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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1976

Payload crew interface design criteria and techniques. Task 1: Inflight operations and training for payloads

Guidelines are developed for use in control and display panel design for payload operations performed on the aft flight deck of the orbiter. Preliminary payload procedures are defined. Crew operational concepts are developed. Payloads selected for operational simulations were the shuttle UV optical telescope (SUOT), the deep sky UV survey telescope (DUST), and the shuttle UV stellar spectrograph (SUSS). The advanced technology laboratory payload consisting of 11 experiments was selected for a detailed evaluation because of the availability of operational data and its operational complexity.

Carmean, W. D.↗
NASA NTRS1978

Space Shuttle data acquisition and processing

The techniques of minimizing the amount of Space Shuttle data processed by using real-time ground data and on board computer to prescreen the data are presented. With the advent of long duration space flight and the large amounts of data generated, it became necessary to limit the amount of data to be processed and also to present data in a manner that highlights the most important data. The Shuttle operational data system with output of 128 kilobits/sec, the developmental flight instrumentation system, and screening of data by the analysts, flight crew, or onboard computers is described, noting that by using these monitoring methods the mission evaluation team can rapidly determine time periods for which postflight data processing is required. Postflight processing systems, batch system output products, data compression techniques, the interactive computer system, and the special telemetry conversion system are discussed, and it is concluded that the combination of Shuttle on board and ground data processing systems enhances the ability to perform a functional evaluation of the Space Shuttle vehicle.

Foster, G. B., Jr.↗
NASA NTRS2018

Error Characterization and Mitigation for 16Nm MLC NAND Flash Memory Under Total Ionizing Dose Effect

A data device includes a memory having a plurality of memory cells configured to store data values in accordance with a predetermined rank modulation scheme that is optional and a memory controller that receives a current error count from an error decoder of the data device for one or more data operations of the flash memory device and selects an operating mode for data scrubbing in accordance with the received error count and a program cycles count.

Li, Yue↗
NASA NTRSDate not supplied

NASA’s Simulation Activities for Evaluating UAM Concept of Operations

NASA’s vision for Advanced Air Mobility (AAM) is to help emerging aviation markets to safely develop an air transportation system that moves people and cargo between places previously not served or underserved by aviation using revolutionary new aircraft types. Urban Air Mobility (UAM), the concept of expanding transportation networks by serving short flights to transport people and goods around metropolitan areas, is part of a larger paradigm shift toward AAM, in which new technologies and business models are enabling transformational applications of aviation. In NASA’s Concept of Operations for UAM, Providers of Services for UAM (PSU) play a key role to enable safe and efficient UAM operations by sharing operational data among UAM operators. PSUs provide a pre-departure strategic conflict management service that establishes operational plans for new flights and coordinates them with all relevant operations in shared airspace. The strategic conflict management aims to minimize the need for tactical separation provision, while considering anticipated traffic demand, vertiport capacity and availability, forecasted weather, and airspace restrictions. NASA is currently preparing a new lab evaluation activity with aviation industry partners, called the X4 simulation, to demonstrate their airspace services and capabilities and test new information exchange requirements for UAM operations. This simulation activity will support NASA’s National Campaign (NC) flight tests which are planned over the next several years to guide the collective community and stakeholders through a series of scenario-based test activities that involve vehicles and airspace management services operating in a live test environment. This presentation will provide an overview of the X4 simulation, including objectives, scenarios, assumptions, system architecture, and test schedule. The X4 simulation will evaluate the interconnectivity and operational intent sharing between PSUs for multiple operators, as well as the performance of strategic conflict management.

