and Development (R&D) Task Groups. A fourth group, the Engineering and Cost Analysis Task Group, was subsequently added to provide cross-cutting functions, such as assuring consistency in developing an inventory of space facilities. Space facilities decisions require an assessment of current and future needs. Therefore, the two task groups dealing with space developed a consistent model of future space mission programs, operations and R&D. The model is a middle ground baseline constructed for NFS analytical purposes with excursions to cover potential space program strategies. The model includes three major sectors: DOD, civilian government, and commercial space. The model spans the next 30 years because of the long lead times associated with facilities development and usage. This document, Volume 3 of the final NFS report, is organized along the following lines: Executive Summary -- provides a summary view of the 30-year mission forecast and requirements baseline, an overview of excursions from that baseline that were studied, and organization of the report; Introduction -- provides discussions of the methodology used in this analysis; Baseline Model -- provides the mission and requirements model baseline developed for Space Operations and Space R&D analyses; Excursions from the baseline -- reviews the details of variations or 'excursions' that were developed to test the future program projections captured in the baseline; and a Glossary of Acronyms.

Source record↗
NASA NTRS1994

Design for dependability: A simulation-based approach

This research addresses issues in simulation-based system level dependability analysis of fault-tolerant computer systems. The issues and difficulties of providing a general simulation-based approach for system level analysis are discussed and a methodology that address and tackle these issues is presented. The proposed methodology is designed to permit the study of a wide variety of architectures under various fault conditions. It permits detailed functional modeling of architectural features such as sparing policies, repair schemes, routing algorithms as well as other fault-tolerant mechanisms, and it allows the execution of actual application software. One key benefit of this approach is that the behavior of a system under faults does not have to be pre-defined as it is normally done. Instead, a system can be simulated in detail and injected with faults to determine its failure modes. The thesis describes how object-oriented design is used to incorporate this methodology into a general purpose design and fault injection package called DEPEND. A software model is presented that uses abstractions of application programs to study the behavior and effect of software on hardware faults in the early design stage when actual code is not available. Finally, an acceleration technique that combines hierarchical simulation, time acceleration algorithms and hybrid simulation to reduce simulation time is introduced.

Goswami, Kumar K.↗
NASA NTRS1992

Executive summary

The Astrotech 21 Optical Systems Technology Workshop was held in Pasadena, California on March 6-8, 1991. The purpose of the workshop was to examine the state of Optical Systems Technology at the National Aeronautics Space Administration (NASA), and in industry and academia, in view of the potential Astrophysics mission set currently being considered for the late 1990's through the first quarter of the 21st century. The principal result of the workshop is this publication, which contains an assessment of the current state of the technology, and specific technology advances in six critical areas of optics, all necessary for the mission set. The workshop was divided into six panels, each of about a dozen experts in specific fields, representing NASA, industry, and academia. In addition, each panel contained expertise that spanned the spectrum from x-ray to submillimeter wavelengths. This executive summary contains the principal recommendations of each panel. The six technology panels and their chairs were: (1) Wavefront Sensing, Control, and Pointing, Thomas Pitts, Itek Optical Systems, A Division of Litton; (2) Fabrication, Roger Angel, Steward Observatory, University of Arizona; (3) Materials and Structures, Theodore Saito, Lawrence Livermore National Laboratory; (4) Optical Testing, James Wyant, WYKO Corporation; (5) Optical Systems Integrated Modeling, Robert R. Shannon, Optical Sciences Center, University of Arizona; and (6) Advanced Optical Instruments Technology, Michael Shao, Jet Propulsion Laboratory, California Institute of Technology. This Executive Summary contains the principal recommendations of each panel.

Ayon, Juan A.↗