Urban Air Mobility, Simulation, Advanced Air Mobil↗
NASA NTRSDate not supplied

Data-Centric Operational Design Domain Characterization for Machine Learning-Based Aeronautical Products

We give for Machine Learning (ML)-based aeronautical products, a first rigorous characterization of Operational Design Domains (ODDs). Unlike in other application sectors (such as self-driving road vehicles) where ODD development is scenario-based, our approach is data-centric: we propose the dimensions along which the parameters that define an ODD can be explicitly captured, using a top-down approach starting from system specifications, and a bottom-up approach starting from detailed ML Model (MLM) designs. Then we give a categorization of the data that ML-based applications can encounter in operation, identifying their system-level relevance and impact. Specifically, we discuss how those data categories are useful to determine: (1) the requirements necessary to drive the design of MLMs; (2) the potential effects on the MLM and higher levels of the system hierarchy; (3) the learning assurance processes that may be needed, and (4) system architectural considerations. We illustrate the underlying concepts with an example of an aircraft flight envelope. The approach in this paper is one of the cornerstones of a future process guidance for development and certification/approval of safety-related aeronautical products implementing Artificial Intelligence (AI), currently being developed through aviation industry-based consensus, jointly by the SAE G-34 Committee for AI in aviation, and EUROCAE WG-114 for AI.

Aeronautical products↗
NASA NTRSDate not supplied

Infusion of AI/ML Technology into Operational NASA Data Systems

NASA has been developing a variety of Artificial Intelligence / Machine Learning technologies related to Earth Observations. In most cases, the full value of such a technology is realized when it is infused into an operational system. NASA’s Earth Science Data Systems program has been formulating repeatable methods to execute technology infusion. These efforts include the Advancing Collaborative Connections for Earth System Science (ACCESS) program, a Technology Infusion Playbook, and an assemblage of working groups investigating methods for infusion collaboration, community development, and capacity building. ESDS has also been executing a pathfinder activity to infuse a machine-learning-driven recommender of science keywords for Earth Observation datasets, which is intended to be used for metadata curation in the Earth Observation System Data and Information System.

C Lynnes↗
NASA NTRS2016

The Laser Ranging Experiment of the Lunar Reconnaissance Orbiter: Five Years of Operations and Data Analysis

We describe the results of the Laser Ranging (LR) experiment carried out from June 2009 to September 2014 in order to make one-way time-of-flight measurements of laser pulses between Earth-based laser ranging stations and the Lunar Reconnaissance Orbiter (LRO) orbiting the Moon. Over 4,000 hours of successful LR data are obtained from 10 international ground stations. The 20-30 centimeter precision of the full-rate LR data is further improved to 5-10 centimeter after conversion into normal points. The main purpose of LR is to utilize the high accuracy normal point data to improve the quality of the LRO orbits, which are nomi- nally determined by the radiometric S-band tracking data. When independently used in the LRO precision orbit determination process with the high-resolution GRAIL (Gravity Recovery and Interior Laboratory) gravity model, LR data provide good orbit solutions, with an average difference of approximately 50 meters in total position, and approximately 20 centimeters in radial direction, compared to the definitive LRO trajectory. When used in combination with the S-band tracking data, LR data help to improve the orbit accuracy in the radial direction to approximately 15 centimeters. In order to obtain highly accurate LR range measurements for precise orbit determination results, it is critical to closely model the behavior of the clocks both at the ground stations and on the spacecraft. LR provides a unique data set to calibrate the spacecraft clock. The LRO spacecraft clock is characterized by the LR data to a timing knowledge of 0.015 milliseconds over the entire 5 years of LR operation. We here present both the engineering setup of the LR experiments and the detailed analysis results of the LR data.

Mao, Dandan↗
NASA NTRS2007

Data Mining Methods Applied to Flight Operations Quality Assurance Data: A Comparison to Standard Statistical Methods

In a previous study, multiple regression techniques were applied to Flight Operations Quality Assurance-derived data to develop parsimonious model(s) for fuel consumption on the Boeing 757 airplane. The present study examined several data mining algorithms, including neural networks, on the fuel consumption problem and compared them to the multiple regression results obtained earlier. Using regression methods, parsimonious models were obtained that explained approximately 85% of the variation in fuel flow. In general data mining methods were more effective in predicting fuel consumption. Classification and Regression Tree methods reported correlation coefficients of .91 to .92, and General Linear Models and Multilayer Perceptron neural networks reported correlation coefficients of about .99. These data mining models show great promise for use in further examining large FOQA databases for operational and safety improvements.

Stolzer, Alan J.↗
NASA NTRS2012

Engineering a Multimission Approach to Navigation Ground Data System Operations

The Mission Design and Navigation (MDNAV) Section at the Jet Propulsion Laboratory (JPL) supports many deep space and earth orbiting missions from formulation to end of mission operations. The requirements of these missions are met with a multimission approach to MDNAV ground data system (GDS) infrastructure capable of being shared and allocated in a seamless and consistent manner across missions. The MDNAV computing infrastructure consists of compute clusters, network attached storage, mission support area facilities, and desktop hardware. The multimission architecture allows these assets, and even personnel, to be leveraged effectively across the project lifecycle and across multiple missions simultaneously. It provides a more robust and capable infrastructure to each mission than might be possible if each constructed its own. It also enables a consistent interface and environment within which teams can conduct all mission analysis and navigation functions including: trajectory design; ephemeris generation; orbit determination; maneuver design; and entry, descent, and landing analysis. The savings of these efficiencies more than offset the costs of increased complexity and other challenges that had to be addressed: configuration management, scheduling conflicts, and competition for resources. This paper examines the benefits of the multimission MDNAV ground data system infrastructure, focusing on the hardware and software architecture. The result is an efficient, robust, scalable MDNAV ground data system capable of supporting more than a dozen active missions at once.

Mission Design and Navigation (MDNAV)↗
NASA NTRS1997

Pioneer Venus Orbiter Ultraviolet Spectrometer: Operations and Data Analysis

The Pioneer Venus spacecraft orbited Venus 5,055 times between 4th December 1978 and 6th October 1992, before entering Venus' atmosphere and burning up on the latter date. On 255 of these orbits, science operations were suspended because of superior conjunction (Venus' proximity to the Sun as seen from Earth). Of the remaining 4800 orbits, about 85% yielded good-quality OUVS science data; 15% were lost to various problems, including loss of uplink (commands) to and downlink (data) from the spacecraft, errors in commanding OUVS, and one or other of the two instrument anomalies mentioned below.

Stewart, A. I. F.↗
NASA NTRS1997

Pioneer Venus Orbiter Ultraviolet Spectrometer: Operations and Data Analysis

The Ultraviolet Spectrometer investigation on the Pioneer Venus Orbiter mission was extremely successful. The instrument was designed, built and tested at CU/LASP and delivered on time and within budget. The spacecraft and its instruments were required to operate for 243 days in Venus orbit. OUVS operated successfully for a further 13 years with only minor problems. The major scientific results listed above that deal with Venus were all unexpected and significant discoveries. The Comet Halley observations came about because of a favorable alignment of Halley, the Sun, and Venus, and were an important contribution to the international study of this comet. The scientific results of the OUVS investigation are to be found in the 41 papers listed in section 4 below. OUVS data provided material for 6 PhD and one MS dissertations, listed in section 5 below.

Stewart, A. I. F.↗
NASA NTRS1990

Vehicle processing operations database

The Vehicle Processing Operations Data base (VPOD) has been designed and constructed to enable the collection and preliminary analysis of data relative to the processing of planetary exploration vehicles in low Earth orbit. VPOD has the capability to hierarchically store the events, assemblies, and equipment data needed for performing vehicle processing analysis, to whatever level of detail available. This permits some analysis to be performed even during the very early phases of vehicle and mission design. As more data about the events, assemblies, or equipment become available, the analysis can then be refined using the increased level of detail. VPOD output is based on a mission defined by selecting from the VPOD Events data base the events that are required to perform the desired tasks. The outputs available include total crew time, crew skill requirements, equipment needed, assemblies worked per event, to any available level of detail. The VPOD design is structured to interface with an operations simulation capability which is currently in the requirements definition phase. The simulator will use information from the VPOD to perform operations analysis for missions being conducted in low Earth orbit. VPOD information, along with information from the science missions operations data base and a Space Station configuration data base, will be used by the simulator to provide a capability to study total system operations and resources requirements.

Ganoe, George G.↗
NASA NTRS2009

Stirling Convertor Extended Operation Testing and Data Analysis at GRC

This paper focuses on extended operation testing and data analysis of free-piston Stirling convertors at the NASA Glenn Research Center (GRC). Extended operation testing is essential to the development of radioisotope power systems and their potential use for long duration missions. To document the reliability of the convertors, regular monitoring and analysis of the extended operation data is particularly valuable; allowing us to better understand and quantity the long life characteristics of the convertors. Further, investigation and comparison of the extended operation data to baseline performance data provides us an opportunity for understanding system behavior should any off-nominal performance occur. GRC currently has 14 Stirling convertors under 24-hour unattended extended operation testing, including two operating the Advanced Stirling Radioisotope Generator Engineering Unit (ASRG-EU). 10 of the 14 Stirling convertors at GRC are the Advanced Stirling Convertors (ASC) developed by Sunpower, Incorporated. These are highly efficient (up to > 33.5% conversion efficiency), low mass convertors that have evolved through technologically progressive convertor builds. The remaining four convertors at GRC are Technology Demonstration Convertors (TDC) from Infinia Corporation. They have achieved> 27% conversion efficiency and have accumulated over 178,000 of the total 250,622 hours of extended operation currently at GRC. A synopsis of the Stirling convertor extended operation testing and data analysis at NASA GRC is presented in this paper, as well as how this testing has contributed to the Stirling convertor's progression toward flight.

Cornell, Peggy A.↗
NASA NTRS2010

A Near-Term Concept for Trajectory Based Operations with Air/Ground Data Link Communication

An operating concept and required system components for trajectory-based operations with air/ground data link for today's en route and transition airspace is proposed. Controllers are fully responsible for separation as they are today, and no new aircraft equipage is required. Trajectory automation computes integrated solutions to problems like metering, weather avoidance, traffic conflicts and the desire to find and fly more time/fuel efficient flight trajectories. A common ground-based system supports all levels of aircraft equipage and performance including those equipped and not equipped for data link. User interface functions for the radar controller's display make trajectory-based clearance advisories easy to visualize, modify if necessary, and implement. Laboratory simulations (without human operators) were conducted to test integrated operation of selected system components with uncertainty modeling. Results are based on 102 hours of Fort Worth Center traffic recordings involving over 37,000 individual flights. The presence of uncertainty had a marginal effect (5%) on minimum-delay conflict resolution performance, and windfavorable routes had no effect on detection and resolution metrics. Flight plan amendments and clearances were substantially reduced compared to today s operations. Top-of-descent prediction errors are the largest cause of failure indicating that better descent predictions are needed to reliably achieve fuel-efficient descent profiles in medium to heavy traffic. Improved conflict detections for climbing flights could enable substantially more continuous climbs to cruise altitude. Unlike today s Conflict Alert, tactical automation must alert when an altitude amendment is entered, but before the aircraft starts the maneuver. In every other failure case tactical automation prevented losses of separation. A real-time prototype trajectory trajectory-automation system is running now and could be made ready for operational testing at an en route Center in 1-2 years.

McNally, David↗
NASA NTRS1983

Conic sectors for sampled-data feedback systems

The conic-sector analysis of the closed-loop stability and robustness of a multivariable-analog-system controller based on sampled-data feedback compensation is investigated. Conic sectors and sampled-data feedback systems are defined, and the existence of a conic sector containing a sampled-data operator is established mathematically. An example is presented to prove that the conic sector is computable and gives sufficient conditions of closed-loop stability. A procedure for determining sampled-data-operator gain is also derived.

Thompson, P. M.↗
NASA NTRS2010

Statistical Analysis of Model Data for Operational Space Launch Weather Support at Kennedy Space Center and Cape Canaveral Air Force Station

The 12-km resolution North American Mesoscale (NAM) model (MesoNAM) is used by the 45th Weather Squadron (45 WS) Launch Weather Officers at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) to support space launch weather operations. The 45 WS tasked the Applied Meteorology Unit to conduct an objective statistics-based analysis of MesoNAM output compared to wind tower mesonet observations and then develop a an operational tool to display the results. The National Centers for Environmental Prediction began running the current version of the MesoNAM in mid-August 2006. The period of record for the dataset was 1 September 2006 - 31 January 2010. The AMU evaluated MesoNAM hourly forecasts from 0 to 84 hours based on model initialization times of 00, 06, 12 and 18 UTC. The MesoNAM forecast winds, temperature and dew point were compared to the observed values of these parameters from the sensors in the KSC/CCAFS wind tower network. The data sets were stratified by model initialization time, month and onshore/offshore flow for each wind tower. Statistics computed included bias (mean difference), standard deviation of the bias, root mean square error (RMSE) and a hypothesis test for bias = O. Twelve wind towers located in close proximity to key launch complexes were used for the statistical analysis with the sensors on the towers positioned at varying heights to include 6 ft, 30 ft, 54 ft, 60 ft, 90 ft, 162 ft, 204 ft and 230 ft depending on the launch vehicle and associated weather launch commit criteria being evaluated. These twelve wind towers support activities for the Space Shuttle (launch and landing), Delta IV, Atlas V and Falcon 9 launch vehicles. For all twelve towers, the results indicate a diurnal signal in the bias of temperature (T) and weaker but discernable diurnal signal in the bias of dewpoint temperature (T(sub d)) in the MesoNAM forecasts. Also, the standard deviation of the bias and RMSE of T, T(sub d), wind speed and wind direction indicated the model error increased with the forecast period all four parameters. The hypothesis testing uses statistics to determine the probability that a given hypothesis is true. The goal of using the hypothesis test was to determine if the model bias of any of the parameters assessed throughout the model forecast period was statistically zero. For th is dataset, if this test produced a value >= -1 .96 or <= 1.96 for a data point, then the bias at that point was effectively zero and the model forecast for that point was considered to have no error. A graphical user interface (GUI) was developed so the 45 WS would have an operational tool at their disposal that would be easy to navigate among the multiple stratifications of information to include tower locations, month, model initialization times, sensor heights and onshore/offshore flow. The AMU developed the GUI using HyperText Markup Language (HTML) so the tool could be used in most popular web browsers with computers running different operating systems such as Microsoft Windows and Linux.

Bauman, William H., III↗
NASA NTRS2002

Managing Risk for Cassini During Mission Operations and Data Analysis (MOandDA)

A Risk Management Process has been tailored for Cassini that not only satisfies the requirements of NASA and JPL, but also allows the Program to proactively identify and assess risks that threaten mission objectives. Cassini Risk Management is a team effort that involves both management and engineering staff. The process is managed and facilitated by the Mission Assurance Manager (MAM), but requires regular interactions with Program Staff and team members to instill the risk management philosophy into the day to day mission operations. While Risk Management is well defined for projects in the development phase, it is a relatively new concept for Mission Operations. The Cassini team has embraced this process and has begun using it in an effective, proactive manner, to ensure mission success. It is hoped that the Cassini Risk Management Process will form the basis by which risk management is conducted during MO&DA on future projects. proactive in identifying, assessing and mitigating risks before they become problems. Cost ehtiveness is achieved by: Comprehensively identifying risks Rapidly assessing which risks require the expenditure of pruject cewums Taking early actions to mitigate these risks Iterating the process frequently, to be responsive to the dynamic internal and external environments The Cassini Program has successfully implemented a Risk Management Process for mission operations, The initial SRL has been developed and input into he online tool. The Risk Management webbased system has been rolled out for use by the flight team and risk owners we working proactive in identifying, assessing and mitigating risks before they become problems. Cost ehtiveness is achieved by: Comprehensively identifying risks Rapidly assessing which risks require the expenditure of pruject cewums Taking early actions to mitigate these risks Iterating the process frequently, to be responsive to the dynamic internal and external environments The Cassini Program has successfully implemented a Risk Management Process for mission operations, The initial SRL has been developed and input into he online tool. The Risk Management webbased system has been rolled out for use by the flight team and risk owners we working put into place will become visible and will be illusmted in future papers.

risk management↗